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Applied Behavior Analysis as Treatment for Autism Spectrum Disorders: Topic Modeling and Linguistic Analysis of Reddit Posts



Background: It is critical for professionals to understand the discourse landscape within various online and social media outlets in order to support families of children with autism in treatment decision-making. This need is heightened when considering treatments that have garnered excitement and controversy, such as applied behavioral analysis (ABA) therapy. Method: The specific aims of this study were to identify the main themes in Reddit posts about ABA-based interventions for autism using topic modeling, to examine the linguistic aspects of Reddit conversations using the Linguistic Inquiry and Word Count (LIWC) analysis, and to examine the relationship between linguistic aspects and user category (i.e., pro- vs. anti-ABA vs. undecided, parent vs. professional vs. an individual with autism). Results: The topic modeling resulted in 11 themes that ranged across various elements, such as autism as a condition and its management, stakeholders, and consequences of autism and the support needed. The posts of individuals were focused on personal experiences and opinions as opposed to clinical and research information sharing. Linguistic analysis indicated that the posts reveal an intimate stance rather than an empirical stance. Conclusions: Results provide insight into perspectives of ABA. This type of research may help in developing and distributing appropriate and evidence-based information.
published: 05 January 2022
doi: 10.3389/fresc.2021.682533
Frontiers in Rehabilitation Sciences | 1January 2022 | Volume 2 | Article 682533
Edited by:
Gloria K. Lee,
Michigan State University,
United States
Reviewed by:
Dan Cai,
Shanghai Normal University, China
Ana Duenas,
Lehigh University, United States
Monica L. Bellon-Harn
Specialty section:
This article was submitted to
Translational Research in
a section of the journal
Frontiers in Rehabilitation Sciences
Received: 18 March 2021
Accepted: 03 December 2021
Published: 05 January 2022
Bellon-Harn ML, Boyd RL and
Manchaiah V (2022) Applied Behavior
Analysis as Treatment for Autism
Spectrum Disorders: Topic Modeling
and Linguistic Analysis of Reddit
Posts. Front. Rehabilit. Sci. 2:682533.
doi: 10.3389/fresc.2021.682533
Applied Behavior Analysis as
Treatment for Autism Spectrum
Disorders: Topic Modeling and
Linguistic Analysis of Reddit Posts
Monica L. Bellon-Harn 1,2,3
*, Ryan L. Boyd 4,5,6 and Vinaya Manchaiah 1, 2,3,7, 8
1Department of Communication Sciences and Disorders, Piedmont University, Demorest, GA, United States, 2Virtual
Hearing Lab, Collaborative Initiative Between Lamar University and University of Pretoria, Beaumont, TX, United States,
3Department of Speech and Hearing, School of Allied Health Sciences, Manipal, India, 4Department of Psychology,
Lancaster University, Lancaster, United Kingdom, 5Security Lancaster, Lancaster University, Lancaster, United Kingdom,
6Data Science Institute, Lancaster University, Lancaster, United Kingdom, 7Department of Speech-Language Pathology and
Audiology, University of Pretoria, Pretoria, South Africa, 8Department of Speech and Hearing Sciences, Lamar University,
Beaumont, TX, United States
Background: It is critical for professionals to understand the discourse landscape within
various online and social media outlets in order to support families of children with autism
in treatment decision-making. This need is heightened when considering treatments
that have garnered excitement and controversy, such as applied behavioral analysis
(ABA) therapy.
Method: The specific aims of this study were to identify the main themes in Reddit
posts about ABA-based interventions for autism using topic modeling, to examine the
linguistic aspects of Reddit conversations using the Linguistic Inquiry and Word Count
(LIWC) analysis, and to examine the relationship between linguistic aspects and user
category (i.e., pro- vs. anti-ABA vs. undecided, parent vs. professional vs. an individual
with autism).
Results: The topic modeling resulted in 11 themes that ranged across various elements,
such as autism as a condition and its management, stakeholders, and consequences
of autism and the support needed. The posts of individuals were focused on personal
experiences and opinions as opposed to clinical and research information sharing.
Linguistic analysis indicated that the posts reveal an intimate stance rather than an
empirical stance.
Conclusions: Results provide insight into perspectives of ABA. This type of research
may help in developing and distributing appropriate and evidence-based information.
Keywords: autism spectrum disorder, Reddit, applied behavioral analysis, health management, topic modeling
Families of children with autism spectrum disorder (ASD) must decide among varied types of
management and intervention options to address symptoms associated with ASD, such as severe
and sustained impairment in communication and social interaction and restricted patterns of
ritualistic and stereotyped behaviors (1). Some children with ASD also exhibit difficulty in adaptive
Bellon-Harn et al. ABA Reddit
behaviors, psychiatric symptoms, and intellectual disability (2,3).
Families often turn to online information and other social media
platforms for treatment decision-making guidance. However,
information for families with children with ASD is frequently
confusing and unreliable (4,5). Further, social media platforms
serve different functions for different stakeholders associated
with ASD, which can influence the content and purpose of
information. For example, Bellon-Harn et al. (6) reported that
a number of Twitter users posting ASD-related tweets were
associated with advocacy communities as compared to clinical
and research communities. It is critical for professionals to
understand the discourse landscape within various online and
social media outlets in order to support families in treatment
decision-making. This need is heightened when considering
treatments that have garnered excitement and controversy,
such as interventions based on principles of applied behavioral
analysis (ABA) (7).
ABA-Based Interventions
Applied behavioral analysis is science on which ABA-based
interventions have been developed. ABA is derived from tenants
of behaviorism, experimental analysis of behavior, and applied
research, and its methods can be applied to a variety of
intervention approaches for children with ASD (8). Evidence-
based research is emerging; however, the consensus from
meta-analysis studies is that more research is necessary to
understand the efficacy and effectiveness associated with ABA-
based intervention (911). More evidence may also clarify
misinformation and diminish misuse surrounding the practice of
ABA-based interventions (8,12,13).
In light of potential misconceptions about ABA, it is valuable
to understand the content of information that is shared
online and the sentiment of the content. Since individuals
with ASD, family members, and other stakeholders utilize
online communities (14), research examining online content
provides an opportunity to learn about the experiences and
voices of adults with ASD and other members of neurodiverse
communities (15,16). This may provide valuable information
in understanding factors linked to decision-making related to
ABA-based intervention. In turn, this may facilitate the ability
of healthcare professionals to provide guidance to families
on making informed choices based on evidence with a clear
understanding of the benefits and limitations of their options
(17). This study is an initial step to understand the discourse
landscape surrounding ABA-based interventions for children
with ASD within a social media platform. Specifically, we used
topic modeling and linguistic analysis methods to examine ABA-
related posts in Reddit.
Reddit and ASD
Reddit ( is a social network that has
many elements common in other popular social media sites (e.g.,
Facebook and Twitter), such as the ability to communicate and
share information with other users, the ability to follow users and
groups, and the ability to create one’s own information. However,
it is distinguished because Reddit’s content is accessible to anyone
with or without an account, and people can have “throwaway”
accounts (i.e., temporary identities). Most Reddit users subscribe
to more subreddits, which are defined as a smaller community of
posters within a broader community of posters.
