Viewpoint Analysis of Autism-Related Comments in Reddit During

COVID-19

Narges Azizifard

, Lidia Pivovarova

and Eetu Mäkelä

University of Helsinki, Finland

ﬁ ﬁ

Keywords:

Autism, COVID-19, Reddit, Social Media.

Abstract:

Major events, such as the COVID-19 health crisis, have different effects on various groups, with vulnerable

populations like individuals with Autism Spectrum Disorder (ASD) being especially affected. Social media

platforms capture the unique experiences of these individuals and their families, offering a wide range of per-

spectives and voices. This study utilizes text analysis methods to examine Reddit discussions concerning both

autism and COVID-19. Through the analysis of these comments, we identify key themes, including chal-

lenges in education, employment, and family life, as well as conspiracy theories and propaganda surrounding

vaccination. Our ﬁndings shed light on the struggles faced by individuals with autism during the lockdown

and highlight their coping strategies. Additionally, the study reveals signiﬁcant variations in sentiment and

emotions across different themes within the comments, providing deeper insights into how these experiences

are expressed and understood in online communities.

1 INTRODUCTION

Signiﬁcant events, like the COVID-19 outbreak, im-

pact various groups differently and can be particularly

challenging for vulnerable populations, such as indi-

viduals with conditions like Autism Spectrum Disor-

der (ASD). In the digital age, they have the opportu-

nity to share their unique experiences online through

social media. At the same time, a variety of views

and opinions on these people is also presented in so-

cial media. Thus, social media allows for studying

a multidimensional discussion of certain events and

conditions, and for revealing communities discussing

same themes from different angles. Various language

analysis techniques, such as topic modelling, senti-

ment analysis, etc. help to organize collections of so-

cial media posts and comments into subsets that differ

in their viewpoint. In addition, in many of social me-

dia platforms, discussions are organized in a form of

groups and channels, that provide an additional facet

of analysis.

In this paper we focus on analysis of ASD-related

discussions in Reddit during the COVID outbreak. To

investigate the impact of COVID-19 and lockdown

https://orcid.org/0000-0002-7525-4561

https://orcid.org/0000-0002-0026-9902

https://orcid.org/0000-0002-8366-8414

on individuals with ASD or people who are close

to them, we ﬁnd all comments regrading autism in

COVID-related subreddits, and all comments from

autism-related subreddits linked to COVID time. This

research aims to uncover patterns, sentiments, and

emotions, providing insights into autism during the

COVID-19 lockdown. Overall, our goal is to high-

light the challenges that arise in such situations, which

often result in shock and confusion.

We utilized a variety of NLP techniques, such as

including topic modeling and sentiment analysis to

identify recurring themes in the discourse and exam-

ine how different signals detected by these tools are

distributed across communities and themes.

We discovered signiﬁcant differences among dis-

cussed topics particularly in the emotions and senti-

ments expressed in the comments. We combine data-

driven analysis with close reading and demonstrate

that the differences found by automatic tools are in-

deed reﬂected in different attitudes and discussion

culture variety among topics which we extracted.

To sum up, our objective is to explore the follow-

ing research questions (RQs):

RQ1: What are the most frequently discussed topics

in the discourse? What types of issues they address?

RQ2: Do users exhibit the different emotional reac-

tions to the different extracted topics?

Azizifard, N., Pivovarova, L. and Mäkelä, E.

Viewpoint Analysis of Autism-Related Comments in Reddit During COVID-19.

DOI: 10.5220/0013064700003825

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 2024), pages 401-408

ISBN: 978-989-758-718-4; ISSN: 2184-3252

401

RQ3: Are there any differences in community reac-

tion on the comments within each topic?

The main contributions are the following:

• To the best of our knowledge, this is the ﬁrst at-

tempt to provide a quantitative analysis of subred-

dit comments regarding ASD during the COVID-

19 lockdown.

• Our analysis encompasses a wide range of sub-

reddit communities without restricting to spe-

ciﬁc subreddit, community types, genders, or age

groups. This ensures that the ﬁltered comments

represent a diverse cross-section of the autistic

population and minimize bias.

