Twitter usage about autism spectrum disorder

Use of Social Media Data Mining to Examine Needs, Concerns, and Experiences of People With Traumatic Brain Injury

PURPOSE

Given the limited availability of topic-specific resources, many people turn to anonymous social media platforms such as Reddit to seek information and connect to others with similar experiences and needs. Mining of such data can therefore identify unmet needs within the community and allow speech-language pathologists to incorporate clients' real-life insights into clinical practices.

METHOD

A mixed-method analysis was performed on 3,648 traumatic brain injury (TBI) subreddit posts created between 2013 and 2021. Sentiment analysis was used to determine the sentiment expressed in each post; topic modeling and qualitative content analysis were used to uncover the main topics discussed across posts. Subgroup analyses were conducted based on injury severity, chronicity, and whether the post was authored by a person with TBI or a close other.

RESULTS

There was no significant difference between the number of posts with positive sentiment and the number of posts with negative sentiment. Comparisons between subgroups showed significantly higher positive sentiment in posts by or about people with moderate-to-severe TBI (compared to mild TBI) and who were more than 1 month postinjury (compared to less than 1 month). Posts by close others had significantly higher positive sentiment than posts by people with TBI. Topic modeling identified three meta-themes: Recovery, Symptoms, and Medical Care. Qualitative content analysis further revealed that returning to productivity and life as well as sharing recovery tips were the primary focus under the Recovery theme. Symptom-related posts often discussed symptom management and validation of experiences. The Medical Care theme encompassed concerns regarding diagnosis, medication, and treatment.

CONCLUSIONS

Concerns and needs shift over time following TBI, and they extend beyond health and functioning to participation in meaningful daily activities. The findings can inform the development of tailored educational resources and rehabilitative approaches, facilitating recovery and community building for individuals with TBI.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24881340.

Building Community and Identity Online: A Content Analysis of Highly Viewed #Autism TikTok Videos

Background

TikTok is a popular social media site for connecting with others online where many users also access and share health-related information. Previous studies have characterized information shared about specific disabilities on TikTok, but descriptions of autism-related content are lacking. Understanding the information shared about autism on TikTok is important to understanding health communication in this space, the lived experiences of autistic people, and the role that social media platforms play in building community through connection, understanding, and inclusion.

Methods

We used an open-source data scraper to identify and download videos with at least 1 million views and that used the hashtag #autism from TikTok. Using a joint inductive and deductive approach, we performed a content analysis of videos. We identified six content topics that described video content (e.g., positive social interactions and features of autism), and three categories that described video purpose (e.g., educational, experiential, and observational). We used descriptive statistics to describe characteristics of who was in the TikTok videos.

Results

We analyzed n = 678 videos. The most common content topic of TikTok videos was features of autism (39.7%), followed by marginalization (25.4%). Most videos were categorized as experiential (61.4%) or observational (31.4%) rather than educational (7.2%). Approximately 65.5% of videos featured an autistic adult and 22.6% featured an autistic child. Among videos featuring an autistic person (n = 594), most autistic people were perceived to be White (87.0%) with similar numbers of feminine (52.9%) and masculine (44.8%) presenting autistic people.

Conclusions

Highly viewed #autism TikTok videos primarily share autistic people's individual experiences rather than providing general education about autism. However, autistic Black, Indigenous, and people of color may have difficulty finding others that represent them along both disability and racial dimensions. Future research should investigate autistic people's motivations for using TikTok, and how #autism content shapes social discourse about autism.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts

BACKGROUND

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits.

OBJECTIVE

In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process.

METHODS

Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks.

RESULTS

We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case.

CONCLUSIONS

By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

Digital trends in autism: a scoping review exploring coverage of autism across YouTube, Twitter, and Facebook

Autism continues to be a leading neurodevelopmental disorder across adult and pediatric populations that transcends racial, ethnic, age, and socioeconomic groups worldwide. Autism care and treatment also exerts immense costs on the healthcare system and lost productivity which are partly attributed to the existing resource limitations globally. Organizations, campaigns, and policies exist worldwide in increasing equity and accessibility of resources and services to individuals with autism. In the context of our digital era, a wealth of information is also more readily available on autism through electronic communication including social media platforms. As YouTube, Twitter and Facebook are ever-growing and among the leading social media platforms in contemporary times, examination of content covered on autism across these communication mediums is timely and warranted. This review consolidates findings from 32 sources on the sources, formats, and nature of content covered on YouTube, Twitter, and Facebook pertaining to a wealth of dimensions surrounding autism. Strengths and limitations of the studies and endeavors are presented. Implications for future campaign development, health equity, health policy, neurodiversity, and patient care are also delineated. Lastly, recommendations for future research and practice are discussed which present directions for tapping into the potential of YouTube, Twitter, and Facebook as health communication mediums across the ever-changing autism landscape.