Some explorations of content and linguistic attributes within
social media platforms, such as Reddit, have occurred. Types
of analysis to examine large corpora of data include and topic
modeling Linguistic Inquiry and Word Count (LIWC). Topic
modeling is a technique that involves text-mining algorithms to
identify patterns within the data (18). This method examines
how words cluster together in their use. LIWC is an automatic
text analysis program that counts and calculates the percentage
of words in the text that match various emotional, cognitive,
structural, and process dimensions. The LIWC program includes
a main text analysis module, along with a group of built-in
dictionaries. The text analysis module compares each word in the
text against a user-defined dictionary (19).
Some analyses of Reddit corpora within the area of ASD are
completed. For example, Thin et al. (20) examined conversational
involvement, emotion, and informational support in a subreddit
r/Aspergers using cluster analysis. Results indicate that the ASD
subreddit was a supportive community. Saha and Agarwal (21)
examined the social support of popular ASD bloggers active
in blogs and Twitter LIWC analysis (19). Results indicate that
the ASD community provides significant social support to its
members both on Twitter and blogs. Bellon-Harn et al. (6)
examined patterns and themes of ASD-related tweet content on
Twitter. The authors reported that the language appears to be
associated with a more guarded, distanced form of discourse
rather than a personal form of discourse. The authors suggested
the length of the tweet does not allow room for more personal
forms of discourse, which may require more space to articulate
the depth of thought.
Summary and Study Purpose
This paper seeks to contribute to information centered on
understanding the role of social media within the area of ASD.
The specific aims include (a) to identify the main themes in
online discussions around ABA-based interventions for ASD
using topic modeling, (b) to examine the linguistic aspects of
conversations using the LIWC analysis, and (c) to examine the
relationship between linguistic aspects and user category (i.e.,
pro- vs. anti-ABA vs. undecided, parent vs. professional vs. an
individual with ASD).
Study Design and Ethical Considerations
The study used a cross-sectional design. Conversations about
ABA in relation to ASD were extracted from Reddit. No ethical
approval is required as the data were anonymous, and no
personally identifiable information was included (22). This was
an analysis of public data, and the authors were careful to ensure
analyses did not compromise user identity.
Data Extraction
The data for this study consist of original posts (i.e., a submission
that starts a conversation) and associated comments (i.e., a
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Bellon-Harn et al. ABA Reddit
submission that replies to posts or other comments) from several
topical focused subreddits (i.e., subcommunities). Two sets of
data (i.e., discussions about ABA for autism and Reddit baseline
data) were extracted via the Reddit application programming
interface (API) using a custom-built script. The approach to
data extraction was to collect the entire thread history. Where
possible, all original post-level information was retained. In cases
where comments remained but user-level information had been
removed, all data were retained that was still available. This
Reddit API is publicly accessible and allows researchers to acquire
language data directly from the site without using the typical web
interface. Reddit does not collect thorough demographic data on
the users of the site, so we cannot describe the characteristics of
the sample. The data posted from the time Reddit started through
March 2020 were extracted chronologically.
To identify the relevant threads containing posts about ABA
in relation to ASD, a search was performed in Reddit using
the keywords “Applied Behavior Analysis,” “ABA Therapy,
“Autism,” or “Autism Spectrum Disorder.” These keywords were
compiled based on consensus between researchers following
searches in Reddit and Google trends (,
a website that analyzes the popularity of search terms and uses
graphs to compare the search volume of the terms over time. The
search was sorted by relevance from all time, and the threads
that had a focus on ABA were included. Although the data
were extracted from 19 subreddit threads, most of the data were
generated from a few threads, such as r/autism (62%), r/aspergers
(13%), r/BehaviorAnalysis (6%), r/ABA (3.5%), r/Parenting
(3%), r/unpopularopinion (2.7%), and r/IAmA (2.3%). A total
of 2,432 posts were extracted. However, 112 posts were not
relevant to ABA and were fewer than five words. As such, they
were excluded. The remaining 2,320 posts were included for
further analysis.
For the purpose of linguistic analysis, another dataset with
baseline Reddit data was generated. For LIWC, the software
provides output (results) on the percent occurrence for each
of the psychologically meaningful dimensions. However, we do
not know if this percentage is appropriate unless it is compared
to a standard or baseline. We decided the best procedure was
to examine Reddit data related to ABA-based intervention in
comparison to other general Reddit conversations with data.
Consequently, baseline data were generated. A subsample of 0.1%
was extracted randomly from r/AskReddit, which resulted in a
sample of 357,795 posts. Of these, 84,215 posts that had five
words or less were excluded, and the remaining 273,580 posts
formulated the baseline data corpus.
Data Analysis
Category Determination
All posts were coded according to the view toward ABA and
the personal identification of their status. Preliminary coding of
the initially posted 100 posts provided the codes for whether or
not the post (1) included support of ABA (i.e., pro-ABA); (2)
include support ABA (anti-ABA); (3) was seeking information
about ABA (i.e., neutral/curious); or (4) was not directly related
to ABA (i.e., unclassified). Unclassified posts included posts
giving feedback about what is or is not appropriate to post,
another related ASD issue (e.g., diagnosis), or commenting on
the relative value of a post. Following cyclical review by the first
author and two graduate students in speech-language pathology,
codes were developed. Pro-ABA codes were defined as posts that
described ABA as beneficial and/or included a positive impact
of ABA. Anti-ABA codes were defined as posts that described
ABA as not beneficial and/or included a negative impact of ABA.
Neutral/curious codes were seeking information about ABA or
wanted to understand characteristics of ABA. Unclassified posts
did not relate to ABA even though they were related to some
aspect of autism causes, characteristics, or treatment.
Additionally, the posts were coded according to their personal
identification of their status as a person with ASD, a parent of a
child with ASD, a professional, or other. In order to be coded,
the post explicitly stated their status (e.g., as an autistic adult).
Upon review and discussion by the three coders, one graduate
student completed coding the complete data set of 2,320 posts.
Following each set of 100 posts, the sample was sent to the first
author for review, consensus, and to resolve queries until all 2,320
posts were reviewed.
Topic Modeling
In this study, topic modeling was performed on all 2,320
posts using the Leximancer software (edition 4.0) (https://info. to identify the main themes, concepts, and
their relationships within the posts. The use of Leximancer to
derive semantic content and relationships from natural language,
in this case written discourse, has been validated (23). This
method uses a suite of algorithms to identify themes, concepts,
and relationships resulting in an output that includes graphic
summaries. The process of topic modeling involves (1) concept
identification in which single, frequently occurring words are
determined; (2) concept definition in which a group of words that
form a concept is compiled; and (3) text classification in which
the concepts that were identified and defined are analyzed for
frequency of occurrence (18). Based on the output, insights into
the nature of a particular discourse topic can be drawn (24).
The LIWC software program (
was used to analyze linguistic aspects of the text data. In the
current study, the research team identified 10 key linguistic
variables, which were included for further analysis using LIWC.
