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Close-up and Whispering: An Understanding of Multimodal and Parasocial Interactions in YouTube ASMR videos

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Abstract and Figures

ASMR (Autonomous Sensory Meridian Response) has grown to immense popularity on YouTube and drawn HCI designers' attention to its effects and applications in design. YouTube ASMR creators incorporate visual elements, sounds, motifs of touching and tasting, and other scenarios in multisensory video interactions to deliver enjoyable and relaxing experiences to their viewers. ASMRtists engage viewers by social, physical, and task attractions. Research has identified the benefits of ASMR in mental wellbeing. However, ASMR remains an understudied phenomenon in the HCI community, constraining designers' ability to incorporate ASMR in video-based designs. This work annotates and analyzes the interaction modalities and parasocial attractions of 2663 videos to identify unique experiences. YouTube comment sections are also analyzed to compare viewers' responses to different ASMR interactions. We find that ASMR videos are experiences of multimodal social connection, relaxing physical intimacy, and sensory-rich activity observation. Design implications are discussed to foster future ASMR-augmented video interactions.
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Close-up and Whispering: An Understanding of Multimodal and
Parasocial Interactions in YouTube ASMR videos
Shuo Niu
Clark University
Worcester, MA, USA
Hugh S. Manon
Clark University
Worcester, MA, USA
Ava Bartolome
Clark University
Worcester, MA, USA
Nguyen B. Ha
Clark University
Worcester, MA, USA
Keegan Veazey
Clark University
Worcester, MA, USA
ASMR (Autonomous Sensory Meridian Response) has grown to
immense popularity on YouTube and drawn HCI designers’ at-
tention to its eects and applications in design. YouTube ASMR
creators incorporate visual elements, sounds, motifs of touching
and tasting, and other scenarios in multisensory video interac-
tions to deliver enjoyable and relaxing experiences to their viewers.
ASMRtists engage viewers by social, physical, and task attractions.
Research has identied the benets of ASMR in mental wellbeing.
However, ASMR remains an understudied phenomenon in the HCI
community, constraining designers’ ability to incorporate ASMR
in video-based designs. This work annotates and analyzes the in-
teraction modalities and parasocial attractions of 2663 videos to
identify unique experiences. YouTube comment sections are also
analyzed to compare viewers’ responses to dierent ASMR inter-
actions. We nd that ASMR videos are experiences of multimodal
social connection, relaxing physical intimacy, and sensory-rich
activity observation. Design implications are discussed to foster
future ASMR-augmented video interactions.
Human-centered computing Empirical studies in collab-
orative and social computing.
ASMR; YouTube; video; multimodal; parasocial; experience
ACM Reference Format:
Shuo Niu, Hugh S. Manon, Ava Bartolome, Nguyen B. Ha, and Keegan
Veazey. 2022. Close-up and Whispering: An Understanding of Multimodal
and Parasocial Interactions in YouTube ASMR videos. In CHI Conference on
Human Factors in Computing Systems (CHI ’22), April 29-May 5, 2022, New
Orleans, LA, USA. ACM, New York, NY, USA, 18 pages.
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Autonomous Sensory Meridian Response (ASMR) is a neurologi-
cal phenomenon usually experienced as tingling sensations in the
crown of the head in response to a range of audio-visual triggers
such as whispering, tapping, and hand movements [
videos incorporate audio, touch, taste, observation, and roleplay ef-
fects to deliver enjoyable and relaxing feelings. Over the past decade,
the creation culture on YouTube has attracted numerous ASMR
creators (known colloquially by users as “ASMRtists”) to design
a wide array of tingle-inducing sounds and actions to intention-
ally induce ASMR feelings [
]. ASMRtists have also leveraged
ASMR videos to connect to the viewers and build online ASMR
communities [
]. A typical YouTube ASMR video may feature an
ASMRtist whispering to the viewer, roleplaying personal attention
such as massages or haircuts, making crisp sounds, or engaging in
various slow and repetitive movements [5]. YouTube hosted more
than 5.2 million ASMR videos in 2016 and 13 million in 2019, and
the searches for ASMR grew over 200% in 2015 and are consis-
tently increasing [
]. Remarkably “ASMR” is among the top
ve YouTube search queries globally and in the US, with a search
volume of more than 14 million1.
In Human-Computer Interaction (HCI), experience-centered de-
sign requires researchers to capture and analyze the experiences
generated from interaction and adopt the understanding of these
experiences in design practices [
]. ASMR is a unique experience
insofar as only some users experience the “tingles” as a response to
particular triggers, and the same trigger may have dierent eects
on dierent people [
]. Over the years, ASMRtists de-
veloped highly stylized and conventionally patterned ASMR videos
to engage their viewers, and as a way to enhance aect and inti-
macy [
]. Prior research on ASMR has focused on characterizing
ASMR triggers [
] or understanding ASMR interactions through
qualitative video analysis [
], user surveys [
], and brain
imaging [
]. Most studies described YouTube ASMR videos primar-
ily as roleplays [
] or as a single video type with a mixture
of ASMR triggers [
]. However, little data-driven research has
been conducted to categorize the wide variety of ASMR experiences
developed by YouTube ASMRtists. YouTubers create ASMR videos
with or without elements of social interaction, using roleplays or
simply manipulating objects, and position themselves up-close or
distant from the viewer. A macro understanding of the delivery
arXiv:2202.06839v1 [cs.HC] 14 Feb 2022
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
mechanism and experience patterns in YouTube ASMR videos will
help technology and service designers explore ways to integrate
ASMR and assess its eects on the user experience. Since experi-
ence seekers need dierent triggers to acquire ASMR sensations,
a quantitative overview of common ASMR interaction modalities
will indicate what ASMR interactions may work for more users.
In this study, we collect a large number of ASMR videos and
perform quantitative analysis to obtain an overview of ASMR in-
teractions and experiences. This work analyzes 2663 ASMR videos
collected from YouTube to examine the multimodal interactions
and the ways ASMR performers para-socially attract the viewers.
We focus on intentional ASMR videos – videos with “ASMR” la-
bels in which a variety of triggers are purposefully displayed by
the performer – to understand ASMRtists’ common approaches
to trigger ASMR experiences. Prior work identied visual, audio,
touch, taste, and scenario-based ASMR triggers [
]. By
interacting with ASMR videos, viewers are able to experience a
simulation of intimacy with the video performer through “paraso-
cial interactions” [
] – a one-sided intimacy experienced by a
viewer through with a gure on screen. In parasocial relationships,
video performers develop and manage three types of attractiveness
social attraction,physical attraction, and task attraction [
]. We
quantify the manners in which the ASMRtists socialize with the
viewer (social attraction), the camera proximity of the ASMRtists in
the videos (physical attraction), and purposeful activities performed
by the ASMRtists (task attraction). This work addresses three main
research questions:
RQ1: What is the distribution of ASMRtists’ interaction
modalities across dierent YouTube ASMR videos?
RQ2: How do YouTube ASMRtists design parasocial attrac-
tiveness through multimodal interactions?
RQ3: How do dierent multimodal interactions and paraso-
cial attractions aect the expression of viewers’ feelings in
the comments?
Figure 1: The structure of the research questions
Figure 1 illustrates the structure of the research questions. RQ1
provides an overview of multimodal interactions in YouTube ASMR
videos to inform designs with common ASMR performing meth-
ods. RQ2 focuses on understanding the patterns of parasocial at-
tractiveness through multimodal interactions. We summarize the
experiences delivered by YouTube ASMR videos and identify the
associated interaction modalities. RQ3 utilizes viewers’ comments
to infer how dierent multimodal interactions and parasocial at-
tractions aect viewers’ social, perceptual, and relaxation feelings.
We rst use grounded-theory approaches to identify subcategories
of interaction modalities and parasocial attractions. Then the code-
book is translated into a questionnaire task. The annotation tasks
were completed by participants recruited from Amazon Mechan-
ical Turk (MTurk). We perform statistical analysis to address the
research questions.
The development of multimodal interactions depends on the
natural integration patterns that typify the combined use of dier-
ent input modes [
]. Understanding diverse interaction modalities
through analyzing extensive video data inform dierent ways to
incorporate ASMR in technology design. Our results indicate social
attractions are enhanced by combining multiple ASMR interaction
modalities. Most ASMRtists use the closeup camera proximity as
a means of building physical attractiveness. ASMRtists emulate
physical closeness through microphonically-amplied whispering,
manipulating objects, virtually “touching” the viewer, and making
mouth noises and microphone-jostling sounds near the camera
or the microphone. Many ASMR videos do not involve purpose-
ful tasks and are not roleplays. Tasks used in non-roleplay videos
include soft and routine activities such as performing medical or
cosmetic treatments, eating and drinking, and demonstrating mun-
dane daily activities. The ASMR experiences delivered by YouTube
ASMRtists can be described as three experience patterns: multi-
modal social connection, relaxing physical intimacy, and observa-
tion of sensory-rich activities. This work aims to inspire future
technologies and services to incorporate ASMR triggers to design
ASMR-augmented relaxing or intimate experiences.
2.1 ASMR Videos on YouTube
The now widely-adopted term “Autonomous Sensory Meridian Re-
sponse” (ASMR) was coined in 2010 to describe a sensory phenome-
non that usually involves the sensation of tingling as a response to
certain audio-visual stimuli [
]. Common ASMR videos show inten-
tional or unintentional gentle interactions such as speaking softly,
playing or brushing hair, moving hands, and tapping or scratching
surfaces [
], which may trigger a low-grade euphoria response
and tingling sensations on the viewer’s head and spine [
]. The
ASMR trend on social media began with a Yahoo group sharing
personal experiences of head tingles when watching specic kinds
of videos [
]. Those original videos were dubbed “unintentional”
ASMR, and involved real-world scenarios such as doctor’s oce
examinations and suit ttings, captured for some non-ASMR pur-
pose and uploaded, but subsequently re-contextualized for their
ASMR tingle-triggering properties [
]. Afterward, creators
made numerous “intentional” ASMR videos on YouTube in which
ASMRtists purposefully use visual and sound stimuli and scripted
roleplays to induce ASMR experiences [
]. In 2019, there were
13 million ASMR videos on YouTube [
]. Popular ASMRtists such
as GentleWhispering ASMR, SAS-ASMR, and Gibi ASMR, have mil-
lions of subscribers, and their videos attracted millions of views
and generate considerable revenue for the creators [66, 72].
Despite the popularity of this emerging video genre on YouTube,
studies found ASMR triggers do not work for everyone, and some
individuals only experience the tingles with very precise, idiosyn-
cratic triggers [
]. ASMR was found to be associated with spe-
cic personality traits of individual viewers and to vary from per-
son to person [
]. Users’ diverse needs triggering eects drive
ASMR consumers to constantly search for videos with the keyword
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
“ASMR.” [
] In turn, the YouTube culture of creativity and participa-
tion [
] encourages ASMRtists to make numerous ASMR videos
to satisfy ASMR experience-seekers’ diverse needs. The growing
trend of ASMR creation and consumption drew researchers’ atten-
tion. Prior studies focused on understanding the sensational eects
through interviewing ASMR viewers (e.g., [
]) or scanning
brain images (e.g., [
]). Some studies examined the creator-viewer
interactions and the digital intimacy through qualitative analyses
of a few viral videos (e.g., [40, 62, 72]).
Although the diversity of ASMR triggers is widely noted, there
is little analysis of large video data sets to explain the creation prac-
tices employed by ASMRtists. ASMR can be induced with virtual
face-to-face interactions [
] or simply by manipulating objects
without showing performers’ faces [
]. Some ASMR videos consist
of constant soft speaking while others involve object manipulation
without talking [
]. Some ASMR videos pretend to touch the view-
ers in roleplays while others perform massages on a second person
who is also visible in the video [
]. ASMR sensations can emerge
both in response to food consumption videos [
] and to videos
showing a person studying quietly [
]. A quantitative analysis of
extensive videos will help HCI designers discern the signicance of
dierent ASMR interactions and experiences. First, since an ASMR
trigger may or may not induce ASMR experiences, an overview of
common ASMR interaction modalities and experiential patterns
will help technology designers to incorporate ASMR triggers that
are eective for a broader range of users. Second, recent research
noted ASMR is not just a sensory experience; it is also a kind of
mediated intimacy oered by ASMRtists to deliver a sense of social
connection [
]. However, there is limited understanding of
how such social experiences are commonly constructed and their
relationships with trigger interactions and social settings. Last,
understanding viewers’ social, perceptual, and relaxation feelings
will help technology designers understand the possible eects of
dierent ASMR experiences.
2.2 Multimodal Interactions in ASMR videos
Researchers have examined various triggers in ASMR videos to
understand this emerging media form and its physiological eects.
