Science communication on YouTube: Factors that affect channel and video popularity

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DOI: 10.1177/0963662515572068 · Source: PubMed
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YouTube has become one of the largest websites on the Internet. Among its many genres, both professional and amateur science communicators compete for audience attention. This article provides the first overview of science communication on YouTube and examines content factors that affect the popularity of science communication videos on the site. A content analysis of 390 videos from 39 YouTube channels was conducted. Although professionally generated content is superior in number, user-generated content was significantly more popular. Furthermore, videos that had consistent science communicators were more popular than those without a regular communicator. This study represents an important first step to understand content factors, which increases the channel and video popularity of science communication on YouTube. © The Author(s) 2015.
Public Understanding of Science
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DOI: 10.1177/0963662515572068
Science communication on
YouTube: Factors that affect
channel and video popularity
Dustin J. Welbourne
Australian National Centre for Public Awareness of Science, Australian National University, Australia;
University of New South Wales, Australia
Will J. Grant
Australian National University, Australia
YouTube has become one of the largest websites on the Internet. Among its many genres, both professional
and amateur science communicators compete for audience attention. This article provides the first overview
of science communication on YouTube and examines content factors that affect the popularity of science
communication videos on the site. A content analysis of 390 videos from 39 YouTube channels was conducted.
Although professionally generated content is superior in number, user-generated content was significantly
more popular. Furthermore, videos that had consistent science communicators were more popular than
those without a regular communicator. This study represents an important first step to understand content
factors, which increases the channel and video popularity of science communication on YouTube.
channel, content analysis, factors, popularity, review, science communication, video, YouTube
1. Introduction
Science communication has traditionally been dominated by professional communicators
employed directly or indirectly by the mainstream media (Valenti, 1999). With the emergence of
Web 2.0, platforms such as blogs, wikis, social media and video sharing websites have redefined
the mediascape (Brossard, 2013; Minol et al., 2007). Web 2.0 provides an alternative to traditional
content distribution by reducing the barriers for content creators to reach an audience (Juhasz,
2009). Many Web 2.0 platforms are constructed on a participatory culture, a ‘function that is most
noticeably absent from most mainstream media’ (Burgess and Green, 2009: 29). Thus, in the era
of Web 2.0, viewers have shifted from being passive consumers to active participants. Science
Corresponding author:
Dustin J. Welbourne, Australian National Centre for Public Awareness of Science, Australian National University,
Science Road, Canberra, ACT 2601, Australia.
572068PUS0010.1177/0963662515572068Public Understanding of ScienceWelbourne and Grant
Theoretical/Research Paper
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2 Public Understanding of Science
communication is now conducted not only by professional communicators but also by scientists,
interest groups, professional organisations and passionate amateurs across numerous Web 2.0
platforms (Claussen et al., 2013; Lo et al., 2010; Nisbet and Scheufele, 2009).
YouTube is a particularly significant example of the Web 2.0 phenomenon. YouTube was
founded by employees of PayPal in 2005 and has undergone spectacular growth to become one of
the top websites on the Internet (Alexa Internet Inc., 2015; Burgess and Green, 2009). YouTube
was founded on the user-generated content (UGC) model, whereby content was to be derived
from YouTube users and consumers. However, the sale of YouTube to Google in 2006 marked the
beginning of a deliberate effort by YouTube management to increase the volume of professionally
generated content (PGC) – content created by corporate entities to extend the reach of commercial
branding (Ackerman and Guizzo, 2011; Kim, 2012; Wasko and Erickson, 2009). PGC and
‘Astroturf’ (content created by corporate entities to mimic grassroots or UGC) have subsequently
increased over the period (Burgess and Green, 2009). The evolving demographic of content
creators on YouTube has meant that amateur science communicators now compete for views with
large well-funded corporations like the British Broadcasting Corporation and the Discovery
Despite the large number of content consumers on YouTube, reaching an audience is not
guaranteed. Reaching an audience and achieving success is a function of how popular a channel
and its videos become, as measured by the number of subscribers and views received (Burgess
and Green, 2009). The popularity of any given video is a function of the video’s content factors,
content-agnostic factors and YouTube’s video recommendation system (Borghol et al., 2012;
Figueiredo et al., 2014). Content factors are the stylistic and informational characteristics of a
video (e.g. topic, duration or delivery style), whereas content-agnostic factors relate to charac-
teristics external to the video (e.g. the creator’s social network or video upload date and time).