All texts with fewer than five words were excluded to prevent
skew [see (25)]. For example, a post with a single word
“Wonderful!” may result in a positive emotion score of 100%,
which is not in line with the typical percentage (4–5%) for
this category. Such a cutoff is a common convention when
performing LIWC (25). The LIWC has high internal reliability
and external validity and is validated across thousands of studies
Statistical Analysis
SPSS software was used for statistical analyses. The assumptions
of normality and the assumption of homogeneity of variance
were tested using the Shapiro-Wilks test and Levene’s test,
respectively. As the data met these assumptions, parametric
statistics were selected. A one-sample t-test was performed to
compare the linguistic variable results with the baseline Reddit
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Bellon-Harn et al. ABA Reddit
TABLE 1 | Reddit user categories.
User categories N(2,320) %
View toward ABA
Anti ABA
Undecided (or curious)
Other (unable to
Personal status
• Parent
• Professional
Individual with ASD
• Other
data. One-way ANOVA was used to test for differences in
language use between user categories. A p-value of 0.05 was used
for statistical significance interpretations.
Review Characteristics
Of the 2,320 Reddit posts, 2,140 came from unique
users. Of these, 75 were original posts; the remaining
2,245 were comments. For the original posts, the median
up-vote ratio was 0.9, and the median ups were seven
suggesting that these posts were quite popular on the
Reddit platforms. Table 1 shows the user categories of
these posts based on the view of users toward ABA and
their relationship.
Topic Modeling: Themes and Concepts
The concept map, generated from the topic modeling analysis
of all Reddit posts, is presented in Figure 1 provides a birds-
eye perspective of the data showing the themes (i.e., bubbles),
main concepts (i.e., dots in bubbles), their frequencies, and their
interconnectedness. This concept map may be interpreted as
users seeking or providing discourse on a specific issue. The
concept map suggests that there is limited or no overlap between
concepts. On the other hand, there is some overlap with some
themes (e.g., work, time, and need), which is expected as they are
interconnected. The topic modeling resulted in 11 themes that
ranged across various elements, such as autism as a condition
and its management (i.e., autism and ABA), stakeholders (i.e.,
people, adults, and therapists), consequences of ASD, and the
support needed (i.e., work, need, school, change, and abuse),
suggesting that the discourse around ABA in Reddit is diverse.
Table 2 presents the 11 main themes, concepts, frequencies,
and examples of meaning units based on the topic modeling.
Here, the terms “theme” and “concept” in topic modeling refer
to “category” and “sub-category,” respectively, in qualitative
content analysis.
ABA: This theme included discussions about definitions,
potential, benefits, limitations, and personal experiences.
Concepts, such as therapy, behavior, use, and children, were
tied together in this theme.
FIGURE 1 | Concept map of open-ended text response using Leximancer
Work: This theme included discussions related to work
conducted within healthcare professions or by ABA therapists.
Discussions related to whether or not ABA “worked” were
included in these discussions. Concepts of kids, child, parents,
and social were connected to this theme.
People: Concepts included “autistic,” “person,” and “different.”
Discussions in this theme centered around the value of people
with ASD and a call for neurodiversity.
Need: The theme refers to whether or not ABA is needed and
how much treatment is needed.
Autism: These discussions centered on understanding the
nature of autism and the experiences of people associated
with autism. Associated concepts included “understand”
and “look.”
Time: This theme related to how much time was required for
change to occur as a consequence of ABA.
Adults: Discussions in this theme centered around the value
of people with ASD and a call for neurodiversity.
Therapist: This theme refers to the role and certification of
ABA therapists and their relationship to other professionals.
School: The theme is related to the ability of parents to
obtain ABA-based intervention in a school setting and to the
education required by ABA therapists.
Change: This refers to both change in behavior or
performance and plan or processes associated with
ABA intervention.
Abuse: This theme is associated with perceptions of ABA
intervention as abusive and creating long-term trauma in
individuals who receive ABA intervention.
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Bellon-Harn et al. ABA Reddit
TABLE 2 | Main themes and concepts in discussions around ABA.
Theme Concepts Frequency (hits) Examples
ABA Aba, therapy, behavior, use,
2,180 I think you’re talking about “behavior modification” which is not the same as “applied
behavior analysis.” Behavior Modification was used in Gay Conversion “therapy” and
Behaviorism is a broad field of psychology that can be applied to training dogs, but
ABA is just the applied science of behaviorism, used to effectively treat children with
learning disorders:) it’s touted as the most effective because it has the most research
to back it.
There’s also a lot of research that ABA is very effective for treating ASD. It’s often
used school-wide (positive behavior supports) because it’s effective for all children.
Work Work, kids, child, parents,
1,599 Also, if you claim to work in a hospital, Drs and employees won’t tell the patient’s
parents in their face what level the patient is, but look the medical records of
comorbidities. Did you say yoga? By socialization you mean a therapist goes with
your child to places right?
This ABA thing sounded nothing more than making parents gain some false “social
imagination” nugget to convince themselves that their kid will get along with other
kids better, at the expense of the kid’s overall health.
People People, autistic, person,
1,530 That means that autistic people differ a lot between themselves. So even though
there are lots of tragic stories online there are also lots of autistic people in the world
who are thriving.
You are assuming autistic people are like neurotypical people. They are not. We
literally have differently wired brains.
Need Need, things, trying, feel 1,235 I am terrified he may grow up and not be able to express if he is sick or describe
how he is sick. These are the kind of reasons I feel like I need to try.
They feel set up to fail- and become even more dysregulated, because try as they
might in that moment- there’s nothing they can do to earn that token/sticker/what
have you. Working with dysregulation needs to have strategies to recognize when
dysregulation is beginning or may occur, and proactively learning how to remove
oneself from those situations.
Autism Autism, understand, look 892 This is a deep problem in understanding autism. It is, it seems, one of these things
that bother normals because it is so fundamental to the human experience to look
at the eyes and the eye region.
As I got older, this started looking more like my autism was her story to tell, not my
own. She’d talk about how she was one of my few advocates when I was young
and that she basically had to teach me everything, including how to imagine since
my brain wasn’t wired for that at first.
Time Time, doing 638 I typically do 2–4h sessions but I’ve gone as long as 11 h a day (although we usually
spend a lot of that time doing fun stuff).
He’s getting clearer and talking much more all the time. He’s receiving speech
therapy and doing great.
Adults Adults, experience 408 A quick overview suggests that some number of adult autistics didn’t have a good
experience with ABA therapy.
I don’t want to doubt anyone’s experience, but plenty of adults who didn’t receive
ABA seem to associate with this CPTSD label. I think this is just what therapists
choose to call autistic style anxiety rather than real PTSD.
Therapist Therapist 288 Multiple supervisors and other therapists quit before me.
I never claimed to be a BCBA, licensed to diagnosed, or anything else I am not. I
am a therapist who utilizes ABA to get the most out of my clients.
School School 200 You provide what we call “behavior assistant.” It’s a direct-care position that requires
a high school diploma and a 20 h course that covers the very basics of ABA and
how to implement some basic procedures.