• Our approach offers a novel insight for a more

comprehensive perception of unrecorded issues

related to ASD, especially during public disasters

such as global health crisis and lockdowns. This

can lead to prevention, resolution, and control of

public health effects.

2 RELATED WORK

The utilization of social media as a medium for dis-

seminating health information has gained consider-

able attention in recent years. While some families

of children with ASD prefer to obtain health informa-

tion from established, local resources, others turn to

social media for support and information. This trend

highlights the necessity of understanding how health-

related issues are represented in online discussions.

Social media platforms provide users with the op-

portunity to discuss health-related information and

advocacy (Whisenhunt Saar et al., 2023). Reddit,

in particular, is popular due to its accessibility and

user-generated content but remains relatively unex-

plored in empirical research compared to larger plat-

forms like Twitter and Facebook (Yeung et al., 2022).

Moreover, a recent review indicates that much of the

knowledge about Reddit content is derived from com-

putational textual analysis, and it advocates for further

contextualization of this data (Proferes et al., 2021).

Reddit is a pseudonymous social media platform,

where users create accounts that do not directly re-

veal their identities but may still include personalized

information. For instance, the subreddits that an ac-

count follows indicate the user’s interests, although

this information is not immediately recognizable to

individuals unfamiliar with the account holder. Red-

dit’s pseudonymous nature facilitates intimate discus-

sions among users, who do not fear to disclose identi-

ﬁable public information (Palonis, 2021). An increas-

ing body of research examines Reddit as a forum for

Table 1: Basic statistics of ﬁltered dataset.

Subreddit # com-

ments

# users

AskReddit 9,622 8,066

conspiracy 3,708 2,145

wallstreetbets 3,088 2,483

DebateVaccines 2,302 485

insanepeoplefacebook 2,083 1758

vaxxhappened 2,069 1,500

memes 2,114 1,880

AmItheAsshole 2,016 1,048

politics 1,884 1,561

autism 1,817 1,273

worldnews 1,755 1,482

news 1,654 1,377

Coronavirus 1,547 1,283

AntiVaxxers 1,292 552

TrueAntiVaccination 1,147 277

insaneparents 1,109 984

facepalm 1,108 982

aspergers 1,032 743

total 41,875 28,270

discussing topics related to ASD. These discussions

generally occur within speciﬁc subreddits, i.e. ded-

icated forums that address various aspects of autism

(Whisenhunt Saar et al., 2023).

Qualitative methods are also frequently used to

analyze Reddit comments related to autism. For ex-

ample, in (Thom-Jones et al., 2024), the authors em-

ployed a qualitative approach to study how individu-

als discuss and reﬂect on their experiences of mother-

hood within a Reddit community dedicated to autistic

parents. This method allowed them to explore the nu-

anced perspectives and personal experiences shared

by community members, providing a deeper under-

standing of the unique challenges and insights associ-

ated with parenting as an autistic individual.

Among those empirically investigating Reddit

content related to ASD (Bellon-Harn et al., 2022) ex-

plored the representation of applied behavior anal-

ysis (ABA) interventions within Reddit discussions

through topic modeling. Additionally, the study in

(Whisenhunt Saar et al., 2023) analyzed ABA dis-

course on Reddit using inductive manual coding of

contextualized thread content.

Other studies investigate how user use internal

voting system to shape their communities by promot-

ing content they ﬁnd useful, relevant, and trustworthy,

while downvoting and thus suppressing content they

consider irrelevant, false, or self-serving(Leavitt and

Robinson, 2017; Richterich, 2014).

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402

3 METHODOLOGY

3.1 Data Collection and Filtering

Data retrieval was conducted using the Reddit Appli-

cation Programming Interface (API).

All comments and subreddit titles were converted

to lowercase. To retain comments related to both

COVID-19 and autism, we applied the following ﬁl-

tering criteria:

• We considered Reddit comments from March

2020 to October 2021 to ensure they were written

during the COVID-19 lockdown, as documented

on Wikipedia

• Next, after searching related phrases on Google

and the Reddit website, we ﬁltered comments

that contained at least one of the following

COVID-related terms: “covid”, “corona”,

“c19”, “c-19”, “sars-cov-2”, “sars-cov2”,

“sarscov2”, “china_ﬂu”, “china-ﬂu”, “chinaﬂu”,

“wuhan_ﬂu”, “wuhan-ﬂu”, “wuhanﬂu”, “lock-

down”, “quarantine”, “vaxx”, or “vaccine”, and at

least one of “asperg” or “autis” for autism-related

comments.