YouTube’s recommendation system both identifies what is popular and creates what is popular
in a rich-get-richer popularity scenario (Figueiredo et al., 2011; Szabo and Huberman, 2010;
Zhou et al., 2010). That is, the recommendation system recommends popular videos to viewers,
which in turn increases the popularity of those videos (Zhou et al., 2010). Although a growing
body of literature has independently addressed content and content-agnostic factors of YouTube
videos broadly, few studies have examined science communication videos specifically.
To fill this knowledge gap, we examined content factors of science communication videos
on YouTube for their influence on video popularity. We first assessed the differences in profes-
sionally and user-generated channels, specifically, the number of views, subscribers, age of the
channel and number of videos created. Then, within the context of PGC and UGC, we examined
the impact of video length and pace and how the video was delivered – delivery being a function
of the gender, style, and the continuity of the delivery person(s) between videos. This was
achieved by manually coding content factors of a sample of videos and analysing the relation-
ships against YouTube’s popularity metrics. Although manually coding limits the quantity of
videos that can be sampled, it was necessary to obtain much of the data required. Understanding
which video content factors contribute to video popularity on YouTube and the impact of PGC
on UGC, if there is any, will assist content creators to create more engaging and popular science
communication content. In the ‘Literature review’ section, current research on understanding
popularity on YouTube is reviewed, followed by the ‘Method’ section that will detail the sampling
protocols and video coding procedures. The ‘Results’ section follows, divided into channel-
specific and video-specific sections, and finally, the results are discussed and the article concludes
by highlighting future research.
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Welbourne and Grant 3
2. Literature review
As there are few studies that have examined science communication on YouTube, the selection
of content factors in this study may seem arbitrary, although this is not the case. We focus on
content factors, as opposed to content-agnostic factors, as they are valuable to understanding
drivers of popularity broadly and allow recommendations to be made in the creation of science
communication content. Upon accepting content factors, the first evaluation is a fundamental
separation of professionally generated and user-generated channels and their videos. Expected
differences in channel resources between user-generated and professionally generated channels
led us to examine content factors related to the delivery of content. For instance, a channel with
large resources may be capable of employing professional creators, which undoubtedly have
different skill sets and, therefore, ideas about how a YouTube video should be presented.
Ultimately, the content factors selected provide a baseline for future research to build upon.
Before reviewing content factors, we briefly address the primary content-agnostic factor that
appears to drive video and channel popularity.
A channel’s social network is the primary content-agnostic factor that influences, and also
confounds, video and channel popularity (Burgess and Green, 2009; Juhasz, 2009; Yoganarasimhan,
2012). Crane and Sornette (2008) postulated three categories of video (viral, quality and junk) and
found that each had a distinct view count distribution history. Figueiredo et al. (2011) similarly
found that top videos (the quality category in Crane and Sornette (2008)) experience a significant
burst of activity, receiving many views in a single day or week, with other videos undergoing several
smaller peaks of activity. The growth of video views is linked to the rich-get-richer effect of the
recommendation system (Borghol et al., 2012) and the channel’s social network (Yoganarasimhan,
2012). Despite these findings, social network analysis on YouTube is problematic for two reasons.
First, a complete social network within YouTube cannot be attained because not all channels make
lists of ‘friends’ or ‘featured channels’ available, and, second, it is not feasible to determine the
social network of a channel beyond YouTube due to difficulties in connecting social networks
across platforms (Yoganarasimhan, 2012). Although an analysis of the social network of science
communication channels on YouTube is beyond the scope of this article, it is clearly an important
consideration in understanding channel popularity generally.