As for ABA, I wonder if there are schools like that in my area. Out of curiosity, how
did you find this school?
Change Change 190 I will then re-evaluate what is going on and make changes if they are necessary. My
company provides behavior assistant services as well, and if any of them were to
change anything in the plan without my consent they would be fired on the spot.
Unfortunately mom changed his meds on him, he went right back to where he
was, and had to be placed in a residential hospital. Very sad, I really miss him.
Abuse Abuse 180 Actually, I do believe it would fall within the definition of emotional abuse. Hitting is
not the only form of abuse.
My hot take is that “not growing eyebrows” is an allegory for developmental
disorders or disabilities or neurodivergence that is then preyed upon by
abusive “You’s.”
ABA, applied behavioral analysis.
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TABLE 3 | Key linguistic analyses variables for Reddit ABA conversations and the baseline Reddit posts.
Dimension and its description Baseline Reddit:
Mean (SD)
ABA Reddit: Mean
T-test results:
t-value, p-value
User engagement
Word count: The number of words that a person
used to provide their views and experiences.
35.2 (55.9) 111.4 (155.8) 23.5, <0.001
Authenticity: The degree to which a post invokes
spontaneity and casualness.
47.4 (38.2) 39.8 (32.5) 11.3, <0.0001
I-words: The degree to which a post invokes an
anecdotal or self-referential manner.
5.15 (6.2) 4.3 (4.6) 8.9, <0.0001
Social and emotional dimensions
Social processes: The degree to which a post
invokes a social dimension, such as friends or
10 (9.2) 12.3 (7.4) 14.9, <0.0001
Positive emotions: The degree to which a post
expresses positive emotions.
3.7 (5.7) 4.2 (6.9) 3.7, <0.0001
Negative emotions: The degree to which a post
expresses negative emotions.
2.8 (4.9) 2.4 (3.5) 4.9, <0.0001
Biological dimension
Health: The degree to which a post includes
health concepts.
0.68 (0.24) 1.2 (2.2) 12.3, <0.0001
Personal concerns
Work: The degree to which a post includes work
1.8 (3.9) 2.9 (3.3) 15.8, <0.0001
Home: The degree to which a post includes
home or home life concepts.
0.38 (1.7) 0.19 (0.6) 14.1, <0.0001
Money: The degree to which a post includes
money concepts.
0.74 (2.6) 0.32 (2.3) 8.7, <0.0001
Mean, SD, and the t-test comparisons are reported. ABA, applied behavioral analysis.
LIWC Analysis
Table 3 presents the mean, SDs, and t-test results for 10
key linguistic variables used in the LIWC analysis across
four dimensions in ABA-related and baseline Reddit posts.
Means reflect the degree to which posts reflect a certain
psychological dimension. There was a statistically significant
difference between the ABA posts and baseline posts in all of the
10 key variables. The ABA posts had a higher number of words
per post. The mean values for authenticity and I-word were
higher for baseline Reddit posts. ABA Reddit posts had higher
references to others (i.e., social processes) and positive emotions,
but less negative emotions when compared to baseline Reddit
posts. In addition, ABA posts in Reddit had higher references to
health and work, but lower references to home and money when
compared to baseline posts.
LIWC Analyses Across User Categories
User Categories Based on Perspectives Toward ABA
ANOVAs were performed to examine the difference in linguistic
variables across user categories. The pro- and anti-ABA
posts included a higher word count than posts from the
undecided/curious group (see Table 4). Group differences were
noted on the positive and negative emotion word measures.
Pro-ABA and anti-ABA posts included more positive emotion
words than the undecided/curious group. The anti-ABA posts
included more words weighted with negative emotion than the
pro-ABA and undecided/curious posts. Group differences were
noted on measures of work, home, and money-related words. The
pro-ABA and undecided/curious posts included work-related
words with greater frequency than the anti-ABA group. The pro-
ABA posts included more words about home life than the other
groups. The undecided/curious posts included more money-
related words than the other groups. No group differences were
noted on measures of authenticity, I-words, social processes,
or health.
User Categories Based on Personal Status
ANOVA results suggest that the ASD and professional posts
included a higher word count than posts from the parent
group (see Table 5). Group differences were noted on the
dimension authenticity. The ASD and professional posts used
more words weighted with authenticity than the parent group.
Group differences were noted on the use of I-words. The
ASD posts included more I-words than the other groups.
Group differences were noted in social processes. The ASD and
professional posts included more words weighted along the social
processes dimension than the parent group. Group differences
were not noted on the positive emotion dimension but were
noted on negative emotion. ASD posts included more negative
emotion words than the other groups. Group differences were
noted on measures of health and work, but not home and
money-related words. Posts from the parent group used more
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TABLE 4 | LIWC across user categories based on view toward ABA.
Dimension Mean ANOVA Pairwise comparisons (p-value)
Pro ABA Anti ABA Undecided Pro vs. anti Pro vs. undecided Anti vs. undecided
Word count 158.9 161.8 119.6 F=3.7, p=0.024 ns ns 0.02
Authenticity 39.9 38.9 43.4 F=1.2, p=0.19
I-words 4.2 3.9 4.2 F=0.7, p=0.48
Social processes 11.4 11.4 11.0 F=0.4, p=0.67
Positive emotions 3.4 3.1 2.8 F=4.7, p=0.009 ns 0.015 ns
Negative emotions 1.9 2.9 1.8 F=26.1, p<0.001 <0.001 ns <0.001
Health 1.3 1.5 1.4 F=2.1, p=0.12
Work 3.9 2.7 3.5 F=20.3, p<0.001 <0.001 0.004
Home 0.24 0.13 0.17 F=5.5, p=0.005 0.003 ns ns
Money 0.33 0.24 0.48 F=4.05, p=0.018 ns ns 0.01
ABA, applied behavioral analysis; LIWC, Linguistic Inquiry and Word Count.
TABLE 5 | LIWC across user categories based on personal status.
Dimension Mean ANOVA Pairwise comparisons (p-value)
Parents Professionals ASD Parent vs. professional Parent vs. ASD Professional vs. ASD
Word count 139.2 176.9 189.6 F=6.3, p=0.002 0.04 0.002 ns
Authenticity 38.1 46.4 50.8 F=15.9, p<0.001 0.001 <0.001 ns
I-words 5.2 5.1 6.6 F=13.3, p<0.001 ns <0.001 <0.001
Social processes 14.4 10.4 10.8 F=69.3, p<0.001 <0.001 <0.001 ns
Positive emotions 3.6 3.7 3.4 F=0.5, p=0.6
Negative emotions 1.7 1.9 2.7 F=23.2, p<0.001 ns <0.001 <0.001
Health 1.6 1.1 1.3 F=9.2, p<0.001 <0.001 0.023 ns
Work 2.7 4.9 2.4 F=86.3, p<0.001 <0.001 ns 0.02
Home 0.27 0.27 0.2 F=1.1, p=0.32
Money 0.22 0.32 0.25 F=1.7, p=0.18
LIWC, Linguistic Inquiry and Word Count.
health-related words. Posts from the professional groups used
more work-related words.