Richard summarizes ASMR stimuli as audio, touch, visual, and sce-
nario triggers [
]. Smith et al. examined people’s responses to
ve trigger types, including watching, touching, repetitive sounds,
simulations, and mouth sounds [
]. Zappavigna explored ASMR
roleplays and found linguistic, visual, and aural resources are used
to create a sense of co-presence with the performer [
]. This study
explores common interactions performed by YouTube ASMRtists.
HCI researchers have identied vision, hearing, touch, smell, and
taste as ve sensing modalities to embody interactions [
Grounded in multimodal interaction theories [
] and trigger
modalities identied in the literature [
], RQ1 explores visual,
sound,touch,taste, and scenario as ve interaction modalities in
YouTube ASMR videos. Visual interactions describe how the per-
formers present themselves and the trigger objects. We look into
visual settings such as ASMRtists showing themselves in front of
the camera, performing slow activities, or simply showing hands
manipulating ASMR trigger objects. Sound interactions refer to what
types of hearing triggers are made by the ASMRtists. This modality
seeks to capture sounds like human speaking, tapping or scratching
objects, or various sound eects produced by interacting with the
microphone. Touch interactions examine how ASMRtists stimulate
haptic feelings. For this modality, we observe how ASMRtists use
their hands to interact with themselves, physical objects, the cam-
era, or another person in the video. Taste interactions investigate
whether or not the ASMRtists eat food in the video. And nally,
scenario triggers describe the simulated situation and environment
in roleplay videos, such as haircuts, eye exams, or other dramatized
forms of assistance from the gure on screen [56].
2.3 Parasocial Attractions in ASMR Videos
A large community of YouTube creators designate themselves as
“ASMRtists” by regularly creating and uploading ASMR videos [
The pseudo-interactive nature of the videos engenders a sort of
intimacy with the video creator [
]. The essentially one-
sided intimacy with the video performers, generated by a “conver-
sational give-and-take,is dened as a parasocial relationship, and
the interactions users have with videos that generate parasocial
relationships are called “parasocial interactions. [
] Parasocial
relationships and interactions have been widely found in the interac-
tive reponses of viewers to a TV or social media gure, which aect
viewers socially and emotionally. Video-watching may lead some
viewers to imagine themselves interacting with the performer [
Studies found parasocial relationships can provide social support
and shield against the eects of exclusion and loneliness [
YouTube users mostly focus their investments (of time, of energy) in
parasocial interactions with the video creator, rather than building
a friend and community network like other social media [
ASMR videos can be seen as a unique form of parasocial inter-
actions oered by ASMRtists [
]. Klausen described ASMR as
a “para-haptic interactional” relation with the ASMRtists while
obtaining a form of presence and intimacy [
]. Zappavigna also
considered ASMR videos as a construction of the interactive context
in which viewers feel co-presence with the performer [
]. Smith
argued that in ASMR videos, aective experiences are intentionally
construed and strategically heightened [
]. However, due to a
dearth of quantitative analysis of parasocial interactions in ASMR
videos, it is unknown how prevalent the social experience is, or
what general approaches are best used to deliver such one-sided
This work analyzes the associations between parasocial interac-
tions and interaction modalities to explore the patterns of social and
intimate experiences designed by ASMRtists. The parasocial interac-
tion theory suggests that video performers develop social,physical,
and task attractions to engage viewers and establish parasocial rela-
tionships [
]. Social attraction refers to the degree to which one
feels they would like to befriend the television or media persona
]. In ASMR, performers may simulate conversational scenarios
and socialize with the viewers through their soft vocal and bodily in-
teractions [
]. Creators use ASMR videos to attract patrons and
build network of fans [
]. Physical attraction refers to how video
performers appeal to the viewer physically [
]. In this study, we
measure camera proximity as a vector of physical attraction since
it is dicult to quantify the attractiveness of performers’ physical
appearance. ASMRtists tend to perform body and hand movements
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
very near the camera [
]. ASMRtists may themselves appear
in the video either in close up to show intimacy [
], or they
may exclusively show trigger objects on screen without showing
their faces [
]. Task attraction describes how ably, credibly, or
reliably a performer can complete a task [
]. ASMRtists perform
dierent tasks such as professional treatments [
], mundane
activities like putting on makeups or sorting cards [
], or
less meaningful activities like cutting soap or tapping their nger-
nails on objects [
]. RQ2 uses the parasocial attraction framework
identied in [
] to examine the patterns of social, physical, or
task-observing experiences.
2.4 ASMR Experiences and Benets
ASMR experiences are touted by many as promoting calm and re-
laxed feelings [
] and are associated with positive aect
and a sense of interpersonal connection [
]. Barratt and
Davis found that ASMR combines positive feelings, relaxation, and
tingling sensation of the skin and provides temporary relief from
depression [
]. Smith et al. analyzed neuroimages during ASMR
tingles and found ASMR was associated with a blending of multiple
resting-state networks [
]. Kovacevich examined comments to
ASMR videos and found positive comments appreciated the calm-
ing or relaxing eects [
]. For social and intimate experiences,
Klausen argued that ASMRtists leverage binaural sounds and hap-
tic interactions to create a form of embodied presence and distant
intimacy with the viewers [
]. Smith and Snider also suggested
ASMR performers intentionally express feelings of intimacy and
aection to the viewers [
]. Recent HCI research explored the use
of ASMR eects in wearable technologies for enchantment and slow
experiences [
]. Studies on food-eating videos (colloquially known
as “Mukbang”) found ASMR a key motivator for video watching
]. YouTube study-with-me videos also use ASMR eects [
RQ3 seeks to obtain an initial understanding of viewers’ re-
sponses to dierent ASMR experiences through comment analysis.
Prior work found that people can have dierent feelings with the
same ASMR trigger [
] and don’t publicly share ASMR ex-
periences with others [
]. Comments represent immediate and
direct user reactions to a video and analyzing comments is a more
straightforward way to capture viewers’ feelings than rating by
external participants [
]. Word analysis of YouTube comments is
a common approach to infer the inuence of videos on viewers
]. In RQ3, we measure how ASMR viewers comment on
three common feelings of ASMR identied in prior research: social
connection and intimacy, sensory perception, and relaxation and
sleepiness. Considering the diculties of manually annotating a
large number of comments and subjectively rating viewers’ feel-
ings, a mixed-method of Linguistic Inquiry and Word Count (LIWC)
software [
] and pointwise mutual information (PMI) [
] is used.
LIWC has been scientically validated to analyze people’s social
and emotional expression on social media [
]. PMI is also a
lexicon-based method to identify topically important keywords
]. Both methods are widely used in prior HCI research to im-
ply psychological processes from social media data (e.g., [
Then we compare the word frequencies in comments of videos with
dierent interaction modalities and parasocial attractions.
We collect recent ASMR videos from active video creators to analyze
ASMR videos on YouTube. The ASMR videos are crawled using
the YouTube Data API
with the search seed “ASMR.” In the rst
step, we search ASMR videos on Jun 11, 2020, and Oct 20, 2020, to
collect a list of videos posted in the prior three months, respective
to each search date. This step lets us identify active channels that
were recently posting ASMR videos. Then the crawler collects all
available videos belonging to those active channels to form a raw
dataset. 227,133 videos are returned from YouTube. For each video,
we also collect up to 300 top-level comments returned from the
. Since comments belonging to a video are analyzed together,
and popular videos may have numerous comments, collecting up to
300 per video ensures all videos have a similar amount of comments.
Titles and tags are processed to lter out non-English videos. We
exclude non-English videos due to diculties in the data tagging
and categorization. Videos without “ASMR” in the titles are removed
since this work focuses on intentional ASMR videos with a clear
ASMR theme and is designed for this experience (an ASMRtist may
post non-ASMR videos). We exclude videos shorter than 5 minutes
26%) due to many of them being previews of full videos
and compilations of short video clips from multiple ASMR videos.
We also only keep videos posted between Jan 1, 2020, and Jun 01,
2020, to ensure the videos reect the latest creation styles and have
enough time to receive comments. We remove videos with fewer
than 50 comments (31.39% of videos) to ensure videos had enough
comments for word analysis. After ltering, 85,734 videos are kept
for data sampling. These videos come from 697 dierent channels.
Then we randomly sample 200 videos for grounded theory analysis.
We sample up to 10 videos per channel for the nal data analysis.
There are many channels with less than ten videos in our dataset –
the eventual sampling results in 2830 videos for data annotation.
The IRB oce at the authors’ institute has reviewed the entire
research process. All videos are publicly available when gathered,
and the researchers did not directly interact with any ASRMtists;
therefore, the IRB oce at the authors’ institute exempted this
research from ethics board review.
4.1 Grounded Analysis
Analysis in prior studies identied triggers from a small video
sample or a few popular ASMR roleplays. Therefore, we choose to
conduct a grounded theory analysis to extract common interaction
modalities and parasocial attraction techniques. Grounded theory
data analysis has been widely used to inductively derive models
of social processes [
]. This work follows open, axial, and
selective coding procedures to generate and verify modality and
attractiveness subcategories. We randomly sample 200 videos from
166 ASMRtists for the grounded analysis [7].
In the open coding phase, two of the authors of this research
watch 50 videos each and take notes on the visual, sound, touch,
YouTube Data API does not specify how the returned comments are selected and
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
taste, and scenario triggers described in [
]. The example mul-
timodal interactions can be found in Figure 2. For parasocial at-
tractiveness, the authors annotate how the ASMRtists simulate
communication with their viewers (social attraction), where the
ASMRtist is situated in proximity to the camera (physical attraction),
and the tasks the ASMRtists perform (task attraction). For axial
coding, the two authors use the anity diagramming approach to
summarize these notes and develop subcategories of interaction
modalities and parasocial attractions (see Table 1 and Table 2 for
the codebook). The categorization of social attraction identies
that ASMRtists may communicate with the viewer in the form of a
one-sided talk, or may chat with the viewers as in a back-and-forth
conversation by pausing and waiting for the viewer to reply. Some
other ASMRtists make ASMR videos without any human voice on
the audio track, instead using gestures and text to communicate. For
physical attraction, we focus on categorizing the proximity with
which ASMRtists positioned themselves in relation to the camera.
We adopt the shot scale concept used in lm and TV
(Figure 3)
to annotate if the ASMRtist positions themselves closeup to the
camera (including extreme closeup, closeup, and medium closeup),
medium distance (including medium shot and medium-full shot),
or showing the whole body from head to toe (full shot). The anno-
tation of the tasks performed by the ASMRtists nds three main
categories of activities with clear goals. Treatment and service tasks
seek to perform actions such as massage or haircut on the viewer.
Some videos perform everyday tasks such as painting, writing, or
applying makeup. Other videos demonstrate eating or drinking a
large quantity of food (called Mukbang videos on YouTube [4]).
In selective coding, two authors annotate the remaining 100
videos using the codebook to validate the subcategories and obtain
the inter-rater agreements between experts. Audio and touch are
annotated as multi-categorical values. Visual, taste, scenario, social,
physical, and task are annotated as single-categorical values. After
annotation, 12 of 100 videos are removed due to unavailability (e.g.,
deleted, private, age-restricted, or non-English). Fleiss Kappa with
Jaccard distance is used to calculate between expert agreement.
For the 88 videos, all multimodal and parasocial categories reach
substantial agreements with kappa scores between 0.62 and 0.88
(Table 3). Social and task have relatively lower agreement due to
the dierences in deciding if the ASMRtist talks to or talks with
the viewer and whether an activity is considered common and
daily. Sound and touch have a lower agreement because they are
multi-choice categories. Then the third author annotate disagreed
answers independently to solve discrepancies and generate 88 ex-
pert annotations. The expert annotations are used to assess the
accuracy of annotations completed on Amazon Mechanical Turk.
4.2 Video Data Annotation
This work uses Amazon Mechanical Turk (MTurk) to annotate the
ASMR videos. Each task consists of two steps. Each participant
is asked to watch each video for three minutes in the beginning,
one minute in the middle, and one minute in the end. Then the
participant is asked to annotate the ve multimodal interactions
and three parasocial attractions by answering multi-choice ques-
tions. Example pictures are provided to explain each visual and
Table 1: Subcategories and denition of the ve multimodal
interactions in ASMR videos
Category Denition
Face-to-face The ASMRtist is face-to-face in front of the camera.
The ASMRtist presents and consumes large quantities of food (Mukbang)
Object only
The ASMRtist interacts with physical objects without showing their faces
Serve people The ASMRtist performs a treatment/service on another person
Images Static image(s) or black screen
Gaming Video shows clip(s) of gaming, with or without the ASMRtist in view
Animals The video has animals as the characters
Sounds made by interacting with a physical or liquid object by tapping,
scratching, pouring, spraying, etc.