Although the popularity of a YouTube video is a function of content and content-agnostic factors,
content factors appear to be the most informative for understanding broad popularity within the
YouTube community. Broad popularity is meant here as popular among a wide spectrum of viewers,
whereas narrow or niche popularity is only popular within a limited audience. Figueiredo et al.
(2014) examined YouTube users’ perceptions of video popularity by exposing volunteers to pairs
of preselected videos. User preferences meant that in many evaluations users could not come to a
consensus on which video had the best content, but, in those evaluations where users did come to
a consensus, the video identified as having the preferred content was frequently more popular on
YouTube (Figueiredo et al., 2014). Hence, for a video to be popular among a broad audience, the
content must be broadly appealing. Therefore, understanding the content factors is vital to under-
standing what drives popularity broadly.
Most studies examining science communication on YouTube are directed at assessing the veracity
of the information, which, depending on the topic, does appear to influence video popularity.
Keelan et al. (2007) analysed 153 immunisation videos for accuracy and tone, categorised as posi-
tive, ambiguous or negative. Positive videos were those that presented immunisation in a positive
way, ambiguous content was neither for nor against and negative content had a central theme of
anti-immunisation. Keelan et al. (2007) found no errors in positive content, whereas 45% of negative
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4 Public Understanding of Science
content had misleading information. Despite misleading information, negative videos had higher
view count and ratings than positive videos. Conversely, Sood et al. (2011) analysed 199 videos on
kidney stone disease and found useful videos received significantly higher views than misleading
content. Still, other research has found no statistical difference in view count and ratings between
useful and misleading content (Ache and Wallace, 2008; Azer, 2012; Murugiah et al., 2011; Pandey
et al., 2010).
The type of channel is of particular interest in understanding YouTube popularity. Professionally
generated channels (i.e. channels that exist to extend commercial branding) often have superior
financial resources compared with user-generated channels. Financial resources can allow profes-
sionally generated channels to increase the appeal of the channel and/or of specific videos through
the creation of regular or large volumes of content and content of high production value. Hence,
the UGC community has expressed concern that they will be overshadowed by PGC (Kim, 2012).
Although superior resources might allow channels to employ professional video producers and
presenters, it has been argued that ‘in order to operate effectively as a participant in the YouTube
community, it is not possible simply to import learned conventions … from elsewhere (e.g. from
professional television production)’ (Burgess and Green, 2009: 69). Furthermore, the popularity of
YouTube content is not determined by the quantity of videos a channel uploads but by the views
and engagement (YouTube, 2012). Thus, while regular content assists in engaging one’s audience
(YouTube, n.d.), a channel must still host content that the YouTube community finds engaging.
Superior resources of a channel may give it an advantage through advertising. YouTube’s
video recommendation system uses the engagement metrics, or popularity metrics, to recommend
videos to other viewers. These can be manipulated as numerous websites sell fake views, com-
ments, likes and subscriptions for YouTube channels and videos (Hoffberger, 2013). While
YouTube has responded by continually policing the artificial inflation of popularity metrics,
which in the past has led to the removal of views and videos, it appears to be an ongoing problem
(Pfeiffenberger, 2014). Regardless of illegitimate forms of advertising, channels can purchase
legitimate advertising. Google advertising can be purchased to increase views and engagement on
videos and channels, thereby giving well-funded channels a competitive advantage.
In an information-rich world, the limiting factor in consuming content is the consumers’ atten-
tion (Davenport and Beck, 2001). Therefore, it logically follows that short videos and/or fast-paced
videos which give the illusion of being short might be more engaging than long or slow-paced
videos (Grabowicz, 2014). Although the length of science communication videos has not been
reviewed explicitly in the primary literature, several media companies have analysed YouTube
video length more generally. The Pew Research Center (2012) reviewed the most viewed YouTube
videos between January 2011 and March 2012 and found ~50% were less than 2 minutes and ~82%
were less than 5 minutes, and Ruedlinger (2012) claims video length was inversely correlated with
capturing and holding viewer attention in business videos. Nevertheless, these findings may be
indicative of sampling bias given that the average length of YouTube videos was found to be
4.4 minutes (Lella, 2014). That is, if the majority of videos are short, then it is likely that most
popular videos are short.