This study serves as an initial exploration of discourse among
ABA-related posts in Reddit. This paper identified the main
themes in online discussions around ABA-based interventions
for ASD, examined the linguistic aspects of conversations,
and examined the relationship between linguistic aspects and
user category. The following highlights the main findings
and implications.
Discourse Themes and Concepts
The most frequent theme (i.e., ABA) is not unexpected since posts
included in this sample were based on this topic. The themes
work,therapists, and school were identified at varying levels of
frequency, but taken together, these themes indicate that in this
sample, posters perceive the work of the ABA therapist to be
an important conversation. Alternatively, the theme work also
refers to whether or not ABA-based interventions are effective.
A question guiding treatment decisions includes whether or not
the treatment is needed and how much treatment is needed (27).
Work along with the themes need,time, and change point toward
an emphasis on the effects of the treatment. Work was also linked
with the concept “social.” As noted by Matson et al. (28), a critical
question in the literature is whether ABA can be used to modify
negative behavior and social skills.
Notably, discussions related to the value of neurodiversity
occurred with high frequency (i.e., people,adult themes). These
posts focused on the need to understand the experience of people
with ASD and not diminish the unique contributions of people
with ASD. These discussions align with the theme of abuse,
which is of critical importance to all stakeholders involved in
working with people with ASD (12,29). These posts highlighted
concerns that ABA-based intervention has negative long-term
consequences on people with ASD. Overall, the themes and
concepts suggest a digital landscape that focuses on the effects of
ABA-based intervention. Interestingly, the posts did not include
themes related to research or evidence-based practice.
Frontiers in Rehabilitation Sciences | 7January 2022 | Volume 2 | Article 682533
Bellon-Harn et al. ABA Reddit
Linguistic Aspects of Conversations
Overall, the comparison of ABA posts and baseline Reddit posts
suggests that the language used to discuss ABA is different
than the language used in general posts within the Reddit
platform. The postings of individuals were focused on personal
experiences and opinions as opposed to clinical and research
information sharing, which is further represented in the LIWC
analysis in that the posts reveal an intimate stance rather than
an empirical stance. For example, the high word count in ABA
posts is suggestive of high engagement in the topic and complex
personal views and experiences. Additionally, posts classified
as “authentic” and personal pronouns (i.e., I-words) refer to
the individualized experiences rather than broad information
sharing. It should be noted that baseline posts had higher
means than ABA posts, suggesting that broadly the use of
Reddit focuses on personal experiences and/or opinion and
may be motivated to signal their position, gain support, or
offer support.
It is not surprising that posts were strong in social connections
since posts were directed toward intervention, which necessarily
includes close personal connections. Emotional responses relate
to how people are reacting to a given topic, the degree of
immersion in a topic, and the level of agreement about a topic
(26). In this sample, positive emotions were weighted more than
negative emotions and more than the baseline Reddit posts.
Positive emotions may suggest user engagement and alignment
with a particular ABA-related topic or the use of civil, polite,
and friendly language. The posts with high positive emotion
scores included both alignment and amiable language (e.g.,
Haha, thanks. It has kind of become my job now- I make videos
explaining (autism-related) stuff to people and Nice! I will look
into this. Thank you. Nice to connect with you). This relationship
between emotional stance, agreement, and immersion is further
supported by the word count in that a higher word count is
related to higher engagement.
With regard to personal concerns, we examined the concepts
of work, home, and money. Words related to work add to the
interpretation that the posts were focused on the ABA therapist
profession or how ABA worked. It is surprising that more weight
was not associated with the sentiments home and money in light
of concerns related to insurance coverage related to ABA-based
intervention and the impact of ABA-based intervention in the
home (30).
Relationships Between Linguistic Aspects
and User Category
Comparison Across Views of ABA
Pro- and anti-ABA groups had more word count and positive
emotion than the undecided/curious group, suggesting the
individuals who had defined positions were more entrenched
in the topic. The anti-ABA group had more words weighted
with negative emotion than the pro-ABA and undecided/curious
groups. The use of negative emotion words is noted within
writing about negative or traumatic events (31,32). The anti-ABA
posts may be more likely to include personal negative experiences
linked to ABA.
Comparison Across Personal Status
Higher word count and use of words along the authenticity
dimension in the ASD and professional posts suggest high
engagement (i.e., spontaneous talk by making references to
self) in the topic as demonstrated through expressing complex
perspectives. It may be that parents were more likely to be seeking
information related to ABA-based interventions rather than
expressing a viewpoint. Additionally, the ASD and professional
post use of social process words indicate a sense of connection
and relationship with a group. There may be a more defined sense
of identify associated within these two groups than may be found
in parent groups.
The ASD group included posts with more I-words and
negative emotion words than the other groups. As noted by
Kapp et al. (15) and McGill and Robinson (16), adults with ASD
often report negative experiences associated with ABA-based
intervention. Taken together, it may be that individuals with ASD
were more likely to express psychological states related to their
experiences and perspectives.
Implications for Practice
Understanding the nature of information shared online may
help healthcare professionals support families in evidence-
based decision-making. These data illustrate that much of the
information shared centers on personal information and/or
opinion. Posts include diverse topics, such as benefits and the
limitation of ABA-based intervention, call for neurodiversity,
and the role of the ABA therapist. Engaging in conversation
with families, asking questions, and opening the dialogue
around these topics may be helpful in understanding their
stance and providing individualized guidance. Being prepared
with accessible evidence-based information may help healthcare
professionals dispel misinformation.
Strengths, Limitation, and Future
The topic modeling and linguistic analysis provided a broad
understanding of the data (i.e., landscape the discourse) rather
than specific discussions. While the automatic process has the
advantage on saving time, it is also limited in its ability to provide
in-depth analysis. For example, the theme “work” included posts
that referred to work as in “it can work” and work as a “job.” In
this context, the same word or concepts have different meanings,
which the software does not differentiate. The study also used
a word counting approach to linguistic analysis, which ignored
the context and intended audience. That said, this simple word
counting approach does provide surprisingly clear and reliable
insights into a person’s psychology (25).
It is important to note that the data may not be representative
of the general population, which is likely the case for most
social media studies. For example, Reddit users have been found
to be predominantly male and younger (under 30 years) (33).
The users are anonymous and not many details are known
about the population. Although we anticipated that the users
of this community included parents of children with ASD,
health professionals with different views toward ABA therapy,
and individuals with ASD, we could not confirm the role.
Frontiers in Rehabilitation Sciences | 8January 2022 | Volume 2 | Article 682533
Bellon-Harn et al. ABA Reddit
Additionally, we do not know the diversity of the sample with
regard to race and ethnicity. While not knowing the user
demographics is a limitation, the anonymous nature of Reddit is
likely to produce a more truthful response (or ecologically valid
data) (34). Finally, the context in which the posts occurred is
difficult to examine, which limits the interpretation of the posts.
The total number of posts on this topic is limited, which makes it
a very specialized discussion relative to the volume of discussions
occurring on Reddit.