Public concerns and attitudes towards autism on Chinese social media based on K-means algorithm

To investigate the hot topics and attitudes of autism in the larger community. In this study, we analyzed and summarized experimental texts from the social media platform Zhihu using the TF-IDF algorithm and K-means clustering approach. Based on the analysis of the 1,740,826-word experimental text, we found that the popularity of autism has steadily risen over recent years. Sufferers and their parents primarily discuss autism. The K-means clustering algorithm revealed that the most popular topics are divided into four categories: self-experience of individuals with autism, external views of individuals with autism, caring and stressful behaviors of caregivers, and information about autism. This study concluded that people with autism face more incredible negative emotions, external cognitive evaluations of the autistic group reflect stereotypes, the caregiver's family suffers high financial and psychological stress, and disorders caused by disease in autistic individuals.

Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Twitter Messages Related to Autism Spectrum Disorders

BACKGROUND

The aim of our study was to conduct an emotional analysis of Turkish Twitter messages related to autism spectrum disorders (ASD).

METHODS

An emotion analysis was performed using quantitative and qualitative analysis methods on Turkish Twitter messages shared between November 2021 and January 2022 that contained the words "autism" and "autistic."

RESULTS

It was found that 81.5% of the 13,042 messages that constituted the sample of this study contained neutral emotions. The most frequently used words in Twitter messages were autism, a, universe, strong, patience, warriors, and happy. The qualitative analysis revealed three main themes. These themes were: "experiences," "informing society and awareness," and "humiliation."

CONCLUSION

In this study, it was found that Turkish Twitter messages related to autism, which were analyzed using artificial intelligence-based emotion analysis, often contained neutral emotions. While the content of these messages, often shared by parents, was related to experiences, and the messages shared by pediatric psychiatrists and rehabilitation center employees were informative in nature, it was determined that the word "autism" was used to insult, which is outside of its medical meaning.

Analysis of the Brazilian-Portuguese Content on Autism Spectrum Disorder Available on YouTube Videos

AIMS

Information about Autism Spectrum Disorder (ASD) is available in different videos on YouTube. However, these videos might present inaccurate or outdated information. The aims of this study were to 1) identify characteristics of Brazilian-Portuguese videos about ASD (i.e., type of content, number of views, likes, and dislikes); 2) analyze the content of informative videos about ASD, by assessing their trustworthiness and overall quality; and 3) investigate the most popular topics of informative videos about ASD over time.

METHODS

This is a cross-sectional study that investigated Brazilian-Portuguese ASD content on YouTube. Videos were selected by two examiners and categorized as experiential or informative. The Discern checklist and the Global Quality Score (GQS) were used to analyze the trustworthiness and quality of informative videos.

RESULTS

In total, 216 videos were analyzed, of which 19.5% and 85% were classified as experiential and informative, respectively. The majority of informative videos presented moderate trustworthiness and quality. Videos about ASD clinical aspects were the most popular.

CONCLUSION

YouTube offers a large number of experiential and informative videos on ASD. However, some of these videos fail to present reliable and additional sources of information for stakeholders. Efforts to promote knowledge translation about ASD on YouTube are necessary.

Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification

BACKGROUND

COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

OBJECTIVE

We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

METHODS

We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

RESULTS

We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

CONCLUSIONS

This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users' different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users' interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users' informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

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.

Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to COVID-19: topic modelling and sentiment analysis

BACKGROUND

Social media contains an overabundance of health information relating to people living with different type of diseases. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts and reported trends have revealed a considerable increase in prevalence and incidence. Research had shown that the ASD community provides significant support to its members through Twitter, providing information about their values and perceptions through their use of words and emotional stance. Our purpose was to analyze all the messages posted on Twitter platform regarding ASD and analyze the topics covered within the tweets, to understand the attitude of the various people interested in the topic. In particular, we focused on the discussion of ASD and COVID-19.

METHODS

The data collection process was based on the search for tweets through hashtags and keywords. After bots screening, the NMF (Non-Negative Matrix Factorization) method was used for topic modeling because it produces more coherent topics compared to other solutions. Sentiment scores were calculated using AFiNN for each tweet to represent its negative to positive emotion.