To analyze the ﬁltered comments, we excluded non-

English posts. For preparing the data for further anal-

ysis, we performed data cleaning using Natural Lan-

guage Processing (NLP) techniques to remove unnec-

essary information from the collected tweets, includ-

ing URLs, special characters (e.g., @, /, ...), and stop-

words (e.g., in, the, ...). After these steps, a total of

111,507 comments remained for analysis.

We focused on subreddits with more than 1000

comments. Among these subreddits, we excluded the

"csci040temp" after determining that it was a tempo-

rary subreddit active only during October and Novem-

ber 2020. The ﬁnal dataset consisted of around 42

thousand comments from 18 subreddits.

3.2 Data Analysis

3.2.1 Explanatory Analysis

In Table 1 we present the basic statistics of our

dataset. We also performed an analysis of the tempo-

ral distribution of comments within our selected sub-

reddits from the ofﬁcial start of quarantine to its con-

clusion. As shown in Figure 1, at the start of the quar-

antine, there was an increase in comments related to

our themes in most of the subreddits, although these

discussions eventually decreased (except conspiracy

https://en.wikipedia.org/wiki/COVID-19_lockdowns

subreddit). The initial surge was largely driven by

the fear and uncertainty people felt during the early

stages of the lockdown, as the situation was unprece-

dented and many were unsure how to cope. The sig-

niﬁcant amount of news, particularly on social media,

further contributed to the heightened anxiety. Many

users also debated vaccines, the anti-vaxx movement,

and the pros and cons of vaccination. This trend re-

ﬂects a growing need for individuals to seek guidance

and information regarding autism-related issues dur-

ing the lockdown. In the following section, we ex-

plore the themes of these discussions to gain a deeper

understanding of the issues raised.

3.2.2 Topic Modeling

We utilized Latent Dirichlet Allocation (LDA) to an-

swer the ﬁrst RQ, i.e. to reveal the most frequently

discussed topics during the COVID-19 lockdown, as

well as the challenges faced by people with autism

and those who support them. We applied LDA

model using different number of topics, from 2 to 20

and choose the best model based on coherence met-

ric (Röder et al., 2015)

3.2.3 Emotion and Sentiment Analysis

To answer RQ2 and uncover differences between top-

ics and the users’ reactions to the issues they face, we

performed emotion and sentiment analyses on ﬁltered

comments for each topic separately.

For sentiment analysis, we employed VADER

(Valence Aware Dictionary and sEntiment Reasoner),

a lexicon and rule-based tool speciﬁcally designed to

detect sentiments expressed in social media.

For emotion analysis we utilized Plutchik’s model

of emotions(Plutchik, 1980), which identiﬁes eight

primary emotions—anger, fear, sadness, disgust, sur-

prise, anticipation, trust, and joy—from user com-

ments. To extract emotions based on Plutchik’s

framework, we employed the NRC emotion lexi-

con(Mohammad and Turney, 2013), which comprises

a list of English words associated with each primary

emotion, as well as two sentiments: negative and pos-

itive. The annotations within the lexicon were man-

ually curated through crowdsourcing. To detect text

emotions, we count a number of tokens from each lex-

icon category in comments and then average them for

each topic.

We did not take the negative and positive senti-

ments from the NRC lexicon into account in this anal-

ysis, as this is analysed separately using VADER.

We used Gensim implementation, https:

//radimrehurek.com/gensim/

https://pypi.org/project/vaderSentiment/

Viewpoint Analysis of Autism-Related Comments in Reddit During COVID-19

403

Figure 1: Temporal distribution of comments in subreddits.

Table 2: Categorical variable of vote score.