Whispering Whispering or talking in a low volume
Sounds made with mouth by eating, drinking, lip smacking, tongue
clicking, kissing, licking, or sucking
Sounds made by touching/brushing/scratching themselves, another per-
son, or a fake/silicon body in the video
Ambient and background sounds emitted from a real or fake environment
Mic Sounds made by interacting with the microphone
The ASMRtist reaches to the viewer with their hands or tools in front of
the camera
Objects The ASMRtist clicks, taps, scratches, squeezes, or rubs physical objects
Own body
The ASMRtist touches their own head, body, clothes by rubbing, scratch-
ing, combing, applying makeup, etc.
Real person
The ASMRtist uses their hands or tools to interact with another real
person in the video
Taste The ASMRtist eats or drinks for more than half of the video
The video is a treatment or service roleplay in which the ASMRtist acts
as a service provider and the viewer acts as a customer/patient (e.g.,
massage, haircut, makeup application, clinical exam, interview, customer
The video is a roleplay in which the ASMRtist acts as a character
in a fantasy, surreal, or otherwise unrealistic scenario (e.g., histori-
cal/anime/comics character)
The video is a roleplay in which the ASMRtist acts as an intimate partner
and directly interacts with the viewer intimately or romantically.
Table 2: Subcategories and denition of the three parasocial
attractiveness in ASMR videos
Category Denition
Talk To The ASMRtist talks to or reads to the viewer
Talk with
The ASMRtist pretends to talk with or chat with the viewer, pretending
the viewer responds to the ASMRtist
Gesture and text
The ASMRtist makes eye contact with the viewer and uses body lan-
guage/closed captions/texts to communicate with the viewer
One of the 3 camera shot scales (Extreme closeup, Closeup, Medium
closeup) in which ASMRtists placing themselves close to the camera
One of the 2 camera shot scales (Medium shot and Medium-full shot)
in which ASMRtists placing themselves in medium distance to the
Fullshot The ASMRtists show full body in the camera
No face Static image(s), black screen, or no human face in the video
Partial face Showing half-face (upper or lower half face)
Treatment and
The ASMRtist performs treatment/service on the viewer or another
person in the video (e.g., massage, makeup application, interview, oce
visit, hypnosis, Reiki, etc.)
Common activity
The ASMRtist engages in common daily activity(s) such as painting,
writing, folding clothes, preparing food, or applying makeup to them-
Eat and drink The ASMRtist eats and/or drinks in the video
Table 3: Agreement scores between experts and between
experts and MTurk participants. Calculated using Fleiss’
Kappa with Jaccard method.
Social Physical Task Visual Sound Touch Taste
Between experts 0.65 0.83 0.62 0.84 0.68 0.64 0.88 0.78
Between experts and MTurk 0.73 0.79 0.72 0.80 0.68 0.67 0.93 0.76
physical subcategory. Example video clips are provided to explain
each sound and touch subcategory. A qualication test is performed
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
Figure 2: Examples of visual, sound, touch, and scenario subcategories
Figure 3: Closeup, medium, and full shot examples in phys-
ical attractions
to pre-screen qualied participants. To be qualied, the MTurkers
must indicate that they have watched at least 10 ASMR videos
before, do not feel ASMR videos disturbing or unsatisfying, and ex-
perience a tingling sensation and relaxation after watching ASMR
videos. To test participants’ ASMR knowledge, a pre-screen ques-
tion asks them to pick two typical ASMR videos from the other four
non-ASMR videos. To ensure annotation quality, we only invite
MTurkers who have completed more than 5000 tasks on MTurk
with an approval rate greater than 97%. A simple math question
and a question asking participants to choose two ASMR videos
from four video descriptions are deployed in each task as attention
tests. MTurkers must answer both attention tests correctly to get
the work accepted. Otherwise, the task is rejected and re-released
to other participants.
Before annotating all the data, we rst test the agreement be-
tween MTurk workers and the expert annotations. MTurkers also
complete the 88 videos that the experts have annotated. The anno-
tations of all subcategories between experts and MTurkers reach
a substantial agreement, with kappa scores ranging from 0.67 to
0.80 (Table 3). At the end, the annotation is completed by 47 MTurk
participants with an average completion time of 8.9 minutes. Each
accepted task is paid at the rate of USD $1.50. MTurkers report
167 videos with problems (99 unavailable, 28 age-restricted, and 40
non-English). After removing the 167 videos, 2663 videos are used
for nal data analysis. These videos have an average of 225,143.69
views (
𝑆𝐷 =
5), 5180.89 likes (
𝑆𝐷 =
74), and 448.17
comments (𝑆𝐷 =778.1).
4.3 Comment Data Processing
The nal 2663 videos have 483,583 comments in total, with an av-
erage of 181.59 (
𝑆𝐷 =
81) comments per video. The comment
analysis uses two dierent text processing methods, LIWC [
and PMI [8], to obtain words related to social connection and inti-
macy, sensory perception, and relaxation and sleepiness. For each
video, we merge all the collected comments into one corpus and use
LIWC to calculate the percentages of words related to social pro-
cesses and sensory perception. Social processes include words like
“mate,” “talk,” and “they” and all non-rst-person-singular personal
pronouns, as well as verbs that suggest human interaction (talk-
ing, sharing) [
]. Sensory perception includes bodily and percep-
tual words. The body category under biological processes contains
words such as “cheek,” “hands,” and “spit.” Perceptual processes
recognize words related to perception, including “look,” “heard,
and “feeling.” We also generate a text document with all comments
combined and obtained the emotional tone and social-, body-, and
perception-word percentages in the entire comment corpus. This
step allows us to examine the overall sentiment in ASMR video
comments and compare ASMR comments with the base rates of
expressive writing, natural speech, and Twitter data [51].
LIWC does not provide words related to intimacy, relaxation, and
sleep processes. Therefore, we use the pointwise mutual informa-
tion (PMI) technique [
] to recognize words and phrases associated
with those processes. Similar approaches have been widely applied
in social media analysis (e.g., [
]). The PMI measures the like-
lihood that two terms occur together in a corpus. The PMIs are
calculated based on the 8,914,289 comments from the ltered 85,734
videos. Comments are pre-processed to remove stop words and
punctuation to retain only meaningful words (including regular
words and emojis). Words are then processed to nd bigrams of
common phrases. The keywords to generate associated word lists
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
are “intimate,” “relax,” and “sleep.” To lter out too-rare and too-
common words, identied associated words must appear in at least
1000 comments and no more than 1/10 of all comments. We choose
the top 3% of the qualied terms with the highest PMIs as the
word lists for each keyword. Table 4 lists example words associated
with each keyword. Then we apply a similar approach as in LIWC
to count the percentages of words from each list in each video’s
Table 4: Example words associated with intimacy, relaxation,
and sleep identied by PMI. The word list is used to count
the percentages of words associated with each feeling.
Keyword Number
of words
Example words with top PMIs
intimate 85
intimate, intimacy, connection, sexual, relationships, sensual, romantic,
personal, desire, events, atmosphere, relationship, emotionally, casual,
decision, approach, scenario, witness, creation, client, private, audience,
sacrice, strangely, destiny, partner, remain, alternate, detail, interact
relax 157
relax, informative, unwind, tense, stressful day, wonderfully, educational,
incredibly, stressful, entertain, stevie, strangely, lavender, atmosphere,
relieved-face, extremely, meds, super, amazingly, movements, peaceful,
manner, drift, stress, visually, surprisingly, serum, ambient, traditional
sleep 155
sleep, have trouble, peacefully, drift, schedule, clinic, nights, meds, pills,
autoplay, 4am, insomnia, alarm, pill, tonight, trouble, brush teeth, aid,
auto play, medication, 1am, night, induce, nightmares, wake, 2am, help,
put, pm, 3am
4.4 Statistical Method
The visual, taste, scenario annotations, and the three parasocial
subcategories are stored as multi-categorical nominal variables.
Sound and touch, the two subcategories with multiple possible
choices, are saved as dummy variables (1 is containing the inter-
action, otherwise 0). For RQ2, Pearson’s Chi-squared test (contin-
gency table) is used to identify signicant associations between
multimodal interaction and parasocial attraction subcategories. For
comment analysis in RQ3, we rst perform regression analysis to
identify multimodal and parasocial subcategories that signicantly
predict each feeling word percentage. For each feeling word, two
least-squared regression (LSR) models are built, with one using mul-
timodal interactions as independent variables and the other using
parasocial attraction variables. The two models are multivariate
regressions with all modality factors (or all attraction factors) serv-
ing as independent variables simultaneously. We perform posthoc
analysis with the Steel-Dwass method for each signicant factor
to identify dierences between subcategory pairs. Nonparamet-
ric comparisons are performed due to the word frequencies being
not normally distributed. In all statistical testings, the signicant
threshold (𝑎𝑙𝑝ℎ𝑎 =0.05) is adjusted with the Bonferroni method.
5.1 RQ1: Multimodal Interactions in ASMR
RQ1 seeks to overview the interaction modalities used in ASMR
videos and suggest how prevalent triggers are (see Figure 4 left).
. Face-to-face is the most common visual setting. Around
2/3 of videos present ASMRtists themselves in front of the cam-
era. The ASMRtists also perform triggers in other modalities like
making whispering and object sounds, manipulating objects, or
reaching to the camera at the same time (see Figure 4 right). Muk-
bang, object only, and images are also common visual settings. Only
a small proportion of ASMR videos use video games, animals, or
other visual interactions.
. We notice ASMRtists tend to mix
multiple sound eects in ASMR videos. Only 896 (33.65%) videos
contain only one type of sound. 1081 (40.59%) of videos use two
audio triggers. 687 (25.80%) videos use three or more dierent types
of sound eects. The most common sound in ASMR videos is whis-
pering and soft speaking. Other common sound eects include
object sounds, mouth eects, body and cloth sounds, mic eects,
and ambient sounds.
. More than half (
of ASMR videos use at least one touch trigger in the video. The
most common touch interaction is touching objects in the video to
generate tingling sounds. 29.1% of videos have ASMRtists reaching
toward the camera and pretended to touch the viewers’ face or
body. Less than 10% of videos contain touching ASMRtists’ own
body parts or a person in the video.
. Tasting is not a com-
monly used interaction modality in ASMR videos. Only 12.35% of
videos use tasting triggers, mostly in Mukbang videos (Figure 4
. 71.65% of videos do not use any roleplays in the
videos. The most common roleplay is in service scenarios in which
ASMRtists perform services or treatment processes. Less than 10%
of videos are fantasy or romance roleplays.
The analysis of the distribution of multimodal interactions in
ASMR videos suggests most ASMRtists choose to perform face-to-
face in front of the viewer as the visual interaction. ASMR videos
are sound-diverse and rich. The most common sound interactions
are whispering to the viewers and sounds made by manipulating
trigger objects. Around 1/3 of ASMR videos use touch interaction by
touching objects and/or touching the viewers. Taste interaction is
used to perform Mukbang videos. The majority of the ASMR videos
do not have roleplays and plotted scenarios. The most common
ASMR scenario is service or treatment roleplays.
5.2 RQ2: Parasocial Attractions and ASMR
Experience Patterns
RQ2 examines parasocial attractions in ASMR videos and their as-
sociated interaction modalities to derive ASMR experiences. Figure
5 left illustrates their distribution, and Figure 5 right shows all the
positive and negative associations.
5.2.1 ASMR for Social Experiences. The analysis of social attraction
shows that 1843 (69.21%) of the videos have the ASMRtists talking
to or talking with the viewer (Figure 5 left). Pearson’s chi-squared
test suggests the face-to-face presentation (
𝑣𝑖𝑠𝑢 𝑎𝑙. 𝑓 𝑎𝑐𝑒 _𝑡𝑜_𝑓 𝑎𝑐𝑒
whispering sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑤ℎ𝑖𝑠𝑝𝑒𝑟𝑖𝑛𝑔
), and virtually touching the
viewer through camera-reaching (
𝑡𝑜𝑢𝑐ℎ.𝑣𝑖𝑒𝑤 𝑒𝑟
) are signicantly
associated with talk-to or and talk-with videos (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑡𝑎𝑙 𝑘_𝑡𝑜
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑡𝑎𝑙 𝑘_𝑤𝑖𝑡
). Videos that talk to the viewers are also signif-
icantly associated with touching objects (
𝑡𝑜𝑢𝑐 ℎ.𝑜𝑏 𝑗𝑒 𝑐𝑡𝑠
). Talking
with the viewers is also associated with all three types of roleplays
(Figure 5 right). Among all 1843 talk-to and talk-with videos, 1366
have the ASMRtists interacting with the viewers face-to-face and
making whispering sounds (e.g., Figure 6-a). 645 videos also pretend
to touch the viewers through camera reaching (e.g., Figure 6-b). 504
videos talk to the viewers while the ASMRtists touch objects to
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
Figure 4: Left: The distribution of videos in the subcategories of the ve interaction modalities. Right: The co-appearance rates
of visual triggers and triggers of other modalities, calculated using the Jaccard index.