Although the evidence is weak, there is some suggestion that UGC is more popular than PGC.
Lorenc et al. (2013) reviewed the top 241 most subscribed channels and found ~68% were from
user-generated channels, and of the genres represented (comedy, n = 83; music, n = 79; gaming,
n = 36; fashion/beauty, n = 14; other, n = 29), only the music genre had more professional-generated
than user-generated channels. In the context of science communication, Lo et al. (2010) reviewed
videos on epilepsy and found that UGC content had more views, ratings and comments than PGC,
and noted that comments on UGC attempted to engage with the videos’ creator and other viewers,
whereas comments on PGC did not. However, little weight can be afforded to either of these
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Welbourne and Grant 5
findings as Lorenc et al. (2013) have not undergone peer-review and Lo et al. (2010) examined
only 10 videos that included only 2 professionally generated. Hence, this study makes a significant
contribution to the science communication literature by examining science communication on
YouTube more thoroughly.
3. Method
Video procurement
To achieve the aims of this article, it was calculated that a minimum sample of 385 videos was
required. To limit bias induced by channels with large numbers of videos, a clustered random
sampling approach was used. In December 2013, YouTube channels were randomly sampled in 50
channel blocks from the top 1000 channels from the SocialBlade (2013) categories of ‘Education’
and ‘Science & Technology’. Videos were then randomly sampled from each channel and reviewed
for inclusion. Videos in English, at least 180 days old and could be defined as science communica-
tion (in the context of this study, see definition below) were retained until 10 videos per channel
were identified, resulting in a total of 39 YouTube channels included in the dataset. Clone-videos
and channels principally composed of reposted content from other creators were excluded from the
Science communication
Science communication in practice is considerably broad, often attracting equally broad definitions
in the academic literature (sensu, Bryant, 2003; Gilbert and Stocklmayer, 2013). In this study, ‘science’
was taken as any topic that would be categorised into one of the Scopus science subject areas of
physical, life, health or social sciences, excluding the topic of ‘Arts and Humanities’ (Elsevier,
2014). The tone of communication of these topics can also be quite broad. Hence, ‘science com-
munication’ in this study was taken to be any video that might be seen as a form of science journalism
that is not overtly didactic or instructional, while also not being principally focused on entertainment.
Defining science communication in this way was necessary because of the different reasons that
one watches YouTube (Burgess and Green, 2009). Although this is somewhat subjective, consist-
ency was maintained as a single author (D.J.W.) reviewed all material for inclusion.
Data coding
The collection of channel data, video popularity metrics and video content factors of the identified
YouTube videos began in January 2014. Data were obtained on videos and channels using both
automated (Zdravkovic, 2013) and manual coding procedures. The following data were coded for
each channel:
(a) Channel age, as measured from the first upload event;
(b) Number of videos at the time of video procurement;
(c) Channel views at the time of video procurement;
(d) Channel subscriptions at the time of video procurement;
(e) Channel type, coded as PGC for channels named after corporate entities or as UGC for
channels that are YouTube derived.
The following popularity metrics were extracted for all videos simultaneously:
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6 Public Understanding of Science
(a) Video view count;
(b) Number of comments on the video;
(c) Number of subscriptions driven from the video;
(d) Number of times the video was shared;
(e) Total number of ratings.
Each video was reviewed manually and the following content factors coded.
(a) Video length (seconds) taken as the complete video duration.
(b) Pace of content delivery (words per minute) calculated from the video and YouTube’s auto-
matic transcript feature. Although this feature does not record each word accurately, it does
capture the number of words accurately (unpublished data).
(c) Communicator continuity (binary) identified whether a channel had a continuous science
communicator or communicators who delivered content. Channels were initially classified
into three categories of mostly continuous, >66% of videos had the same communicator;
mostly non-continuous, >66% of videos did not have the same communicator; and mixed.