Category name Score range

Slightly upvoted 2–10

Moderately upvoted 11–50

Greatly upvoted 51–200

Very greatly upvoted 201–1000

Extremely upvoted Greater than 1000

Slightly downvoted -10–0

Moderately downvoted −50 to −11

Greatly downvoted −200 to −51

Very greatly downvoted −1000 to −201

Extremely downvoted Less than −1000

3.3 Vote Scores

To address RQ3, we analyzed the comment scores to

identify any changes in user voting behavior across

different topics, applying the categories deﬁned in

(Davis and Graham, 2021). The Reddit vote score is

determined by subtracting the number of downvotes

from the number of upvotes. This score is then clas-

siﬁed into one of ten categories, reﬂecting whether a

comment is slightly, moderately, greatly, very greatly,

or extremely upvoted (or downvoted)(Davis and Gra-

ham, 2021) as shown in Table 2.

3.4 Results

3.4.1 Topic Modeling

Table 3 presents a selection of words associated with

each identiﬁed topic. We further analysed each topic

through a close reading of the comments related to

each topic, focusing on the dominant keywords asso-

Table 3: Prominent words for each topic.

Topic 0 Topic 1 Topic 2

vaccine doctor people

autism fact kid

study money work

children government school

cause health parent

claim mask mom

evidence disease family

anti_vaxxer science live

risk opinion job

support issue hard

ciated with each topic. The primary discussions cen-

tered around the following themes:

Topic 0:

• Issues regarding vaccination and its potential

harms: This topic accounts for 47 percent of

all comments in the dataset. Some users discuss

claims and studies suggesting that vaccines may

have side effects, such as causing autism in chil-

dren or increasing the risk of having autistic off-

spring in the future. Consequently, some individ-

uals are concerned about why others take the per-

ceived risk of using vaccines. Additionally, it is

argued that vaccines can lead to death and cer-

tain diseases. Therefore, discussions on this topic

tend to either support vaccinations or align with

the views of anti-vaxxers.

Topic 1:

• Conspiracy theories and propaganda regard-

ing vaccination and wearing mask: Topic 1

comprises 31 percent of all comments in the

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404

dataset; it also discusses whether to trust vac-

cinations, particularly in the context of con-

spiracy theory,propaganda,and governmental per-

spectives. Many users argue that medical research

and science afﬁrm the safety of vaccines, while

those who deny science and promote conspir-

acy theories, anti-science propaganda, and anti-

government narratives claim that vaccines cause

autism, death, and disease. Numerous comments

ridiculed these claims by comparing the state-

ment "vaccines cause autism" to assertions like

"5G technology is deadly","the Earth is ﬂat", and

"COVID is a hoax". Additionally, there was dis-

cussion about the inﬂuence of authorities in shap-

ing political opinions. Some users argued that

governments, pharmaceutical companies and doc-

tors have plans to proﬁt from vaccines by promot-

ing their brands as safe and not causing autism or

disease. However, other users disagreed, express-

ing appreciation for the efforts and hard work

of healthcare workers. Similar discussions arose

around wearing masks, with some users criticiz-

ing anti-maskers who claimed that mask mandates

were driven by ﬁnancial motives and intended to

prevent a market crash due to COVID-19.

Topic 2: This topic makes up 22 percent of the com-

ments in the dataset, and discuss several themes re-

lated to effects that the crisis causes in everyday life.

• Parental issues related to COVID-19 lock-

down: This topic explores how the COVID-19

has affected family dynamics, particularly focus-

ing on how parents, especially single mothers,

have struggled with managing autistic children

exhibiting aggressive behavior due to quarantine

measures and the loss of recreational activities,

and challenges to cover expenses while struggling

to meet the needs of their autistic children. Many

autistic individuals or parents of autistic children

have been unable to speak with therapists or ac-

cess other services due to COVID-19, adversely

affecting their mental health.

• Issues related to incorrect behavior of parents:

Numerous autistic users voiced concerns about

how their parents blamed and punished them for

their condition. Due to lockdown restrictions and

social distancing rules, they could not access af-

fordable shared living spaces and were forced to

live with their aggressive parents. Many parents

also pressured their autistic children not to receive

vaccines, fearing it would worsen their condition.

• Issues related to work: Some users discussed

losing their jobs and the challenges of employ-

ment as autistic individuals, given the high un-

employment rate within the autistic community.

Single mothers, in particular, face signiﬁcant chal-

lenges as they must work more to cover expenses

while struggling to meet the needs of their autistic

children.