Figure 5: Left: the distribution of videos in subcategories of parasocial attractions. Right: The signicant associations between
subcategories of multimodal interactions and parasocial attractions. Orange squares are signicant positive associations. Grey
squares are signicant negative associations.
make tingling sounds (e.g., Figure 6-c). Talk-with videos use scenar-
ios in which the ASMRtist roleplays a service provider (
Figure 6-d), a fantasy character (
108, Figure 6-e), or an intimate
partner (
81, Figure 6-f). Although most talk-with videos show
the performer looking at the viewer face-to-face, 116 out of 639
talk-with videos are videos with static or no images (
e.g., Figure 6-f). Non-socializing videos are associated with Muk-
bang (
), object-only (
𝑣𝑖𝑠𝑢 𝑎𝑙.𝑜𝑏 𝑗 𝑒𝑐𝑡 _𝑜𝑛𝑙𝑦
), serving
other people (
𝑣𝑖𝑠𝑢𝑎𝑙.𝑠𝑒𝑟 𝑣𝑒_𝑝𝑒𝑜𝑝𝑙𝑒
), touching another person in
the video (
𝑡𝑜𝑢𝑐ℎ.𝑟 𝑒𝑎𝑙 _𝑝𝑒𝑟𝑠𝑜𝑛
), tasting (
), and non-roleplays
(𝑠𝑐𝑒𝑛𝑎𝑟 𝑖𝑜 .𝑛𝑜𝑛𝑒 ).
The high percentage of videos with conversational content sug-
gests that social connection is a common experience incorporated
by ASMRtists. ASMR performers deliver social connection experi-
ences with multimodal interactions such as face-to-face whispering,
hand-reaching, and one-sided or back-and-forth conversation. The
ASMRtists tend to touch objects to generate tingling sounds while
talking to the viewers. ASMRtists also perform service, fantasy, and
romantic roleplays to engage the viewer in an emulated conver-
sation, in which many ASMRtists pretend that they can hear the
viewer’s responses. Videos without socialization are videos of food
eating, serving another person, or merely manipulating objects.
5.2.2 ASMR for Intimate Interaction. The result of physical at-
traction suggests that the majority (1868, 70.15%) of the ASMR
videos have the ASMRtists presenting their full faces in closeup,
medium, and full shot camera distances (Figure 5 left). Only 550
(20.65%) videos do not have human appearances, and 245 (9.2%)
videos show partial faces. Closeup is the most used shot scale
used in ASMR videos, suggesting most ASMRtists seek to sim-
ulate nearness with the viewer by positioning themselves in close
camera proximity. The association analysis suggests that face-to-
face (
𝑣𝑖𝑠𝑢 𝑎𝑙. 𝑓 𝑎𝑐𝑒 _𝑡𝑜_𝑓 𝑎𝑐𝑒
), whispering sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑤ℎ𝑖𝑠𝑝𝑒𝑟𝑖𝑛𝑔
object sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑜𝑏 𝑗𝑒𝑐 𝑡
), mouth sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑚𝑜𝑢𝑡
), mic ef-
fects (
𝑠𝑜𝑢𝑛𝑑 .𝑚𝑖𝑐
), and the touching of objects (
𝑡𝑜𝑢𝑐 ℎ.𝑜𝑏 𝑗𝑒 𝑐𝑡𝑠
) and
viewers (
𝑡𝑜𝑢𝑐ℎ.𝑣𝑖𝑒𝑤 𝑒𝑟
) signicantly associate with videos using
closeup camera proximity (
, Figure 5 right). In all
1653 closeup videos, 1490 videos whispers to the viewer near the
camera (e.g., Figure 7-a). 809 videos have ASMRtists making various
trigger sounds (e.g., Figure 7-b). 591 and 245 closeup videos contain
mouth eects (e.g., Figure 7-c) and mic eects (e.g., Figure 7-d).
For touch interactions, closeup videos also consist of manipulating
objects (e.g., Figure 7-b) and pretending to touch the viewers (e.g.,
Figure 7-e). In these videos, ASMRtists make mouth sounds near
the mic or interact with the mic to engender the sound of physical
closeness. Although some videos do not show the ASMRtist on
screen, 59 of them simulate intimate and romantic roleplays in their
conversations with the viewers (Figure 7-f).
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
(a) The ASMRtist softly whispers
to the viewer.
(b) The ASMRtist talks with
viewer and pretends to reach
viewer’s face in a treatment.
(c) The ASMRtist manipulate a
bottle near the mic while intro-
ducing it in whispers.
(d) The ASMRtist performs a con-
versation during a nerve exam on
the viewer in a treatment role-
(e) The ASMRtist pretends to re-
cruit the viewer into their punk
band in a fantasy roleplay.
(f) The ASMRtist talks with the
viewer as the Goddess of Love in
a romantic, image-only roleplay.
Figure 6: Example ASMR videos showing ASMR interactions
for social attraction.
These results suggest that ASMRtists seek to use cameras and
microphones to emulate intimate interactions with the viewers.
Common approaches include positioning near the camera, near-mic
whispering, manipulating objects, reaching hands to the camera,
and making mouth and mic sounds. Even in videos without the
performer’s physical presence, ASMRtists can emulate intimate
roleplays and conversations to express intimacy.
5.2.3 ASMR for Activity Observation. The analysis of task attrac-
tion shows most of the videos do not contain a clear task. More than
half of the videos (
17%) are
. The associations
suggest three main types of videos that do not have specic tasks.
The rst type is ASMRtists looking at the viewer face-to-face (
1085) and chatting with the viewer and/or making random sounds
(e.g., Figure 8-a). The second main type is chatting in fantasy or
romance roleplays (
210, e.g., Figure 6-f and 7-f). The third type
is object-only videos (
191), in which ASMRtists manipulate
trigger objects without meaningful purposes (e.g., Figure 8-b and c).
More than 40% of videos contain treatment, eating and drinking, and
common daily tasks. Among 578 treatment videos, ASMRtists whis-
per to the viewer (
507), make object (
317) and body/cloth
sounds (
164), and emulate service scenarios (
381). Treat-
ment videos (
) are signicantly associated with face-
to-face (
𝑣𝑖𝑠𝑢 𝑎𝑙. 𝑓 𝑎𝑐𝑒 _𝑡𝑜_𝑓 𝑎𝑐𝑒
456) and touching the viewer
(a) An ASMRtist whispers near
the camera/mic to simulate close-
ear speaking.
(b) The ASMRtist manipulates
pliers near the camera to gener-
ate tingling sound.
(c) The ASMRtist smacks lips
near the mic to emulate close-ear
(d) The ASMRtist scratches the
mic and whispers trigger words
(coconut, pickle, etc.).
(e) The ASMRtist pretends to do
makeup on viewer’s face while
the viewer is asleep.
(f) An audio-only ASMR, where
ASMRtist emulates cleaning
viewer’s ears as a sister.
Figure 7: Example ASMR videos showing ASMR interactions
for intimacy.
𝑡𝑜𝑢𝑐ℎ.𝑣𝑖𝑒𝑤 𝑒𝑟
304), as those treatment videos pretend to per-
form the service on the viewer (e.g., Figure 6-b and d and Figure 7-e).
Treatment videos (
) are also signicantly associated
with performing service on another person (
𝑣𝑖𝑠𝑢𝑎𝑙.𝑠𝑒𝑟 𝑣𝑒_𝑝𝑒𝑜𝑝𝑙𝑒
85) and touching them (
𝑡𝑜𝑢𝑐ℎ.𝑟 𝑒𝑎𝑙 _𝑝𝑒𝑟𝑠𝑜𝑛
83, e.g., Fig-
ure 8-d). 337 videos consist of tasks of eating and drinking (e.g.,
Figure 8-e and f), most of which present a large quantity of food in
the video (
259) and make mouth sounds
𝑠𝑜𝑢𝑛𝑑 .𝑚𝑜𝑢𝑡
301). Mukbang task is signicantly associ-
ated with non-social (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑛𝑜𝑛𝑒
) and partial face presentations
𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙.𝑝𝑎𝑟 𝑡𝑖𝑎𝑙_𝑓 𝑎𝑐𝑒
). The third major task is common daily ac-
tivities (e.g., Figure 8-g and h). 216 (8.11%) videos show daily tasks
in which ASMRtists perform everyday activities. Common daily
tasks (
𝑇𝑎𝑠𝑘 .𝑐𝑜𝑚𝑚𝑜𝑛
) are signicantly associated with object-only
presentations (
𝑣𝑖𝑠𝑢 𝑎𝑙.𝑜𝑏 𝑗 𝑒𝑐𝑡 _𝑜𝑛𝑙𝑦
43), whispering sounds
𝑠𝑜𝑢𝑛𝑑 .𝑤ℎ𝑖𝑠𝑝𝑒𝑟𝑖𝑛𝑔
184), and object sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑜𝑏 𝑗𝑒𝑐 𝑡
122). Common daily tasks (
) are also signicantly
associated with touching own body (
𝑡𝑜𝑢𝑐ℎ.𝑜𝑤 𝑛_𝑏𝑜𝑑𝑦
38) be-
cause these videos contain activities such as applying makeup (e.g.,
Figure 8-h).
These results indicate that ASMRtists tend to present activities
without a clear, purposeful task. The taskless videos include mun-
dane, repetitive, and unintentional actions, facing the viewers or
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
(a) The ASMRtist scratches the
mic with a brush and makes ran-
dom mouth sound.
(b) An ASMRtist constantly typ-
ing on a keyboard for 1.6 hours
without speaking.
(c) An ASMRtist collapses soaps
and starch without showing
themselves or speaking.
(d) The ASMRtist performs head
massage on another person in
the video.
(e) The ASMRtist consumes a
large quantity of food.
(f) The ASMRtist eats cakes and
jelly and explains with captions.
(g) The ASMRtist shows magic
tricks with cards.
(h) The ASMRtist put makeup on
face while chewing gum.
Figure 8: Example ASMR videos showing no-task and with-
task ASMR interactions.
only showing the trigger objects. Videos with particular tasks in-
volve treating the viewer or another person, eating a large quantity
of food, or other common everyday activities. Those activities are
also considered soft, clicking, slow, or repetitive, which are likely
to induce ASMR experiences [5].
5.3 RQ3: Viewers’ Comments about the
Feelings of ASMR Experience
RQ3 explores viewers’ feelings about dierent ASMR interactions
by calculating the percentages of the LIWC and PMI identied key-
words. We rst compare the linguistic attributes of all ASMR video
comments with the base rates of expressive writing, natural speech,
and Twitter data [
]. Figure 9 shows the results. The emotion tone
score of all ASMR comments is 99 (50 is neutral), higher than the
other three types of textual data, indicating viewers’ overall positive
reaction to the ASMR videos. ASMR comments have comparable
social word frequencies, suggesting viewers have similar social
expression as in other texts. The body words (7.68%) and perception
words (7.63%) in ASMR comments are higher than the other three
texts. This shows that viewers write more in the comments about
things associated with body and perception processes. The overall
positive emotion and high frequency of body and perception words
imply that viewers obtained sensational pleasure from watching
the ASMR videos (see example comments in Appendix A).
Figure 9: Comparison of emotion tone and percentages of so-
cial, body, and perception words between ASMR videos and
expressive writing, natural speech, and Twitter [51]
5.3.1 Social and Intimacy. The LSR model that predicts social word
frequencies by the parasocial attraction subcategories suggests
that social and physical are signicant predictors (Table 5). The
model that predicts social word frequencies by interaction modali-
ties shows that visual and sound are signicant predictors. The
posthoc analysis shows that ASMR videos that leverage social
attraction techniques lead to higher use of social words in the
comments (Figure 10 top). The social word frequencies in talk-
to and talk-with (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑡𝑎𝑙 𝑘_𝑡𝑜
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑡𝑎𝑙 𝑘_𝑤𝑖𝑡
) videos are
signicantly higher than gesture/text videos (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑔𝑒𝑠𝑡𝑢𝑟𝑒
and non-social videos (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑛𝑜𝑛𝑒
). Similarly, videos with ASM-
Rtists whispering sounds have more social comments than videos
without communication. For physical attraction, videos with the
ASMRtists being closeup (
), and medium distance
) have higher social word frequencies than videos
without ASMRtists’ appearances. With regard to visual modalities,
videos that use visual settings of static images (mostly audio-only
roleplays), face-to-face interaction, and serving people have higher
social word frequencies than Mukbang and object manipulation
videos. These results indicate that the social attraction techniques
used in ASMR videos, such as talking to/with the viewers, showing
themselves face-to-face, and whispering led viewers to express more
social processes in the comments than non-social ASMR videos.