In the final dataset, this was collapsed to a binary classification as no ‘mixed’ channels were
(d) Gender (male, female, both or no-gender) of the person or persons delivering the science
(e) Video style was coded as one of six styles identified while reviewing the dataset – Vlog:
an iconic YouTube video style where the presenter delivers content by talking directly to
the camera; Hosted: stylistically similar to vlog where the communicator presents the
information; however, other people such as members of the public or interviewees are also
part of the video content; Interview: videos where the person delivering content is being
interviewed by a person off camera who is often the video creator; Presentation: the
presenter is presenting information to an audience and not the camera specifically; Voice
over visuals: videos where someone talks over animated or static visuals; Text over visuals:
similar to voice over visual, but with text in place of the voice.
Statistical analysis
All statistical analysis was carried out in the R statistical package version 3.0.2 (Cran Team, 2014).
Provided assumptions held and data transformations were suitable, parametric tests were used,
otherwise non-parametric tests. Welch’s t-test was used in place of Student’s t-test where unequal
variance was identified using Levene’s test for homogeneity of variance. An alpha of .05 was used
for significance in all tests. Effect sizes and correlations were described according to Cohen (1988)
and Evans (1996).
4. Results
Channel results
A total of 411 YouTube channels were sampled to obtain the 39 science communication channels
required. These consisted of 21 professionally generated and 18 user-generated channels. The age
of professionally generated channels (M = 1220 days, standard deviation (SD) = 864) was not sig-
nificantly different from user-generated channels (M = 1263 days, SD = 679; Student’s t(37) = 0.17,
p = .87, Cohen’s d = 0.05). Professionally generated channels had significantly more videos than
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Welbourne and Grant 7
user-generated channels (Welch’s t(34.5) = 1.73, p = .04, Cohen’s d = 0.55; Figure 1(a)).
Professionally generated and user-generated channels both had highly positively skewed distribu-
tions of subscriptions and channel views (Figure 1(b) and (c)). Hence, half of professionally
generated and user-generated channels had less than ~1.8 × 106 and ~4.6 × 107 channel views
(respectively) and less than 26,533 and 366,805 subscriptions (respectively). Channel type had a
large effect on subscriptions and channel views; user-generated channels had significantly more
subscriptions (Welch’s t(33.4) = 4.90, p < .01, Cohen’s d = 1.55) and channels views (Student’s
t(37) = 3.38, p < .01, Cohen’s d = 1.09) than professionally generated channels.
Pearson’s product-moment correlation was used to examine the relationships between channel
data and popularity metrics. Both professionally generated and user-generated channels exhibited
similar relationships between channel data and popularity metrics; hence, channel type (i.e. UGC
or PGC) was not considered in the correlations. Channel views were very strongly positively
correlated with subscriptions (t(37) = 15.7, p < .01, r = .93) and moderately positively correlated
with the number of videos on a channel (t(37) = 2.8, p < .01, r = .42). However, by controlling for
subscriptions and uploads, views per subscription was not correlated with subscriptions (t(37) = 1.92,
p = .06, r = −.30), and no correlation was found between views per video and number of videos
(t(37) = 0.80, p = .43, r = −.13). Number of videos was moderately positively correlated with the age
of a channel (t(37) = 4.2, p < .01, r = .57), but after controlling for channel age no correlation was
found between the age and the number of videos uploaded daily (t(37) = 0.11, p = .92, r = −.07).
Interestingly, neither channel views nor subscriptions were correlated with the age of the channel
(t(37) = 1.32, p = .19, r = .21; t(37) = 0.01, p = .99, r = .00, respectively), and channel subscriptions
were not correlated with the number of videos on a channel (t(37) = 0.89, p = .38, r = .14).
Video results: popularity metrics
A total of 10 videos from each channel were acquired resulting in a final dataset of 210 videos
of PGC and 180 videos of UGC. Similar to channel age, video age was approximately normally
distributed (M = 752 days, SD = 540), and there was no significant video age difference between
PGC and UGC (Student’s t(387) = 0.54, p = .59, Cohen’s d = 0.06). All video popularity metrics (i.e.
views, comments, subscriptions driven, number of shares and total ratings) were found to be highly
Figure 1. The number of (a) videos, (b) subscriptions and (c) channel views of professionally generated
(PGC) and user-generated (UGC) YouTube science channels. Asterisks indicate a significant (p < .05)
difference between PGC and UGC.