• Issues related to wearing mask: The ﬁnal issue

raised in the comments was the difﬁculty autis-

tic children face in wearing masks and adhering

to other rules, such as social distancing at school.

This highlights the sensory challenges associated

with mask-wearing and physical contact.

• Positive but still negative point: Some autis-

tic users expressed relief from the isolation, not-

ing that not attending school and taking exams

remotely reduced their stress. They also high-

lighted that being away from environments where

they are often mocked, especially by anti-vaxxer

groups, provided a sense of peace.

To enhance our analysis and determine the speciﬁc

subreddits where each topic is most prevalent, we

identiﬁed the subreddits associated with each topic

and examined the distribution of comments pertain-

ing to each topic. As illustrated in Figure 2, the

largest concentration of Topic 0, which includes dis-

cussions that either support vaccinations or align with

anti-vaccination sentiments, is found in the subreddits

“DebateVaccines” and “TrueAntiVaccination”. This

is followed by the “AntiVaxxers” subreddit, which

also primarily features debates centered around vac-

cines. The content in these subreddits reﬂects a di-

verse range of opinions, from those advocating for

vaccination to those opposing it, highlighting the po-

larized nature of the vaccine debate.

Topic 1, which revolves around vaccine and mask

debates related to conspiracy theories and propa-

ganda, is most prevalent in the “wallstreetbets” sub-

reddit, with signiﬁcant discussions also occurring in

the “politics” and “worldnews” subreddits. As men-

tioned earlier, many comments within this topic sug-

gest that vaccines and masks are perceived by some as

tools for ﬁnancial exploitation and control, reﬂecting

a broader skepticism towards public health measures

and governmental motives.

Finally, Topic 2 primarily focuses on individuals

coping with autism, caring for autistic family mem-

bers, addressing parental challenges, dealing with

aggressive parenting, facing difﬁculties with mask-

wearing, navigating workplace problems, and man-

aging mental health issues. This topic is most promi-

nent in the “AmItheAsshole” subreddit, which aims to

provide advice based on personal stories and conﬂicts

shared by users. Additionally, this topic is highly

relevant in the “aspergers” and “autism” subreddits,

which are dedicated to discussions about autism and

Viewpoint Analysis of Autism-Related Comments in Reddit During COVID-19

405

Figure 2: Distribution of topics per each subreddit, sorted by the prominence of Topic 0.

Figure 3: Sentiment distribution across topics.

the experiences of autistic individuals; these subred-

dits serve as support communities for those seeking

guidance, understanding and shared experiences.

3.4.2 Sentiment Analysis

Figure 3 displays the results of the sentiment analy-

sis, in relation to each of the topics. From the data

presented, it is evident that the comments associated

with Topic 1 tend to be the most negative and the

least positive overall. As mentioned in the previ-

ous section, users engaged in this topic often discuss

how powerful entities manipulate false and mislead-

ing claims, crafting conspiracy theories and propa-

ganda to inﬂuence public opinion on vaccination and

mask-wearing. These discussions suggest that these

actors are motivated by proﬁt, exploiting the situation

for ﬁnancial gain while disregarding the well-being

and health of the public.

Conversely, Topic 2 contains the highest number

of positive comments and the fewest neutral ones. As

we previously discussed, this topic primarily centers

on the challenges faced by autistic individuals and

their families during the lockdown, as well as the cop-

ing strategies they employed. These discussions tend

to be more optimistic and supportive, reﬂecting a pos-

itive outlook rather than a neutral stance.

The discussions in Topic 0 remain relatively neu-

tral, not leaning strongly toward one side or the other.

They generally revolve around the debate on whether

or not to trust vaccines, with participants expressing

either anti-vaccine sentiments or support for vaccina-

tion. As a result, this topic showcases a range of user

perspectives, reﬂecting the diverse stances within the

conversation.

3.4.3 Emotion Analysis

The results of the emotion analysis are shown in Fig-

ure 4. To visualize the emotional ﬁngerprint of the

corpora and the text annotated with Plutchik’s model,

we utilized "PyPlutchik" (Semeraro et al., 2021), a

specialized tool designed for this purpose.