The LSR models which predict the intimate word frequency
show that social, physical, and task, are signicant parasocial pre-
dictors (Table 5). Visual, sound, and scenario are the signicant
multimodal interaction predictors. The posthoc analysis shows
that talking with or to viewers leads to more comments related to
intimacy. Figure 10 bottom shows the results. Videos with whis-
pering sounds also have signicantly more intimate words in the
comments than videos without whispering. Mukbang videos have
signicantly lower intimate words in comments. The comparison
of intimate words shows that comments to roleplay videos have
more viewers’ intimate expressions than videos without roleplays.
Since many fantasy and romance roleplays are voice-only, videos
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
Table 5: Results of multivariate LSR models that predict the percentages of feeling words in comments. Only the p-values of
signicant predictors are presented. Adjusted 𝛼=
. *Multi-choice factors, 𝑝is from the dummy variable with the smallest
Dependent variable LSR of Parasocial Attractions LSR of Multimodal Interactions
𝐹 𝑝 𝑟2𝑝𝑠𝑜𝑐𝑖𝑎𝑙 𝑝𝑝 ℎ𝑦𝑠𝑖 𝑐𝑎𝑙 𝑝𝑡𝑎𝑠𝑘 𝐹 𝑝 𝑟 2𝑝𝑣𝑖 𝑠𝑢𝑎𝑙 𝑝𝑠𝑜𝑢𝑛𝑑 *𝑝𝑡 𝑜𝑢𝑐ℎ *𝑝𝑡𝑎𝑠 𝑡𝑒 𝑝𝑠𝑐 𝑒𝑛𝑎𝑟𝑖 𝑜
Social words 32.35 <0.0001 0.11 <0.0001 <0.0001 - 18.44 <0.0001 0.13 <0.0001 <0.0001 - - -
Intimacy words 24.03 <0.0001 0.08 <0.0001 0.0012 <0.0001 16.92 <0.0001 0.12 0.0016 <0.0001 - - 0.0001
Body words 49.36 <0.0001 0.16 <0.0001 <0.0001 <0.0001 39.03 <0.0001 0.25 <0.0001 <0.0001 0.0002 0.0060 0.0003
Perception words 29.55 <0.0001 0.10 <0.0001 <0.0001 0.0006 27.21 <0.0001 0.18 <0.0001 <0.0001 <0.0001 - <0.0001
Relax words 43.28 <0.0001 0.14 0.0003 <0.0001 <0.0001 30.42 <0.0001 0.20 <0.0001 <0.0001 <0.0001 0.00035 <0.0001
Sleep words 36.73 <0.0001 0.12 0.0065 0.0004 <0.0001 23.03 <0.0001 0.16 - <0.0001 <0.0001 <0.0001 <0.0001
Figure 10: Social and intimate word frequencies between subcategories of signicant multimodal and parasocial predictors.
Ordered by the average percentage in descending order. Horizontal bars show signicant dierences (𝑝* < 0.05, 𝑝** < 0.01, 𝑝***
< 0.001).
without performers’ faces (
𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙.𝑛𝑜_𝑓 𝑎𝑐𝑒
), with static images
), and with ambient sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑎𝑚𝑏𝑖𝑒𝑛𝑐𝑒
) have
signicantly higher numbers of intimate words in the comments.
The comparison of social and intimate words suggests that the
ASMR videos with social interactions – such as presenting the
ASMRtist in the videos and whispering to the viewers – are more
likely to receive viewers’ social responses than ASMR videos with-
out socialization. Roleplays lead to more intimate reactions in the
comments than non-roleplay videos. In contrast, Mukbang and
object-only videos have lower social and intimate expressions.
5.3.2 Body and Perception. Viewers share their body and percep-
tual feelings or comment on ASMRtists’ body or actions (see Ap-
pendix A). The LSR model suggests social, physical, and task at-
tractions can signicantly predict the use of body and perception
words (Table 5). All multimodal interaction subcategories are signif-
icant predictors of body words. Visual, sound, touch, and scenario
are signicant predictors of perception words. Posthoc shows that
Mukbang videos lead to the highest sensory words in comments
(Figure 11). Eating or drinking videos (
) have sig-
nicantly more sensory responses than other task-oriented and
taskless videos. Comments to videos that contain mouth sounds
have signicantly more body and perception words. Taste interac-
tions lead to signicantly more body words. Since Mukbang videos
tend to show partial faces and only use gestures and text to commu-
nicate (e.g., Figure 8-e and f), the gesture/text (
𝑠𝑜𝑐𝑖𝑎𝑙 .𝑔𝑒𝑠𝑡𝑢𝑟𝑒
and the partial face (
𝑝ℎ𝑦𝑠𝑖𝑐𝑎𝑙.𝑝𝑎𝑟 𝑡𝑖𝑎𝑙_𝑓 𝑎𝑐𝑒
) videos have the highest
sensory word use in the comments. We also noticed that videos
without tasks (
) have higher body words. Non-roleplay
videos (
) have the highest usages of body and percep-
tion words. Object-only videos (
𝑣𝑖𝑠𝑢 𝑎𝑙.𝑜𝑏 𝑗 𝑒𝑐𝑡 _𝑜𝑛𝑙𝑦
, e.g., Figure 8-b
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
and c) also have a higher mentioning of the body and perception
words. Videos with object sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑜𝑏 𝑗𝑒𝑐 𝑡
, e.g., Figure 6-c
and Figure 7-b) and mic sounds (
𝑠𝑜𝑢𝑛𝑑 .𝑚𝑖𝑐
, e.g., Figure 7-d and
8-a) have signicantly more comments with body and perception
words. These results imply that presenting and consuming a large
quantity of food in ASMR videos, as well as videos without tasks
or roleplays are more likely to induce viewers’ feelings related to
sensory perception.
5.3.3 Relaxation and Sleepiness. The LSR model that predicts relax-
ation words shows that all parasocial and multimodal subcategories
are signicant predictors (Table 5). The model that predicts sleep
words suggests that all parasocial and multimodal subcategories
except for visual interaction are signicant predictors. Posthoc
analysis between dierent categories shows that videos related to
treatment and intimate interactions have the highest percentage
of relax and sleep words in comments (Figure 12). Videos with
treatment performance (
𝑠𝑐𝑒𝑛𝑎𝑟𝑖𝑜.𝑠𝑒𝑟 𝑣𝑖𝑐𝑒
) have
the highest percentages for both measurements. Videos that in-
volve performing services on another person (
𝑣𝑖𝑠𝑢𝑎𝑙.𝑠𝑒𝑟 𝑣𝑒_𝑝𝑒𝑜𝑝𝑙𝑒
𝑡𝑜𝑢𝑐ℎ.𝑟 𝑒𝑎𝑙 _𝑝𝑒𝑜𝑝𝑙𝑒
) also lead to more relaxation expression.
Videos that pretend to touch the viewer by reaching toward the
camera (
𝑡𝑜𝑢𝑐ℎ.𝑣𝑖𝑒𝑤 𝑒𝑟
) have signicantly more sleep words than
videos without this interaction. ASMR videos with mic sounds
𝑠𝑜𝑢𝑛𝑑 .𝑚𝑖𝑐
), in which ASMRtists get close up to the camera and
make near-ear mic sound eects (e.g., Figure 7-d and Figure 8-a),
lead viewers to comment more about relaxation and sleepiness.
These results imply that videos showing treatment processes
and physical intimacy generate feelings of relaxation and sleepiness
for viewers more often. Videos with near-ear microphone eects
also incite feelings of relaxation and sleepiness. It should be noted
that although the visual and touch settings of treatment ASMRs
seek to simulate physical intimacy with the viewers, viewers do not
express more intimacy in the comments. Instead, the emulation of
close physical interactions in treatments lets viewers express more
relaxation and sleepiness.
The analysis of multimodal interactions and parasocial attractions
describes the common patterns used to create ASMR experiences.
Our work depicts common ASMR interactions but does not contrast
their eects with other online content. This section summarizes
that the ASMRtists deliver ASMR eects through three experience
patterns: multimodal social connection, relaxing physical intimacy,
and sensory-rich activity observation.
6.1 ASMR as an Experience of Multimodal
Social Connection
Prior research primarily examine ASMR triggers’ characteristics,
and their dierent physiological eects on the viewers [
Although ASMR videos are considered as a new pathway to connect
creators and their viewers [
], there is little knowledge regarding
what specic interactions ASMRtists perform to best establish so-
cial connections. We nd the social experience in YouTube ASMR
videos is commonly oered and multi-modeled. Around 65% of
videos in our data contain the performer looking at the viewer
face-to-face, and 78% of videos involve whispering. 70% of ASMR
videos have ASMRtists communicating verbally to the viewer, with
24% of videos pretending that the performer can hear viewers’ reply
(talk-with videos). 59.07% of videos used at least one type of touch
interaction. The pervasive use of conversational content reveals
that sound eects are not the only drivers of ASMR; ASMRtists
engage viewers and induce ASMR through experiences of one-sided
social connection. These results are consistent with the signicance
of face-to-face interactions noted in prior research [
]. In a multi-
modal conversation, the performer faces the viewer, communicates
in whispers, touches the viewers through camera reaching, touches
and introduces triggers, and emulates imagined scenarios. Since
ASMR needs to be triggered with appropriate stimuli [
], and be-
cause not all triggers “work” for all viewers, the diverse modalities
allow viewers to try out and encounter triggers that can bring ASMR
sensations. The social interactions could also foster the feeling of
co-presence with the ASMR performer [
]. Interaction modalities
such as whispering with/to the viewers and being spatially close up
to the camera lead viewers to write more about the social processes
in the comments. Even in audio-only videos without visual presen-
tations, ASMRtists play fantasy and romantic roles and chat with
the viewers in stories. The analysis of viewer comments suggests
that viewers tend to leave more intimate comments to videos with
those ASMR components.
These ndings imply new pathways to design parasocial experi-
ences with ASMR eects. ASMR interaction techniques can provide
social exposure that increases closeness in asynchronous video
communication. Video-based technologies incorporating ASMR
eects and multimodal ASMR interactions may augment parasocial
connection experiences. Since ASMR is proven to oer positive
aect, as well as intimate and relaxing experiences for viewers
], face-to-face video communications can leverage ASMR in-
teractions to transfer the process of speaking-listening to a richer
experience with tingling sensations. The social experience pattern
captured from ASMRtists’ videos implies that technologies can
incorporate ASMR interactions in multiple modalities such as whis-
pering, camera-reaching, emulated back-and-forth conversation,
and trigger manipulation in order to induce viewers’ ASMR sen-
sations. Users need both time and variety in order to see if tingles
develop, and the multi-modalities allow for that temporal unfolding
and variety. For example, applications such as video conferencing
tools, podcasts, and social audio apps can potentially introduce mul-
timodal ASMR to reduce the exhaustion and fatigue from long-time
use [
]. Voice-based virtual assistants [
] may also include ASMR
eects to reduce the robotic sound.
6.2 ASMR as an Experience of Relaxing
Physical Intimacy
Leveraging attraction and interaction techniques to demonstrate
intimacy is also a typical pattern in ASMRtists’ videos. Prior re-
search has explored ASMR as an experience of digital intimacy
] as well as the ways ASMRtists create roleplay videos to
foster intimate feelings with the viewers [
]. This paper overviews
ASMRtists’ techniques to design intimate experiences and how
these techniques relax viewers and help with sleeping. We nd
that the most common camera shot scales in ASMR videos are
closeups – framing the performer’s face at a near distance while
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
Figure 11: Body and perception word frequencies between subcategories of signicant multimodal and parasocial predictors.
Ordered by the average percentage in descending order. Horizontal bars show signicant dierences (𝑝* < 0.05, 𝑝** < 0.01, 𝑝***
< 0.001).
excluding most of their body. Around 30% of videos have the ASM-
Rtists pretending to touch the viewers through camera reaching.
About 30% of videos also make close-mic mouth sounds, and 12%
manipulate the microphone itself to simulate physical intimacy
through sound interactions. These interactions are commonly per-
formed in service-oriented videos such those involving massage,
haircuts, makeup applications, etc. However, our comment analysis
suggests that viewers do not express more intimacy to videos with
intimate interactions than other videos. On the other hand, videos
with close interactions have more comments regarding relaxation
and sleepiness-related words. Our results imply that although ASM-
Rtists virtually approach the viewers, viewers expressed relaxing
and calming experiences more than intimacy to such videos.