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8 Public Understanding of Science
positively skewed (skew > 4.6, kurtosis > 24.8). Furthermore, Spearman’s rank-order correlation
showed that all popularity metrics were very strongly positively correlated to one another, which
differed little between channel type (all relationships ρ > 0.88 and p < .01). Hence, only video views
were considered further as the dependent variable.
Considering popularity metrics in terms of engagement revealed that engagement activity differed
between popularity metrics and that PGC and UGC were engaged with differently. Engagement
refers to the number of views received per event of another metric. A one-way between-subjects
analysis of variance (ANOVA) (followed by Tukey’s post hoc test) was conducted without video
type as a function. All engagement metrics had significantly different views per engagement event
(F(3, N = 647) = 467, p < .01, η2 = .52; Figure 2). That is, views per rating event were significantly
lower than per subscription driven, views per subscription driven were significantly lower than per
comment received and views per comment were significantly lower than per share event. Whether
a video was professionally generated or user-generated had no effect on the number of views
received per subscription driven (Welch’s t(199) = 0.26, p = .80, Cohen’s d = 0.03) or comment
received (Student’s t(345) = 1.53, p = .13, Cohen’s d = 0.16). However, UGC had significantly fewer
views than PGC per rating received (Student’s t(372) = 5.30, p < .01, Cohen’s d = 0.55), and PGC
had significantly fewer views than UGC per share event (Welch’s t(206) = 4.90, p < .01, Cohen’s
d = 0.63; Figure 2). Thus, for the same number of views UGC would receive significantly more
ratings, but PGC would be shared significantly more.
Video results: content factors
PGC and UGC differed in several, but not all, of the content factors measured. A chi-square test was
used to examine the proportions of PGC and UGC that contained a regular science communicator.
UGC had a significantly higher proportion of videos (~56%, n = 100) with regular communicators
than PGC (~37%, n = 77; χ2(1, N = 390) = 13.95, p < .01). A binomial exact test was used to evaluate
whether science communicators were equally represented by both genders. The test showed males
were in a significantly greater proportion of both PGC (p < .01) and UGC (p < .01; Figure 3(a)).
There was no null hypothesis to test the proportion of delivery styles employed in PGC and UGC;
still, Figure 3(b) shows that PGC was marginally more varied than UGC. The rapidity with which
Figure 2. Number of views of professionally generated (PGC) and user-generated (UGC) YouTube science
videos per engagement event. Asterisks indicate a significant (p < .05) difference between PGC and UGC.
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Welbourne and Grant 9
content was delivered, as measured in words per minute, was significantly quicker in UGC
(M = 169, SD = 32) than PGC (M = 153, SD = 27; Student’s t(338) = 5.10, p < .01, Cohen’s d = 0.55).
Despite the difference in pace, there was no significant difference in the length of PGC
(Median = 196 seconds, range = 19–4996 seconds) and UGC (M = 333 seconds, SD = 196 seconds;
Welch’s t(355) = 0.37, p = .71, Cohen’s d = 0.04).
Of the content factors measured, only communicator continuity, pace of delivery and (margin-
ally) gender appeared to impact video views. Videos with a regular communicator, in both video
types, had significantly more views than videos without a regular presenter (UGC: Student’s
t(178) = 9.03, p < .01, Cohen’s d = 1.35; PGC: Welch’s t(192) = 3.90, p < .01, Cohen’s d = 0.54;
Figure 4). Furthermore, the effect of a regular communicator was larger for views of UGC than
PGC. Using a one-way ANOVA, gender was not found to be significant for views of UGC (F(2,
N = 177) = 2.53, p = .08), whereas it was significant for PGC (F(2, N = 206) = 2.95, p = .03, η2 = .04).