Once again, the comments associated with Topic

2 exhibit the highest levels of emotional expression,

followed by those in Topics 1 and 0. This heightened

emotional intensity in Topic 2 can be attributed to the

nature of the comments, where individuals share per-

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406

Figure 4: Plutchik’s emotions of Topics.

Figure 5: Distribution of score categories among topics.

sonal experiences and seek support in coping with

their challenges, leading to a richer expression of

emotions compared to the other topics.

At the same time, it is hard to ﬁnd any single emo-

tion is more prominent in any of the topics. All three

contain the whole spectrum of emotions, although

with different intensity.

3.4.4 Vote Scores of Comments

We created vote score categories for each comment in

our dataset and grouped the comments based on their

respective topics. The results are shown in Figure 5,

where we show a proportion for each vote score cat-

egory for each topic. We did not include percentage

labels for categories where the percentage was below

5%. A large number of comments had a score of 1,

which do not fall into any speciﬁc category and nei-

ther supports nor opposes the content; therefore, we

assigned these comments to a separate category la-

beled "score 1".

As it was mentioned earlier, the scores reﬂect

otherusers’ reactions to a comment rather than the

sentiment of the comment author. After reviewing

the scores, we discovered that there were no com-

ments in the "Extremely downvoted" category across

any of the topics, and only one comment was in the

"Very Greatly downvoted" category. Additionally, as

shown in Figure 5, the percentage of comments in the

"Slightly upvoted" category is higher than in other

categories, indicating that more comments are pro-

moted rather than suppressed. Regarding the "Moder-

ately upvoted," "Greatly upvoted," "Very greatly up-

voted," and "Extremely upvoted" categories, there are

not signiﬁcant differences across the three topics.

Overall, our analysis revealed that the percentage

of downvoted categories in Topic 2, which primarily

focused on issues faced by autistic individuals and

those who interact with them, was lower compared

to the other topics. This lower rate of downvotes in-

dicates that users perceived these comments as more

valuable, insightful, and relevant. The topics dis-

cussed in this category were likely seen as meaning-

ful and resonated more strongly with the community,

suggesting a higher level of engagement and agree-

ment with the content.

Viewpoint Analysis of Autism-Related Comments in Reddit During COVID-19

407

4 CONCLUSION

Online communication is important for many people,

and it can be a part of coping strategies during crisis.

At the same time, it is important to discriminate be-

tween different communities, and to be able to point

people to right places on the WEB where they can get

support. Viewpoint analysis may potentially help in

that regard.

In this paper we presented a ﬁrst attempt to apply

data-driven analysis on Reddit comments devoted to

autism during COVID-19 outbreak. While the pre-

vious work studying autism in Reddit were focused

on some pre-selected subreddits and employs mostly

qualitative analysis, we combined quantitative analy-

sis with close reading. We have shown that language

analysis techniques, such as topic modelling and sen-

timent analysis, can reveal viewpoint differences in

reddit communities.

We observed various themes in the comments,

such as vaccination and disinformation, family issues,

conspiracy theories, and propaganda. Comments that

focus on the challenges faced by autistic individu-

als and their families during quarantine, as well as

their coping strategies, tend to be more emotional and

less neutral. These comments also display more pos-

itive sentiment and are generally more optimistic and

supportive. Additionally, they tend to receive fewer

downvotes compared to other types of comments.

Based on the discussions related to various top-

ics, sentiments, emotions, and vote scores, we can

observe the complex and multi-dimensional nature of

online conversations. These discussions reveal how

different themes and emotional tones interact with

user engagement, highlighting the diverse perspec-

tives and reactions within online communities. This

complexity reﬂects the broader structure of online dis-

course, where multiple factors contribute to shaping

the ﬂow and tone of the conversation, making it both

dynamic and multifaceted. In general, the analysis

presented in this study offers valuable insights into the

challenges faced by autistic individuals and their fam-

ilies during the outbreak. These ﬁndings can aid com-

munities, particularly children, parents, and individu-

als with disorders, in better managing autism. Fur-

thermore, the study provides critical information for

government and authorities to enhance their policy-

making processes regarding support and services for

communities with disorders, including ASD, during

critical situations such as global health crisis.

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