Our ndings suggest new opportunities to design ASMR-based
applications to present intimacy and deliver soothing experiences.
For example, ASMR interactions allow service providers such as
masseurs and Reiki masters to oer virtual treatments through
ASMR videos. This virtual therapy could provide a possible so-
lution when face-to-face service is unavailable, or for users who
cannot aord in-person treatment. People separated from loving re-
lationships [
] or patients living in stressful hospital settings [
need intimate interactions. ASMR eects with close-mic whispers
and near-camera touching could potentially engender a feeling of
intimacy to induce relaxing experiences. Virtual social encounters
with ASMR performers could also provide alternatives for people
with social diculties (e.g., due to autism or social anxiety) to en-
joy safe, calm, regularized social experiences on demand [
]. To
augment such experiences, designers can create new ASMR video
interactions. For example, ASMRtists use the talk-with and camera-
reaching techniques to mimic physical proximity. Novel interaction
techniques such as VR, AR, and other telepresence technologies
can be integrated to augment the social and virtual presence during
ASMR videos. However, we want to remind the HCI community
that some ASMR videos were found to connect to sexuality and
sexual arousal [
]. The design for intimacy needs to be cau-
tious with this potential side eect, especially when children use
ASMR videos.
6.3 ASMR as an Experience of Sensory-rich
Activity Observation
Prior research have studied roleplay ASMRs as a primary type
]. However, our ndings suggest that more than
70% of videos in our data do not have roleplay scenarios. Also, in
contrast to the wide use of social and physical attractions, most
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
Figure 12: Relax and sleep word frequencies between subcategories of signicant multimodal and parasocial predictors. Or-
dered by the average percentage in descending order. Horizontal bars show signicant dierences (𝑝* < 0.05, 𝑝** < 0.01, 𝑝*** <
ASMRtists’ videos do not use task attractions to forge ASMR expe-
riences. Only around 40% of videos in our dataset have identiable
tasks and goals. These numbers indicate that intentional ASMR
videos are not limited to roleplays; future work should include the
diverse non-roleplays and taskless videos when examining ASMR
performance and eects. Videos with tasks include the performance
of various physical treatments, eating a large quantities of food,
and displaying mundane activities such as playing cards or putting
on makeup. Taskless videos can involve casual chatting or object or
mic manipulation without showing the performer. The infrequent
appearance in videos of purposeful tasks implies that ASMR eects
do not require attention or real acts of care to take place. Therefore,
many ASMRtists choose not to demonstrate abilities by complet-
ing tasks or making clear storylines, but instead remain focused
exclusively on the production of triggering eects. The analysis
of viewer comments further reveals that eating/drinking videos
and videos without tasks or roleplays are associated with viewers’
comments about the body and perceptual processes, indicating that
these videos are prone to trigger bodily and perceptual experiences.
Activities of “tasklessness” in ASMR videos don’t particularly
care about addressing viewers, adopting a stance of calculated indif-
ference, and this disinvested attitude may be more likely to cause
ASMR feelings [
]. Therefore, any form of videos that does not
require close attention except for observing peaceful and repeti-
tive activities – videos such as crafting process demonstrations,
instructions for applying makeups or skincare, and tutorials on or-
ganizing everyday objects – may consider employing ASMR eects.
Prior research suggested that ASMR is an ambient sensory eect in
YouTube study-with-me videos [
]. Videos like these may reduce
the human presence and intentionally make tingling sounds in the
background to trigger ASMR feelings. However, videos that include
slow and dull tasks may cause ASMR feelings by accident and could
make viewers lose focus and feel sleepy. In those cases, ASMR
may need to be avoided if the video is geared toward learning and
requires attention. Designers may also consider conveying sensory-
rich experiences through Mukbang ASMR or sound-focused ASMR.
Watching food-eating videos has shown benets to mitigate home-
sickness [
]. People watch Mukbang videos to gain multi-sensory
immersion and “commensality.” [
] ASMR can be a sensory experi-
ence incorporated in human-food interaction [
]. Interaction
designers can generate ASMR experiences by mouth and mic sounds
to augment sensory pleasure. Technologies for sensory reality and
relaxation (e.g., virtual reality for anxiety-related problems [
can incorporate ASMR techniques such as eating/drinking sounds
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
or sound-focused scenes to induce sense of presence and relaxing
This work analyzed the multimodal interactions and parasocial
experiences in 2663 YouTube ASMR videos. We annotated how
ASMRtists use visual, sound, touch, taste, and roleplay triggers
to deliver social, physical, and task attractiveness. We obtained
the distribution of ASMR interaction modalities and parasocial
attractions. The associations between interaction modalities and
parasocial attractions reveal patterns of ASMR experiences. Feeling-
oriented words were recognized from viewer comments in order
to probe whether dierent ASMR interactions lead to dierent
viewer feelings. Face-to-face orientation, whispering sounds, and
touching objects are the most interaction modalities. Social interac-
tions are common and multi-modeled. ASMRtists implement social
and physical attractions, but most ASMR videos do not involve
roleplays or contain purposeful tasks. Our results summarize that
YouTube ASMR videos provide three experiences: multimodal social
connection, relaxing physical intimacy, and sensory-rich activity
observation. These experiential descriptions seek to foster future
media productions on a wide array of platforms that include ASMR
interactions and eects.
Moving forward, we hope this work serves as a seminal study
to inspire more ASMR-augmented designs. There are also many
open-ended questions to be addressed by HCI researchers and prac-
titioners. First, one limitation of this work is that we only consider
intentional ASMR created and shared by YouTube ASMRtists to
induce ASMR experiences specically. Prior studies noticed view-
ers also experience ASMR with videos such as Bob Ross’ The Joy
of Painting and a recording of Lectures on Quantum Field Theory
], which are not made for ASMR but contains ASMR proper-
ties. We did not include unintentional videos without “ASMR” labels
due to diculties recognizing and collecting them from YouTube.
We also consider intentional ASMR interactions to be purposefully
designed and performed; therefore, easier to be adopted in design.
Future research may compare and contrast the eects of the two
ASMR video types. Second, this work does not interview actual
viewers to obtain their in-situ feelings of ASMR interactions; view-
ers’ reactions to dierent ASMR interactions were obtained from
video comments. It is possible that viewers do not externalize all
of their feelings of intimacy or relaxation in comments. However,
we believe this work provides an overview of ASMR interaction
techniques that can guide future studies to examine ASMR-based
intimacy and well-being in various use cases [
]. Future research
needs to assess the actual eects of ASMR interactions of dierent
people and in dierent contexts, especially when ASMR interac-
tions are designed for people with social anxiety or disabilities.
Third, YouTube creators contribute vernacular creativity [
] to
build parasocial relationships. HCI researchers should consider in-
terviewing ASMRtists or involving them in participatory design to
understand their preferences and diculties in managing parasocial
interactions. Last, the growing ASMR communities across dierent
cultures [
] encourages HCI studies to examine how ASMR
videos aect the creator-viewer communications and relationships.
It is valuable to expand ASMR research to non-English videos to
have a cross-cultural understanding of ASMR.
Nitin K Ahuja. 2013. "It feels good to be measured": clinical role-play, Walker
Percy, and the tingles. Perspectives in biology and medicine 56, 3 (2013), 442–451.
Muhammad Abubakar Alhassan and Diane Pennington. 2020. Detecting Critical
Responses from Deliberate Self-Harm Videos on YouTube. In Proceedings of
the 2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20).
Association for Computing Machinery, New York, NY, USA, 383–386. https:
Joceline Andersen. 2014. Now You’ve Got the Shiveries: Aect, Intimacy, and the
ASMR Whisper Community. Television & New Media 16, 8 (11 2014), 683–700.
Laurensia Anjani, Terrance Mok, Anthony Tang, Lora Oehlberg, and Wooi Boon
Goh. 2020. Why Do People Watch Others Eat Food? An Empirical Study on
the Motivations and Practices of Mukbang Viewers. In Proceedings of the 2020
CHI Conference on Human Factors in Computing Systems (CHI ’20). Association
for Computing Machinery, New York, NY, USA, 1–13.
Emma L Barratt and Nick J Davis. 2015. Autonomous Sensory Meridian Response
(ASMR): a ow-like mental state. PeerJ 3 (2015), e851.
Emma L Barratt, Charles Spence, and Nick J Davis. 2017. Sensory determinants of
the autonomous sensory meridian response (ASMR): understanding the triggers.
PeerJ 5 (2017), e3846.
Ava Bartolome, Nguyen Binh Ha, and Shuo Niu. 2021. Investigating Multimodal
Interactions and Parasocial Attractiveness in YouTube ASMR Videos. In Compan-
ion Publication of the 2021 Conference on Computer Supported Cooperative Work
and Social Computing (CSCW ’21). Association for Computing Machinery, New
York, NY, USA, 14–18.
Gerlof Bouma. 2009. Normalized (pointwise) mutual information in collocation
extraction. Proceedings of GSCL 30 (2009), 31–40.
Jean Burgess. 2012. YouTube and the formalisation of amateur media. In Amateur
Media. Routledge, 69–74.
Jean Burgess and Joshua Green. 2018. YouTube: Online video and participatory
culture. John Wiley & Sons, Cambridge, UK.
Sean. Cannell and Benji. Travis. 2018. YouTube secrets : the ultimate guide to
growing your following and making money as a video inuencer. Lioncrest Publish-
ing. 192 pages. https://ww Secrets-Ultimate-Following-
Kathy Charmaz and Linda Liska Belgrave. 2015. Grounded Theory. https:
Harry Cheadle. 2012. ASMR, the Good Feeling no one can Explain. VICE.
http://www. vice. com/read/asmr-the-good-feeling-no-one-can-explain (accessed
October 15, 2014) (2012).
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013.
Predicting depression via social media. Icwsm 13 (2013), 1–10.
Jialin Deng, Yan Wang, Carlos Velasco, Ferran Altarriba Altarriba Bertran,
Rob Comber, Marianna Obrist, Katherine Isbister, Charles Spence, and Flo-
rian ’Floyd’ Mueller. 2021. The Future of Human-Food Interaction. In Ex-
tended Abstracts of the 2021 CHI Conference on Human Factors in Computing
Systems. Association for Computing Machinery, New York, NY, USA. https:
Yvonne D Eaves. 2001. A synthesis technique for grounded theory data analysis.
Journal of Advanced Nursing 35, 5 (9 2001), 654–663.
Ethan Fast, Binbin Chen, and Michael S Bernstein. 2016. Empath: Understanding
Topic Signals in Large-Scale Text. In Proceedings of the 2016 CHI Conference
on Human Factors in Computing Systems (CHI ’16). Association for Computing
Machinery, New York, NY, USA, 4647–4657.
Beverley Fredborg, Jim Clark, and Stephen D Smith. 2017. An Examination of
Personality Traits Associated with Autonomous Sensory Meridian Response
(ASMR). Frontiers in Psychology 8 (2017), 247.
Beverley K Fredborg, James M Clark, and Stephen D Smith. 2018. Mindfulness
and autonomous sensory meridian response (ASMR). PeerJ 6 (8 2018), e5414.
Rob Gallagher. 2016. Eliciting Euphoria Online: The Aesthetics of “ASMR” Video
Culture. FILM CRITICISM 40, 2 (6 2016).
Yumei Gan. 2021. Capturing love at a distance: Multisensoriality in intimate video
calls between migrant parents and their left-behind children. Social Interaction.
Video-Based Studies of Human Sociality 4, 3 (2021).
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
David C Giles. 2002. Parasocial Interaction: A Review of the Literature and a
Model for Future Research. Media Psychology 4, 3 (8 2002), 279–305. https:
Paula Clare Harper. 2020. ASMR: bodily pleasure, online performance, digital
modality. Sound Studies 6, 1 (1 2020), 95–98.
Tilo Hartmann. 2017. Parasocial interaction, parasocial relationships, and well-
being. In The Routledge handbook of media use and well-being: International per-
spectives on theory and research on positive media eects. Routledge/Taylor & Fran-
cis Group, New York, NY, US, 131–144.
Marc Hassenzahl. 2010. Experience Design: Technology for All the Right Reasons.
Synthesis Lectures on Human-Centered Informatics 3, 1 (1 2010), 1–95. https:
Donald Horton and R Richard Wohl. 1956. Mass Communication and Para-Social
Interaction. Psychiatry 19, 3 (8 1956), 215–229.
Mu Hu, Mingli Zhang, and Nuan Luo. 2016. Understanding participation on video
sharing communities: The role of self-construal and community interactivity.
Computers in Human Behavior 62 (2016), 105–115.