Tukey’s post hoc test indicated that male-only PGC was viewed significantly more than PGC with
both genders present, although this was a small effect. Pearson’s product-moment correlation was
used to examine the impact of pace and video length on video views. Pace was found to be weakly
positively correlated with views in both UGC (t(160) = 2.60, p < .01, r = .21) and PGC (t(171) = 3.40,
p < .01, r = .25), but, interestingly, no correlation was identified between views and video length
(t(388) = 0.69, p = .49, r = −.03). Delivery style could not be analysed for its impact upon views as
a number of channels were found to use only one style for their delivery.
5. Discussion
In this study, 390 science communication videos, from 21 professionally generated and 18 user-
generated YouTube channels, were examined to identify content-related factors that influenced
popularity. We identified three factors that contribute to popularity. First, although PGC is more
numerous than UGC, UGC is far more popular in the science communication genre. Therefore,
whether a channel is an overtly professionally generated channel or one that appears to be
YouTube derived (UGC) is the largest correlate of popularity. Second, whether a channel had a
regular communicator to deliver content greatly impacted video views. Third, for both PGC and
Figure 3. (a) Gender representation and (b) deliver style of professionally generated (PGC) and user-
generated (UGC) YouTube science videos.
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10 Public Understanding of Science
UGC, videos that delivered information more rapidly had more views than slow-paced videos.
Several results from this study, namely, the effect of video length on popularity and the rates of
engagement with videos, disagree with findings from prior work (Chatzopoulou et al., 2010).
Still, we make several recommendations that may increase the popularity of science communica-
tion videos on YouTube, and we identify future research directions to expand upon this work.
Despite the concerns of Kim (2012), this research highlights that user-generated science com-
munication need not fear PGC monopolising audience attention. The superior financial resources
of professionally created channels and (likely) formal technical training of PGC creators do not
lead to science communication videos or channels that are more popular with the YouTube com-
munity. This result can be explained by how content consumers identify trusted sources. Among
the key factors used by consumers to identify trusted sources of information on Web 2.0 are
communicator expertise, experience, impartiality, affinity and a source being trusted within a
content consumer’s social network (Borgatti and Cross, 2003; Heath et al., 2007). These factors
also support why communicator continuity increased video views. Making a connection with the
audience is logically more direct if there is continuity throughout a series of videos; in short, a
regular communicator adds to the authenticity of a channel (Burgess and Green, 2009). Thus, the
success of UGC can be explained by user-created channels fostering meaningful connections
with the viewer base, and the increased success of UGC with a regular science communicator
merely compounds the effect.
It is logical that the pace of content delivery needs to suit the medium of communication. To get
your message across when public speaking, instructional tips often repeat the dictum that one
should not speak too quickly or too slowly, while averaging between 100 and 150 words per minute
(Sudha, 2010). Comprehension studies, for instance, have found that students benefit from receiving
content at lower than average speaking rates (~190 words per minute; Weinstein and Griffiths,
1992). The main reason why public speakers should ensure they are not talking too quickly is
because of the transitory nature of the medium. It is not possible to replay something if it is missed.
In contrast, however, faster rates of speech are considered to improve the persuasiveness of argu-
ments and increase audience focus (Chambers, 2001; Miller et al., 1976; Smith and Shaffer, 1995).
However, these are competing outcomes. Slower rates of delivery may improve comprehension,
Figure 4. Views (natural log) of professionally generated (PGC) and user-generated (UGC) YouTube
science videos as a function of communicator continuity. Asterisks indicate a significant (p < .05) difference
between videos with a continuous host (Con.Host) and non-continuous host (Non-Con.Host).
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Welbourne and Grant 11
whereas greater rates may increase engagement and interest. In the YouTube context, comprehension
may not be affected as YouTube videos can easily be replayed as necessary. Thus, these results
support the point that higher rates of content delivery do increase views, but future research should
examine whether comprehension of the message deteriorates.
For the most part, the gender of the science communicator was not found to influence views;
however, in terms of representation, science communicators, especially in UGC, were often male.