Future studies should focus on performing more in-depth
analysis of ABA discussion to examine the specific narratives used
and the tensions among posts from these groups. Moreover, in
the current study, the key dimensions and the generic LIWC
dictionary were used for analysis, but future studies should
aim to develop and use concepts and dictionaries specific
to ASD.
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
All authors listed have made a substantial, direct, and intellectual
contribution to the work and approved it for publication.
1. American Psychological Association. Diagnostic and Statistical Manual of
Mental Disorders. 5th ed. Arlington, VA: American Psychological Association
(2013). doi: 10.1176/appi.books.9780890425596
2. Rosen TE, Spaulding CJ, Gates JA, Lerner MD. Autism severity, co-occurring
psychopathology, and intellectual functioning predict supportive school
services for youth with autism spectrum disorder. Autism. (2019) 23:1805–
16. doi: 10.1177/1362361318809690
3. Simonoff E, Kent R, Stringer D, Lord C, Briskman J, Lukito S, etal. Trajectories
in symptoms of autism and cognitive ability in autism from childhood to adult
life: findings from a longitudinal epidemiological cohort. J Am Acad Child
Adolesc Psychiatr. (2019) 11:20. doi: 10.1016/j.jaac.2019.11.020
4. Bellon-Harn M, Manchaiah V, Morris L. Autism online: a cross-sectional
study of the portrayal of autism spectrum disorders in YouTube videos.
Autism. (2020) 24:263–8. doi: 10.1177/1362361319864222
5. Reichow B, Halpern JI, Steinhoff TB, Letsinger N, Naples A, Volkmar FR.
Characteristics and quality of autism websites. J Autism Dev Disord. (2012)
42:1263–74. doi: 10.1007/s10803-011-1342-6
6. Bellon-Harn M, Ni J, Manchaiah V. Twitter usage about autism spectrum
disorder. Autism. (2020) 24:1805–16. doi: 10.1177/1362361320923173
7. Roane H, Fisher W, Carr J. Applied behavior analysis as
treatment for autism spectrum disorder. J Pediatr. (2016)
175:27–32. doi: 10.1016/j.jpeds.2016.04.023
8. Dillenburger K, Keenan M. None of the As in ABA stand for
autism: dispelling the myths. J Intellect Dev Disabil. (2009)
34:193–5. doi: 10.1080/13668250902845244
9. Nahmias AS, Pellecchia M, Stahmer AC, Mandell DS. Effectiveness of
community-based early intervention for children with autism spectrum
disorder: a meta-analysis. J Child Psychol Psychiatr. (2019) 60:1200–
9. doi: 10.1111/jcpp.13073
10. Peters-Scheffer N, Didden R, Korzilius H, Sturmey P. A meta-analytic study
on the effectiveness of comprehensive ABA-based early intervention programs
for children with autism spectrum disorders. Res Autism Spectr Disord. (2011)
5:60–9. doi: 10.1016/j.rasd.2010.03.011
11. Yu Q, Li E, Li L, Liang W. Efficacy of interventions based on applied behavior
analysis for autism spectrum disorder: a meta-analysis. Psychiatry Investig.
(2020) 17:432. doi: 10.30773/pi.2019.0229
12. Sandoval-Norton AH, Shkedy G. How much compliance
is too much compliance: is long-term ABA therapy abuse?
Cogent Psychol. (2019) 6:1641258. doi: 10.1080/23311908.2019.16
13. Trump CE, Pennington RC, Travers JC, Ringdahl JE, Whiteside
EE, Ayres KM. Applied behavior analysis in special education:
misconceptions and guidelines for use. Teaching Except Child. (2018)
50:381–93. doi: 10.1177/0040059918775020
14. Parsloe SM. Discourses of disability, narratives of community:
reclaiming an autistic identity online. J Appl Commun Res. (2015)
43:336–56. doi: 10.1080/00909882.2015.1052829
15. Kapp SK, Steward R, Crane L, Elliott D, Elphick C, Pellicano E, et al. “People
should be allowed to do what they like”: autistic adults’ views and experiences
of stimming. Autism. (2019) 23:1782–92. doi: 10.1177/13623613198
16. McGill O, Robinson A. “Recalling hidden harms”: autistic experiences of
childhood applied behavioural analysis (ABA). Adv Autism. (2020) 7:269–
82. doi: 10.1108/AIA-04-2020-0025
17. Elwyn G, Frosch G, Edwards A, Montori VM. Investing in deliberation:
a definition and classification of decision support interventions for people
facing difficult health decisions. Medical Decision Making. (2010) 30:701–
11. doi: 10.1177/0272989X10386231
18. Nunez-Mir GC, Iannone BV, Pijanowski BC, Kong N, Fei
S. Automated content analysis: addressing the big literature
challenge in ecology and evolution. Methods Ecol Evol. (2016)
7:1262–72. doi: 10.1111/2041-210X.12602
19. Pennebaker JW, Boyd RL, Jordan K, Blackburn K. The Development and
Psychometric Properties of LIWC2015. Austin: University of Texas at Austin.
T29G6Z. (2015).
20. Thin N, Hung N, Venkatesh S, Phung D. Estimating support scores of
autism communities in large-scale web information systems. In: International
Conference on Web Information Systems Engineering. Cham: Springer (2017).
p. 347–55. doi: 10.1007/978-3-319-68783-4_24
21. Saha A, Agarwal N. Modeling social support in autism community on
social media. Netw Model Anal Health Informat Bioinformat. (2016)
5:8. doi: 10.1007/s13721-016-0115-8
22. Eysenbach G, Till JE. Ethical issues in qualitative research on internet
communities. BMJ. (2001) 323:1103–5. doi: 10.1136/bmj.323.7321.
23. Smith AE, Humphreys MS. Evaluation of unsupervised semantic mapping
of natural language with Leximancer concept mapping. Behav Res Methods.
(2006) 38:262–79. doi: 10.3758/BF03192778
24. Cheng M, Edwards D. A comparative automated content analysis approach
on the review of the sharing economy discourse in tourism and
hospitality. Curr Issues Tour. (2019) 22:35–49. doi: 10.1080/13683500.2017.13
25. Boyd RL. Psychological text analysis in the digital humanities. In: Hai-
Jew S, editor, Data Analytics in Digital Humanities. Multimedia Systems
and Applications. Cham: Springer (2017). p. 7. doi: 10.1007/978-3-319-
26. Tausczik YR, Pennebaker JW. The psychological meaning of words: LIWC
and computerized text analysis methods. J Lang Soc Psychol. (2010) 29:24–
54. doi: 10.1177/0261927X09351676
27. Warren SF, Fey ME, Yoder PJ. Differential treatment intensity research: A
missing link to creating optimally effective communication interventions.
Ment Retard Dev Disabil Res Rev. (2007) 13:70–7.