Agnieszka B Janik McErlean and Michael J Banissy. 2017. Assessing Individual
Variation in Personality and Empathy Traits in Self-Reported Autonomous Sen-
sory Meridian Response. Multisensory Research 30, 6 (2017), 601–613. https:
Isabel L Kampmann, Paul M G Emmelkamp, and Nexhmedin Morina. 2016. Meta-
analysis of technology-assisted interventions for social anxiety disorder. Journal
of Anxiety Disorders 42 (2016), 71–84.
Ryan M Kelly, Yueyang Cheng, Dana McKay, Greg Wadley, and George Buchanan.
2021. “It’s About Missing Much More Than the People”: How Students Use Digital
Technologies to Alleviate Homesickness. In Proceedings of the 2021 CHI Conference
on Human Factors in Computing Systems (CHI ’21). Association for Computing
Machinery, New York, NY, USA.
Helle Breth Klausen. 2019. ’Safe and sound’: What technologically-mediated
ASMR is capable of through sound. SoundEects - An Interdisciplinary Journal of
Sound and Sound Experience 8, 1 (2019), 87–103. https://www.sounde
Josephine Klefeker, Libi Striegl, and Laura Devendorf. 2020. What HCI Can Learn
from ASMR: Becoming Enchanted with the Mundane. In Proceedings of the 2020
CHI Conference on Human Factors in Computing Systems (CHI ’20). Association
for Computing Machinery, New York, NY, USA, 1–12.
Alexsandra Kovacevich and David Huron. 2019. Two studies of autonomous
sensory meridian response (ASMR): The relationship between ASMR and music-
induced frisson. Empirical musicology review 13, 1-2 (2019), 39–63.
Kate Szer Kurtin, Nina O’Brien, Deya Roy,and Linda Dam. 2018. The Development
of Parasocial Interaction Relationships on YouTube. The Journal of Social Media
in Society; Vol 7, No 1 (2018): The Journal of Social Media in Society 7 (5 2018),
Joanna Łapińska. [n.d.]. Posthuman and Post-Cinematic Aect in ASMR “Fixing
IN A POSTHUMAN PERSPECTIVE. Creativity Studies 13, 2 (5 2020), 336–350.
Yoonjoo Lee, John Joon Young Chung, Jean Y Song, Minsuk Chang, and Juho
Kim. 2021. Personalizing Ambience and Illusionary Presence: How People Use
“Study with Me” Videos to Create Eective Studying Environments. In Proceedings
of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21).
Association for Computing Machinery, New York, NY, USA.
Mengjie Liu and Qiang Zhou. 2019. A Preliminary Compilation of a Digital Video
Library on Triggering Autonomous Sensory Meridian Response (ASMR): A Trial
Among 807 Chinese College Students. Frontiers in Psychology 10 (2019), 2274.
Anna M Lomanowska and Matthieu J Guitton. 2016. Online intimacy and well-
being in the digital age. Internet Interventions 4 (2016), 138–144.
Jessica Maddox. 2020. What do creators and viewers owe each other? Micro-
celebrity, reciprocity, and transactional tingles in the ASMR YouTube community.
First Monday 26, 1 SE - Articles (12 2020).
Hugh S Manon. 2018. ASMR Mania, Trigger-Chasing, and the Anxiety of Digital
Repletion. Springer International Publishing, Cham, 227–248.
10.1007/978-3- 319-63817- 1{_}12
Allison Mooney, Jason Klein, Consumer Goods, and Consumer Trends. 2016.
ASMR Videos Are the Biggest YouTube Trend You’ve Never Heard Of. Google,
Sept (2016).
Ricardo Morales, Daniela Ramírez-Benavides, and Mario Villena-Gonzalez. 2021.
Autonomous Sensory Meridian Response self-reporters showed higher scores for
cognitive reappraisal as an emotion regulation strategy. PeerJ 9 (2021), e11474.
Robby Nadler. 2020. Understanding “Zoom fatigue”: Theorizing spatial dynamics
as third skins in computer-mediated communication. Computers and Composition
58 (2020), 102613.
Iida Nissinen. 2018. Tingles, tea boxes and sticky sounds–ASMR read diractively
with agential realism. (2018).
Shuo Niu, Ava Bartolome, Cat Mai, and Ha B Nguyen. 2021. #Stayhome #withme:
How do youtubers help with covid-19 loneliness?. In Conference on Human Factors
in Computing Systems - Proceedings.
Shuo Niu, Cat Mai, Katherine G McKim, and D Scott McCrickard. 2021.
#TeamTrees: Investigating How YouTubers Participate in a Social Media Cam-
paign. Proc. ACM Hum.-Comput. Interact. 5, CSCW2 (10 2021).
Theodore Oing and Julie Prescott. 2018. Implementations of Virtual Reality for
Anxiety-Related Disorders: Systematic Review. JMIR Serious Games 6, 4 (2018),
Sharon Oviatt. 1999. Ten Myths of Multimodal Interaction. Commun. ACM 42,
11 (11 1999), 74–81.
Emmi Parviainen and Marie Louise Juul S\ondergaard. 2020. Experiential Quali-
ties of Whispering with Voice Assistants.In Procee dings of the 2020 CHI Conference
on Human Factors in Computing Systems (CHI ’20). Association for Computing
Machinery, New York, NY, USA, 1–13.
James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015. The
development and psychometric properties of LIWC2015. Technical Report.
Giulia Poerio. 2016. Could Insomnia Be Relieved with a YouTube Video? The
Relaxation and Calm of ASMR. Springer International Publishing, Cham, 119–128. 319-45264- 7{_}15
Giulia Poerio. 2016. Could Insomnia Be Relieved with a YouTube Video? The
Relaxation and Calm of ASMR. In The Restless Compendium : Interdisciplinary
Investigations of Rest and Its Opposites, Felicity Callard, Kimberley Staines, and
James Wilkes (Eds.). Springer International Publishing, Cham, 119–128. https:
// 319-45264- 7{_}15
Giulia Lara Poerio, Emma Blakey, Thomas J Hostler, and Theresa Veltri. 2018.
More than a feeling: Autonomous sensory meridian response (ASMR) is charac-
terized by reliable changes in aect and physiology. PLOS ONE 13, 6 (6 2018),
Eugenia Ha Rim Rho and Melissa Mazmanian. 2020. Political Hashtags & the Lost
Art of Democratic Discourse. In Proceedings of the 2020 CHI Conference on Human
Factors in Computing Systems (CHI ’20). Association for Computing Machinery,
New York, NY, USA, 1–13.
Craig Richard. 2018. Brain tingles: The secret to triggering autonomous sensory
meridian response for improved sleep, stress relief, and head-to-toe euphoria. Simon
and Schuster.
Dana Rotman, Jennifer Golbeck, and Jennifer Preece. 2009. The Community is
Where the Rapport is – on Sense and Structure in the Youtube Community. In
Proceedings of the Fourth International Conference on Communities and Technolo-
gies (C&T ’09). Association for Computing Machinery, New York,N Y, USA, 41–50.
Rebecca B Rubin and Michael P McHugh. 1987. Development of parasocial
interaction relationships. Journal of Broadcasting & Electronic Media 31, 3 (6
1987), 279–292.
Kristen M Scott, Simone Ashby, and Julian Hanna. 2020. "Human, All Too
Human": NOAA Weather Radio and the Emotional Impact of Synthetic Voices. In
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.
Toward Multimodal Human-Computer Interface. In Advances in Image Processing
and Understanding. Series in Machine Perception and Articial Intelligence,
Vol. Volume 52. WORLD SCIENTIFIC, 349–365.
Stefan Siersdorfer, Sergiu Chelaru, Wolfgang Nejdl, and Jose San Pedro. 2010.
How Useful Are Your Comments? Analyzing and Predicting Youtube Comments
and Comment Ratings. In Proceedings of the 19th International Conference on
World Wide Web (WWW ’10). Association for Computing Machinery, New York,
NY, USA, 891–900.
Naomi Smith and Anne-Marie Snider. 2019. ASMR, aect and digitally-mediated
intimacy. Emotion, Space and Society 30 (2019), 41–48.
Stephen D Smith, Beverley Katherine Fredborg, and Jennifer Kornelsen. 2020.
Functional connectivity associated with ve dierent categories of Autonomous
Sensory Meridian Response (ASMR) triggers. Consciousness and Cognition 85
(2020), 103021.
An Understanding of Multimodal and Parasocial Interactions in ASMR videos CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA
Stephen D Smith, Beverley Katherine Fredborg, and Jennifer Kornelsen. 2017.
An examination of the default mode network in individuals with autonomous
sensory meridian response (ASMR). Social Neuroscience 12, 4 (7 2017), 361–365.
Rebecca Lurie Starr, Tianxiao Wang, and Christian Go. 2020. Sexuality vs.
sensuality: The multimodal construction of aective stance in Chinese ASMR
performances*. Journal of Sociolinguistics 24, 4 (9 2020), 492–513. https:
Amanda Stears. 2019. Three Things You Need to Know About ASMR
Videos. you-need- to- know-about-
Emma Leigh Waldron. 2016. "This FEELS SO REAL!" Sense and sexuality in
ASMR videos. First Monday 22, 1 (12 2016).
Jayne Wallace, Anja Thieme, Gavin Wood, Guy Schoeld, and Patrick Olivier.
2012. Enabling Self, Intimacy and a Sense of Home in Dementia: An Enquiry into
Design in a Hospital Setting. In Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (CHI ’12). Association for Computing Machinery,
New York, NY, USA, 2629–2638.
Philip Weber, Thomas Ludwig, Sabrina Brodesser, and Laura Grönewald. 2021.
“It’s a Kind of Art!”: Understanding Food Inuencers as Inuential Content
Creators. In Proceedings of the 2021 CHI Conference on Human Factors in Computing
Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA.
Donghee Yvette Wohn, Guo Freeman, and Caitlin McLaughlin. 2018. Explain-
ing Viewers’ Emotional, Instrumental, and Financial Support Provision for Live
Streamers. In Proceedings of the 2018 CHI Conference on Human Factors in Com-
puting Systems (CHI ’18). Association for Computing Machinery, New York, NY,
USA, 1–13.
Zhenhui Yuan, Gheorghita Ghinea, and Gabriel-Miro Muntean. 2015. Beyond
Multimedia Adaptation: Quality of Experience-Aware Multi-Sensorial Media
Delivery. IEEE Transactions on Multimedia 17, 1 (2015), 104–117.
Michele Zappavigna. 2020. Digital intimacy and ambient embodied copresence
in YouTube videos: construing visual and aural perspective in ASMR role play
videos. Visual Communication (7 2020), 1470357220928102.
Following are example comments with the highest counts of social,
intimate, body, perception, relaxation, and sleepiness words. The
percentages of word use in statistical analysis are calculated based
on a text corpus that merges all comments of one video.
Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi kiss-mark kiss-mark kiss-mark
kiss-mark kiss-mark kiss-mark kiss-mark
love you !!!!!!!!!!!!!!!!!!!!!!
Love you love you love you love you love you love you love you love
you thumbs-up thumbs-up smiling-face waving-hand waving-hand
smiling-face smiling-face-with-smiling-eyes smiling-face-with-smiling-
eyes smiling-face-with-smiling-eyes smiling-face-with-smiling-eyes
smiling-face-with-smiling-eyes smiling-face-with-smiling-eyes thumbs-
up thumbs-up thumbs-up thumbs-up
Hi kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark
kiss-mark kiss-mark heart-decoration heart-decoration bouquet bou-
quet bouquet two-hearts two-hearts cherry-blossom smiling-face-
with-heart-eyes smiling-face-with-heart-eyes tulip tulip tulip
Love you love you love you love you love you
Fool fool fool fool fool fool fool fool fool fool fool fool fool fool fool
fool fool fool fool fool fool fool fool fool . .. [all the rest “fool”]
Love you love you love you love you love you love you love you
love heart-suit heart-suit heart-suit heart-suit heart-suit heart-suit
heart-suit heart-suit heart-suit ribbon strawberry smiling-face-with-
hearts smiling-face-with-hearts smiling-face-with-hearts smiling-
face-with-hearts smiling-face-with-hearts smiling-face-with-hearts
Hi kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark
kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-
mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark
kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-mark kiss-
mark kiss-mark kiss-mark kiss-mark
Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi
Love love the set up, make up and atmosphere!
Social distance bro social distance back up bro I was not ready
Literally nobody does casual conversation and personal attention
like ASMR Power of Sound
Good video, keep it up and you will grow
does my mom have to sign the permission slip?
I literally looked up simp asmr and this came up
Your whole get up and close up is great!
little by little closer and closer ill be here upside-down-face
How the hell she wake up with make up on
everything will be okay when you stand up really quick
Head shoulders knees and toes knees and knees knees and toes
Great. Body scan. Head...shoulders...knees and toes, knees and toes.