Jenkins et al. (2009) define a participatory culture as one with relatively low barriers to entry,
where people are supported and encouraged to create and share content and where participants
feel a degree of social cohesion with other participants. YouTube is therefore often described as a
participatory culture, and we would nominally expect that creators represent the demographics of
the community (Chau, 2010). While it appears that YouTube has relatively the same amount of
male and female viewers (Chau, 2010), Abisheva et al. (2013) identified clustering in different
subjects on YouTube; for example, sports had more male viewers while entertainment had more
female viewers. Thus, the lack of female science communicators may be symptomatic of a lack of
female viewers. Alternatively, female science communicators simply may choose not to make
content. Molyneaux and O’Donnell (2008) in fact identified that females did create and consume
fewer vlogs than males, despite having the same technical skills and feeling just as much a part of
the YouTube community as their male counterparts. To explore the gender gap in the creation of
science communication content, future research should explore qualitative approaches.
Two findings in this study conflict with prior research: video length and engagement
rates. First, longer videos intuitively seem that they would be less popular than shorter videos
(Davenport and Beck, 2001), a point expressed by content creators and even YouTube (n.d.).
This study does not support this claim. Content creators, however, should not assume any video
length is appropriate; further research on video length of YouTube science videos is needed, and
we recommend that this should occur on few channels with variability in video length to control
for channel effects. Second, we found that for the same number of views, UGC would receive
more ratings than PGC. In contrast Chatzopoulou et al. (2010) found that videos with higher
views had relatively fewer ratings, comments and favourites. Their explanation was that videos
with more views elicit a ‘less acute reaction’ (Chatzopoulou et al., 2010: 2). This hypothesis might
explain why we found that PGC was shared more than UGC. Our contrary finding of relatively
higher ratings may simply be an idiosyncrasy of science communication; nevertheless, it alludes
to how UGC becomes more popular. Given ratings were received significantly more than other
engagement metrics, given UGC received significantly more ratings, and given YouTube’s video
recommendation systems incorporate such engagement metrics, UGC may become more popu-
lar by simply being recommended more often.
With the abundance of information in the modern era, understanding how to capture audience
attention is paramount to having one’s message heard. On YouTube specifically, long-term success
requires understanding what factors contribute to the growth of video and channel popularity
(Burgess and Green, 2009). It is important to recognise that analysis in this study was correlative,
and causation cannot necessarily be inferred from these results. Still, this study highlights several
factors that appear to contribute to popularity. Science communicators on YouTube need to have a
face and they must engage with the community. The biggest mistake that content creators can make
is in viewing YouTube as merely a video hosting platform, rather than a participatory community.
As this study describes some of the characteristics of science communication on YouTube, it
provides a foundation for future research. We urge continued research of science communication
on YouTube as we cannot assume that broad YouTube trends identified elsewhere apply to the science
communication genre.
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12 Public Understanding of Science
Thanks go to Merryn McKinnon and Vanessa Hill who provided feedback on the original manuscript and to
the two anonymous reviewers who reviewed the submitted manuscript.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit
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Author biographies
Dustin J. Welbourne is a PhD candidate at the University of New South Wales in the School of Physical,
Environmental and Mathematical Sciences. His research interests include evolutionary biogeography and
how these topics are communicated in society.
Will J. Grant is a researcher/lecturer at the Australian National Centre for the Public Awareness of Science,
Australian National University. His research and teaching have focused on the intersection of science, society
and technology.
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    ... Based on these users' activities such as "like", "comment", and "share", YouTube accordingly presents popular video content on its website or mobile app. Researchers have examined factors affecting the popularity of YouTube's video content [16]. In these studies, the popularity of YouTube's video content was measured by the number of views, comments, subscriptions, shares, and total number of ratings [16]. ...
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  • ... for Twitter, the single, time-based newsfeed that displays all posts does not. Research into public engagement with science via social media is occurring in multiple contexts, including YouTube (Welbourne and Grant 2016), Twitter (Daume and Galaz 2016), and Facebook (Fauville et al. 2015), yet these empirical studies do not necessarily capture the ways in which social media can be used for educative purposes. ...
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