28. Matson JL, Turygin NC, Beighley J, Rieske R, Tureck K, Matson
ML. Applied behavior analysis in autism spectrum disorders: recent
developments, strengths, and pitfalls. Res Autism Spectr Disord. (2012) 6:144–
50. doi: 10.1016/j.rasd.2011.03.014
Frontiers in Rehabilitation Sciences | 9January 2022 | Volume 2 | Article 682533
Bellon-Harn et al. ABA Reddit
29. Kirkham P. “The line between intervention and abuse”–
autism and applied behaviour analysis. Hist Human Sci. (2017)
30:107–26. doi: 10.1177/0952695117702571
30. Sharpe DL, Baker DL. Financial issues associated with having a child with
autism. J Fam Econ Issues. (2007) 28:247–64. doi: 10.1007/s10834-007-
31. Jones SM, Wirtz JG. How does the comforting process work? An empirical
test of an appraisal-based model of comforting. Hum Commun Res. (2006)
32. Sun J, Schwartz HA, Son Y, Kern ML, Vazire S. The language of well-being:
Tracking fluctuations in emotion experience through everyday speech. J Pers
Soc Psychol. (2020) 118:364.
33. Finlay SC. Age and gender in Reddit commenting and success. J
Inform Sci Theor Practice. (2014) 2:18–28. doi: 10.1633/JISTaP.2014.
34. Ma X, Hancock J, Naaman M. Anonymity, intimacy and self-disclosure in
social media. Proc 2016 CHI Conference Hum Fact Comput Syst. (2016)
3857–69. doi: 10.1145/2858036.2858414
Conflict of Interest: The authors declare that the research was conducted in the
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this article, or claim that may be made by its manufacturer, is not guaranteed or
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Copyright © 2022 Bellon-Harn, Boyd and Manchaiah. This is an open-access article
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Frontiers in Rehabilitation Sciences | 10 January 2022 | Volume 2 | Article 682533
... However, only a handful of studies have used social analytical tools [35][36][37][38] , especially using Twitter 39,40,41 for investigating ASD. In addition, other social networking sites such as Reddit [42][43][44][45] , Facebook 46 , Instagram 47, 48 , Flickr 49 and Sina Weibo 50 have also provided a valuable source of data for detecting and studying mental health conditions, substance abuse and risky behaviors. Using these prior works as inspiration, we curated a novel large scale Twitter dataset to study various aspects of social communication that differentiate autistic people from their neurotypical peers. ...
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The increasing usage of social media platforms has given rise to an unprecedented surge in user-generated content with millions of users sharing their thoughts, experiences, and health-related information. Because of this social media has turned out to be a useful means to study and understand public health. Twitter is one such platform that has proven to be a valuable source of such information for both public and health officials. We present a novel dataset consisting of 6,515,470 tweets collected from users self identifying with autism using "#ActuallyAutistic" and a control group. The dataset also has supporting information such as posting dates, follower count, geographical location, and interaction metrics. We illustrate the utility of the dataset through common Natural Language Processing (NLP) applications such as sentiment analysis, tweet and user classification, and topic modeling. The textual differences in social media communications can help researchers and clinicians to conduct symptomatology studies, in natural settings, by establishing effective biomarkers to distinguish an autistic individual from their typical peers. For better accessibility, reusability and new research insights, we have released the dataset publicly.
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Reddit discourse surrounding treatments for autistic individuals is easily accessible. Multiple Reddit threads discuss Applied Behavior Analysis (ABA) as one option. Such content impacts decision-making despite the absence of gatekeeping mechanisms to identify misinformation. Using a cross-sectional design, this study explores perceptions of ABA-based interventions shared on Reddit by exploring posts' content, stance and evidence used to support claims. Posts examined generally lacked support of scientific evidence. Additionally, perspectives on ABA were influenced by personal experiences with the intervention. This data provides insight to support healthcare professionals and families engaged in shared decision making regarding intervention choices.
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Objective: To systematically evaluate evidence for the use of interventions based on appied behavior analysis (ABA) to manage various symptoms of children with autism spectrum disorder (ASD). Methods: Sensitivity analyses were conducted by removing any outlying studies and subgroup analyses were performed to compare the effectiveness of ABA and early start denver model (ESDM), picture exchange communication systems (PECS) and discrete trial training (DTT). Results: 14 randomized control trials of 555 participants were included in this meta-analysis. The overall standardized mean difference was d=-0.36 (95% CI -1.31, 0.58; Z=0.75, p=0.45) for autism general symptoms, d=0.11 (95% CI -0.31, 0.54; Z=0.52, p=0.60) for socialization, d=0.30 (95% CI -0.02, 0.61; Z=1.84, p=0.07) for communication and d=-3.52 (95% CI -6.31, -0.72; Z=2.47, p=0.01) for expressive language, d=-0.04 (95% CI -0.44, 0.36; Z=0.20, p=0.84) for receptive language. Those results suggested outcomes of socialization, communication and expressive language may be promising targets for ABA-based interventions involving children with ASD. However, significant effects for the outcomes of autism general symptoms, receptive language, adaptive behavior, daily living skills, IQ, verbal IQ, nenverbal IQ, restricted and repetitive behavior, motor and cognition were not observed. Conclusion: The small number of studies included in the present study limited the ability to make inferences when comparing ABA, ESDM, PECS and DTT interventions for children with ASD.
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This article discusses the prevalence of ASD with specific regard to the most ubiquitous current treatment, Applied Behavior Analysis (ABA). A discussion of some of the issues with the underlying theory of ABA in its current application is conducted, especially with regard to “lower functioning” and nonverbal autistic individuals; namely, the curtailing of soothing “stimming” behaviors, operant conditioning, behaviorist principles that research has continued to prove it is not apt for usage with autistic individuals, as well as the unintended but damaging consequences, such as prompt dependency, psychological abuse and compliance that tend to pose high costs on former ABA students as they move into adulthood. Serious issues with the application of ABA to autistic students, specifically “lower functioning” and nonverbal ones, are discussed, especially with regard to lack of current and longitudinal scientific testing and research with respect to these individuals. These effects and the trauma that occurs resultantly are categorized as abuse. Finally, drivers of the expanded usage of ABA within the autistic community despite a lack of efficacy are also discussed, such as a potential current market size as large as $17 billion annually and the deficiency of variety in techniques used by the psychologists and behavior technicians who utilize ABA with ASD students, as well as a lack of introspection about the true effectiveness of the technique amongst the whole population on the part of these professionals.
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Supportive school services are a primary service modality for youth with autism spectrum disorder. Autism spectrum disorder, as well as co-occurring psychiatric symptoms and low intellectual abilities, interfere with academic achievement and therefore influence decisions about school services. Therefore, we examined the association of parent, teacher, and clinician ratings of autism spectrum disorder and co-occurring psychiatric symptom severity and intellectual functioning with school services. In total, 283 youth with autism spectrum disorder were assessed with clinical evaluation via the Autism Diagnostic Observation Schedule and parent and teacher versions of the CASI-4R (Child and Adolescent Symptom Inventory). Full Scale Intelligence Quotient scores were obtained from case records. Clinical and teacher evaluations of autism spectrum disorder severity predicted services and were more strongly associated with school services than parent ratings. Teacher ratings were only associated with common school services (e.g. speech/language therapy, occupational therapy, and/or social skills training) frequency at medium and high levels of clinician-rated autism spectrum disorder severity. Higher IQ and parent-rated externalizing symptoms predicted lower likelihood of receiving school services, whereas internalizing symptoms were not predictive of school services. Autism spectrum disorder symptoms may overshadow externalizing and internalizing symptoms when considering school service supports. Results highlight the importance of evaluating autism spectrum disorder severity via multiple sources, especially in cases of unclear symptom presentation, when examining correlates of school services for youth with autism spectrum disorder.