Eyes: sleep. . .Brain: COMMENTSSSS
OH!!! SHIT!!!!! smiling-face-with-heart-eyes smiling-face-with-heart-
eyes smiling-face-with-heart-eyes smiling-face-with-heart-eyes smiling-
face-with-heart-eyes.....face-with-tears-of-joy face-with-tears-of-joy
Boobs..... breasts.... tits.... and I’m done
MY EYES !!!!!!!!!
Did she say nose bleed to sleep???neutral-face face-with-rolling-eyes
ushed-face expressionless-face
Lyrics Mouth Sounds Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds**Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds**Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds**Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds**Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds**Mouth Sounds**Mouth Sounds**Mouth Sounds*
*Mouth Sounds*imma sleep now Gn
Madi-“eyes and ears and mouth and nose”Me-“head, shoulders,
knees and toes”face-with-tears-of-joy
Beautiful... Beautiful... Beautiful... Beautiful...
Tingles!!!!!!!!! smiling-face-with-smiling-eyes
Kakyoin: Lick lick lick lick lick lick lick lick lick lick lick... [all the
rest are “lick”]
are “TINGLY”]
Audiobook Audiobook Audiobook Audiobook Audiobook Audio-
book Audiobook Audiobook Audiobook Audiobook Audiobook Au-
Beautiful picture, beautiful voice, beautiful story. Just beautiful!
Beautiful Eyes , Beautiful Colors & Beautiful Presentation. Beautiful
tingles tingles tingles tingles tingles
Relax tktktktk relax relax relax relax relax relax relax relax relax
Oh How I love my tingly tingly tingly tingly tingly tingly tingly
CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA Anonymous, et al.
the hand movements towards the end were super super super relax-
Her voice is super super relaxing to me right now
Ur voice always helps me relax thanks for the videos
Love your soft and gentle voice,so relaxing sleeping-face
your calm voice helps me sleep. thank you.
This guy has an insanely relaxing voice and calm demeanour
OMG smiling-face-with-heart-eyes thank you thank you thank
you thank you smiling-face-with-smiling-eyes red-heartgrowing-
that slow pace soft voice makes the video so enjoyable.
Good night good night sleep sleep go to sleep go to sleep go to sleep
Having trouble falling asleep tonight. This has helped, goodnight!
This puts me to sleep every night for past 3 weeks
Can you do a asmr dim light shh shh go to sleep now shh shush i
tuck you in bed with lots shh shh
Or a asmr dim light shh shush I tuck in bed its just a nightmare aww
shh shh for crying and sleep
Or best friend helps you fall asleep with lots of shh shh sweetheart
i tuck you in bed with lots shh shh
You helped me sleep better the past few nights.
Your videos are helpful with insomnia and other sleep issues
i was so stressed tonight and this helped ease my panic thankyou
I kept falling asleep and then waking up then falling asleep back-
... A perceptual video summarization and retrieval was proposed to provide a precise representation of video contents [11]. The interaction modalities and parasocial attractions of ASMR videos was annotated for multi-modal video interactions [5]. However, an effective way to explore ASMR videos has been lacking in previous works. ...
Full-text available
Autonomous sensory meridian response (ASMR) is a type of video contents designed to help people relax and feel comfortable. Users usually retrieve ASMR contents from various video websites using only keywords. However, it is challenging to examine satisfactory contents to reflect users' needs for ASMR videos using keywords or content-based retrieval. To solve this issue, we propose a perceptual video retrieval system for ASMR videos and provide a novel retrieval user interface that allows users to retrieve content according to watching purpose and anticipated expectations, such as excitement, calmness, stress and sadness. An ASMR video perception dataset is constructed with annotations on affective responses after watching the videos. To verify the proposed video retrieval system, a user study is conducted showing that users can retrieve satisfactory ASMR contents easily and efficiently compared to conventional keywords-based retrieval systems.
Autonomous sensory meridian response (ASMR) is a type of video contents designed to help people relax and feel comfortable. Users usually retrieve ASMR contents from various video websites using only keywords. However, it is challenging to examine satisfactory contents to reflect users’ needs for ASMR videos using keywords or content-based retrieval. To solve this issue, we propose a perceptual video retrieval system for ASMR videos and provide a novel retrieval user interface that allows users to retrieve content according to watching purpose and anticipated expectations, such as excitement, calmness, stress and sadness. An ASMR video perception dataset is constructed with annotations on affective responses after watching the videos. To verify the proposed video retrieval system, a user study is conducted showing that users can retrieve satisfactory ASMR contents easily and efficiently compared to conventional keywords-based retrieval systems. KeywordsASMRVideo retrievalUser interfaceUser perception
Frisson is the feeling and experience of physical reactions such as shivers, tingling skin, and goosebumps. Using entrainment through facilitating interpersonal transmissions of embodied sensations, we present "Frisson Waves" with the aim to enhance live music performance experiences. "Frisson Waves" is an exploratory real-time system to detect, trigger and share frisson in a wave-like pattern over audience members during music performances. The system consists of a physiological sensing wristband for detecting frisson and a thermo-haptic neckband for inducing frisson. In a controlled environment, we evaluate detection (n=19) and triggering of frisson (n=15). Based on our findings, we conducted an in-the-wild music concert with 48 audience members using our system to share frisson. This paper summarizes a framework for accessing, triggering and sharing frisson. We report our research insights, lessons learned, and limitations of "Frisson Waves".
Conference Paper
Full-text available
Autonomous Sensory Meridian Response (ASMR) videos have become a popular video genre on YouTube and attracted millions of views every day. However, there is limited understanding in HCI and CSCW of how the ASMRtist community on YouTube leveraged the multi-sensorial stimuli to design the experiences of calm and relaxation and construct parasocial relationships with the viewers. This work presents a typology to understand the multimodal interactions and parasocial attractiveness in YouTube ASMR videos. The annotation of 88 YouTube ASMR videos reveals that, beyond tingling sensation, ASMR videos are also experiences of social connection, physical intimacy, and activity observation. We describe the future design and research opportunities to utilize ASMR effects in video-based applications.
Full-text available
YouTube is not only a platform for content creators to share videos but also a virtual venue for hosting community activities, such as social media campaigns (SMCs). SMCs for public awareness is a growing and reoccurring phenomenon on YouTube, during which content creators make videos to engage their audience and raise awareness of global challenges. However, how the unique celebrity culture on YouTube affects collective actions is an underexplored area. This work examines an SMC on YouTube, #TeamTrees, initiated by a YouTube celebrity and sought to raise people’s awareness of tree-planting and climate change. The authors annotated and analyzed 992 #TeamTrees videos to explore how YouTube celebrities, professionals, and amateurs in different channel topics diagnose problems, present solutions, and motivate actions. This study also looks into whether platform identities and framing activities affect campaign reach and engagement. Results suggest that #TeamTrees reached creators who are generally not active in social issues. The participating YouTubers were likely to motivate the viewers to donate and join celebrities’ and community’s actions, but less involved in examining the environmental problems. Celebrities’ videos dominated the campaign’s influence. Amateurs’ videos had a higher engagement level, although they need more support to frame campaign activities. Based on these findings, we discuss design implications for video-sharing platforms to support future SMCs.
Full-text available
Studies have shown that multisensorial interactions are an important medium for achieving love and intimacy. Nevertheless, the question remains: How do people constitute their “love at a distance” when they can only interact with each other over a video call, in which certain sensorial resources (e.g., touch, smell, and taste) are not available? Drawing from two years of video-based fieldwork involving recordings of habitual calls among the members of migrant families, I consider the application of Ethnomethodology and Conversation Analysis (EMCA)-informed video analysis to investigating intimate relationships constructed through remote means. I present an innovative method of video recording that allows me to analyze the interactional resources toward which participants orient themselves in their calls. I illustrate this approach with data analysis to demonstrate the relevance of video to examining intimacy at a distance. This article proposes that a distinct contribution of video-based research to the discipline lies in its ability to capture how people use their embodied and sensorial interactions to form intimacy across distances.
Full-text available
Background Autonomous Sensory Meridian Response (ASMR) describes the experience of a pleasant tingling sensation along the back of the head, accompanied with a feeling of well-being and relaxation, in response to specific audio-visual stimuli, such as whispers, soft sounds, and personal attention. Previous works have assessed individual variations in personality traits associated with ASMR, but no research to date has explored differences in emotion regulation associated with ASMR. This omission occurred even when ASMR, a sensory-emotional experience, has been proposed to be located in a sound sensitivity spectrum as the opposite end of misophonia, a phenomenon associated with difficulties regulating emotions. The present work aimed to assess group differences between ASMR self-reporters and non-ASMR controls associated with emotion regulation strategies. Methods We used the validated Spanish version of the Emotion Regulation Questionnaire to assess individual differences in the use of cognitive reappraisal and expressive suppression. Results Our results showed that participants who experience ASMR had higher scores in the cognitive reappraisal subscale of the emotion regulation questionnaire than the non-ASMR group. Conclusions Individuals who experience ASMR reported higher use of cognitive reevaluation of emotionally arousing situations, suggesting more effectiveness in regulating emotions. Our finding further elucidates individual differences related to this experience, supporting that ASMR is a real psychophysiological phenomenon associated with other psychological constructs and has remarkable consequences in affective/emotional dimensions and general well-being.
Conference Paper
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There is an increasing interest in food within the HCI discipline, with many interactive prototypes emerging that augment, extend and challenge the various ways people engage with food, ranging from growing plants, cooking ingredients, serving dishes and eating together. Grounding theory is also emerging that in particular draws from embodied interactions, highlighting the need to consider not only instrumental, but also experiential factors specific to human-food interactions. Considering this, we are provided with an opportunity to extend human-food interactions through knowledge gained from designing novel systems emerging through technical advances. This workshop aims to explore the possibility of bringing practitioners, researchers and theorists together to discuss the future of human-food interaction with a particular highlight on the design of experiential aspects of human-food interactions beyond the instrumental. This workshop extends prior community building efforts in this area and hence explicitly invites submissions concerning the empirically-informed knowledge of how technologies can enrich eating experiences. In doing so, people will benefit not only from new technologies around food, but also incorporate the many rich benefits that are associated with eating, especially when eating with others.
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Although the number of infuencers is increasing and being an infuencer is one of the most frequently mentioned career aspirations of young people, we still know very little about infuencers’ motivations and actual practices from the HCI perspective. Driven by the emerging feld of Human-Food Interaction and novel phenomena on social media such as Finstas, ASMR, Mukbang and live streaming, we would like to highlight the signifcance of food infuencers as infuential content creators and their social media practices. We have conducted a qualitative interview study and analyzed over 1,500 posts of food content creators on Instagram, focusing on practices of content creation, photography, staging, posting, and use of technology. Based on our fndings, we have derived a process model that outlines the practices of this rather small, but infuential user group. We contribute to the feld of HCI by outlining the practices of food infuencers as infuential content creators within the social media sphere to open up design spaces for interaction researchers and practitioners.
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Loneliness threatens public mental wellbeing during COVID-19. In response, YouTube creators participated in the #StayHome #WithMe movement (SHWM) and made myriad videos for people experiencing loneliness or boredom at home. User-shared videos generate parasocial attachment and virtual connectedness. However, there is limited knowledge of how creators contributed videos during disasters to provide social provisions as disaster-relief. Grounded on Weiss's loneliness theory, this work analyzed 1488 SHWM videos to examine video sharing as a pathway to social provisions. Findings suggested that skill and knowledge sharing, entertaining arts, homelife activities, live chatting, and gameplay were the most popular video styles. YouTubers utilized parasocial relationships to form a space for staying away from the disaster. SHWM YouTubers provided friend-like, mentor-like, and family-like provisions through videos in different styles. Family-like provisions led to the highest overall viewer engagement. Based on the findings, design implications for supporting viewers' mental wellbeing in disasters are discussed.
ASMR has skyrocketed to international popularity in recent years, and a thriving community and cultural exists around the phenomenon on YouTube. However, misunderstandings about the practice persist, and little is known about this community in terms of its texts and practices. This research draws on a multiyear digital ethnography into the ASMR culture and community on YouTube, where I analyze how microcelebrity, the attention economy, platform-specific dynamics, and content creation merge. Drawing on extant research that identifies reciprocity as a key cultural dynamic on YouTube, I argue reciprocity in the ASMR YouTube community, as well as the relationship between creator and viewer, can best be understood as transactional tingles: relaxation in exchange for likes, clicks, and views within the attention economy. Transactional tingles is also a contemporary blending of more traditional art patronage and dealer-critic systems, which offers insights into the role viewers and platforms play in content creation, digital labor, and precarity.