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'Stereotyped or repetitive motor movements' are characterised as core features in the diagnosis of autism, yet many autistic adults (and the neurodiversity movement) have reclaimed them as 'stimming'. Supported by a growing body of scientific research, autistic adults argue that these behaviours may serve as useful coping mechanisms, yet little research has examined stimming from the perspective of autistic adults. Through interviews and focus groups, we asked 32 autistic adults to share their perceptions and experiences of stimming, including the reasons they stim, any value doing so may hold for them and their perceptions of others' reactions to stimming. Using thematic analysis, we identified two themes: stimming as (1) a self-regulatory mechanism and (2) lacking in social acceptance, but can become accepted through understanding. Autistic adults highlighted the importance of stimming as an adaptive mechanism that helps them to soothe or communicate intense emotions or thoughts and thus objected to treatment that aims to eliminate the behaviour.
The current paper set out to investigate the long-term impacts autistic adults experienced from childhood participation in Applied Behaviour Analysis (ABA). Possible participants were recruited through advertisement on social media and through autism and ABA organisations. Possible participants were given the choice between an online or face-to-face interview or an anonymised online questionnaire. Reflections from 10 participants were indicative of a predominantly detrimental impact of ABA. Reflections gave rise to a core theme ‘Recalling Hidden Harms of Childhood Experiences of ABA’. Outcomes are discussed in relation to impact on Autistic Identity, current research and progressing understanding of the impacts of Early Intervention from the autistic perspective. The practical implications of ABA are discussed alongside recommendations for future practice and research with the involvement of autistic individuals within interventive processes. This is the first paper to take an in-depth, qualitative approach to autistic experiences of ABA. The findings themselves are driven to conceptualise and give voice to the core impacts which carried through participants exploration and understanding of self.
Stakeholders within autism spectrum disorder communities use Twitter for specific purposes. The goal of this study was to characterize patterns and themes of tweet content and sentiment and intercommunications between users sending and retweeting content to their respective user networks. The study used cross-sectional analysis of data generated from Twitter. Twitter content, sentiment, users, and community networks were examined from a sample of tweets with the highest Twitter reach and the lowest Twitter reach. Results indicate that Twitter content from both samples was primarily related to empowerment and support. Differences between the number of tweets originating from an individual in the lowest reach sample (i.e. 41%) as compared to the individuals in the highest reach sample (i.e. 18%) were noted. The number of users belonging to an advocacy subcommunity was substantially larger than a clinical and research subcommunity. Results provide insight into the presuppositions of individuals with autism spectrum disorder, their families and significant others, and other stakeholders.
Objective: For the first time, we use a longitudinal population-based autism cohort to chart the trajectories of cognition and autism symptoms from childhood to early adulthood and identify features that predict the level of function and change with development. Method: Latent growth curve models were fitted to data from the Special Needs and Autism Project cohort at three timepoints: 12, 16 and 23 years. Outcome measures were IQ and parent-reported Social Responsiveness Scale (SRS) autism symptoms. Of the 158 participants with an autism spectrum disorder at 12 years, 126 (80%) were re-assessed at 23 years. Child, family and contextual characteristics obtained at 12 years predicted intercept and slope of the trajectories. Results: Both trajectories showed considerable variability. IQ increased significantly by a mean of 7.48 points from 12 to 23 years while autism symptoms remained unchanged. In multivariate analysis, full-scale IQ was predicted by initial language level and school type (mainstream/specialist). Those with a history of early language regression showed significantly greater IQ gains. Autism symptoms were predicted by Social Communication Questionnaire scores (lifetime version) and emotional and behavioral problems. Those attending mainstream schools showed significantly fewer autism disorder symptoms at 23 than those in specialist settings; this finding was robust to propensity score analysis for confounding. Conclusion: Our findings suggest continued cognitive increments for many across the adolescent period, but a lack of improvement in autism symptoms. Our finding of school influences on autism symptoms requires replication in other cohorts and t settings before drawing any implications for mechanisms or policy.
Background: Research trials of early intervention (EI) programs for children with autism spectrum disorder (ASD) generally demonstrate medium-to-large gains, on average, compared with "treatment as usual," in different developmental domains. Almost all children with ASD receive their treatment through community-based services, however, and studies suggest that evidence-based interventions rarely make their way into community practice. Understanding the effectiveness of community-based EI and factors associated with these effects is the first step in developing strategies for wide-scale implementation of effective EI. Methods: Studies of community-based EI for children with ASD were identified through a systematic search. Changes in cognitive, communication, social, and adaptive functioning from pre-treatment to post-treatment were assessed using standardized mean gain scores. Effect sizes were estimated using random effects models. Moderators of interest included type of community EI program, year of publication, intervention duration, and sample selection. Moderator effects were assessed using analysis of variance of mixed-effects models and meta-regression analyses. Results: Forty-six groups from 33 studies met inclusion criteria (1,713 participants, mean age 37.4 months, 81.1% male). There were small but statistically significant gains in each of the four domains. Hedges's g ranged from 0.21 for adaptive behavior to 0.32 for communication outcomes, after removing outliers and correcting for publication bias. EI programs associated with universities and hospitals were superior, on average, to other community EI programs for cognitive and adaptive behavior outcomes. Intervention duration was negatively associated with effect sizes for communication and adaptive behavior outcomes. Conclusions: These results indicate that there remains a large gap between outcomes observed in community settings and those reported in efficacy trials.
The words that people use have been found to reflect stable psychological traits, but less is known about the extent to which everyday fluctuations in spoken language reflect transient psychological states. We explored within-person associations between spoken words and self-reported state emotion among 185 participants who wore the Electronically Activated Recorder (EAR; an unobtrusive audio recording device) and completed experience sampling reports of their positive and negative emotions 4 times per day for 7 days (1,579 observations). We examined language using the Linguistic Inquiry and Word Count program (LIWC; theoretically created dictionaries) and open-vocabulary themes (clusters of data-driven semantically-related words). Although some studies give the impression that LIWC's positive and negative emotion dictionaries can be used as indicators of emotion experience, we found that when computed on spoken language, LIWC emotion scores were not significantly associated with self-reports of state emotion experience. Exploration of other categories of language variables suggests a number of hypotheses about substantive everyday correlates of momentary positive and negative emotion that can be tested in future studies. These findings (a) suggest that LIWC positive and negative emotion dictionaries may not capture self-reported subjective emotion experience when applied to everyday speech, (b) emphasize the importance of establishing the validity of language-based measures within one's target domain, (c) demonstrate the potential for developing new hypotheses about personality processes from the open-ended words that are used in everyday speech, and (d) extend perspectives on intraindividual variability to the domain of spoken language. (PsycINFO Database Record (c) 2020 APA, all rights reserved).