RESULTS

From the 2.458.929 tweets produced in 2020, 691.582 users were extracted (188 bots which generated 59.104 tweets), while from the 2.393.236 total tweets from 2019, the number of identified users was 684.032 (230 bots which generated 50.057 tweets). The total number of COVID-ASD tweets is only a small part of the total dataset. Often, the negative sentiment identified in the sentiment analysis referred to anger towards COVID-19 and its management, while the positive sentiment reflected the necessity to provide constant support to people with ASD.

CONCLUSIONS

Social media contributes to a great discussion on topics related to autism, especially with regards to focus on family, community, and therapies. The COVID-19 pandemic increased the use of social media, especially during the lockdown period. It is important to help develop and distribute appropriate, evidence-based ASD-related information.

Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis

Purpose Telepractice was extensively utilized during the COVID-19 pandemic. Little is known about issues experienced during the wide-scale rollout of a service delivery model that was novel to many. Social media research is a way to unobtrusively analyze public communication, including during a health crisis. We investigated the characteristics of tweets about telepractice through the lens of an established health technology implementation framework. Results can help guide efforts to support and sustain telehealth beyond the pandemic context. Method We retrieved a historical Twitter data set containing tweets about telepractice from the early months of the pandemic. Tweets were analyzed using a concurrent mixed-methods content analysis design informed by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results Approximately 2,200 Twitter posts were retrieved, and 820 original tweets were analyzed qualitatively. Volume of tweets about telepractice increased in the early months of the pandemic. The largest group of Twitter users tweeting about telepractice was a group of clinical professionals. Tweet content reflected many, but not all, domains of the NASSS framework. Conclusions Twitter posting about telepractice increased during the pandemic. Although many tweets represented topics expected in technology implementation, some represented phenomena were potentially unique to speech-language pathology. Certain technology implementation topics, notably sustainability, were not found in the data. Implications for future telepractice implementation and further research are discussed.

#Bias: The Opportunities and Challenges of Surveys That Recruit and Collect Data of Autistic Adults Online

Internet-based online surveys are a crucial tool for researchers to learn about the understudied and often overlooked population of autistic adults. The recruitment and administration of online surveys can be cheaper, quicker, and have a wider reach compared with more traditional in-person methods. As online surveys become more prevalent, it is important to place strengths in the context of limitations and biases that can arise when recruiting and administering surveys online. In this perspective, we discuss two common issues that often appear in studies that use online tools to recruit and administer surveys to autistic adults and nonautistic volunteers: selection bias and sample identifiability. Selection bias is the distortion in effect estimates (e.g., relative risk, risk ratio, incidence rate) resulting from the factors that influence why a person chose to participate or how the researcher recruits and selects participants in a study. Sampling identifiability is the ability (or inability) to quantify and define the population of interest. We use a case example of an online survey study of suicidal ideation in autistic adults and describe how issues in selection bias and sample identifiability arise and may lead to challenges unique to studying autistic adults. We conclude with recommendations to improve the quality and utility of online survey research in autistic adults. Using online resources to recruit and collect data on autistic adults is an incredible tool with great potential; yet, authors need to consider the limitations, potential biases, and tools to overcome systematic error at each stage of the study.

LAY SUMMARY

What is the purpose of this article?: Our purpose was to describe challenges in conducting and analyzing data from surveys of autistic adults, recruited and completed online.What is already known on the topic?: Health outcomes for autistic adults are understudied by crucial areas of autism research. While researchers are interested in the outcomes of autistic adults, this type of research is difficult because many autistic adults are not formally diagnosed, so not available to recruit for studies through clinic registries. Furthermore, study participation can be a long, inconvenient, and stressful process. It is not surprising then that we are seeing internet surveys of autistic adults become a popular tool to reach this population. We wanted to offer an overview and recommendations of these issues to researchers and people who read research about topics pertaining to autistic adults.What are the perspectives of the authors?: We are epidemiologists at Boston University and the University of Wisconsin-Madison. We both conduct research centered in improving health and well-being for autistic people across the life span. As people who study research methods, we have seen a lot of new research using survey methods. This research is intriguing, but all too often the articles need more information so we can be sure that the research is high quality. We want to share ways to improve this type of research and to help people in understanding the strengths and limitations of online survey research.What do the authors recommend?: We offer a few considerations for researchers working in this area. (1) Make the steps you took to do the research as clear as possible. (2) Be specific about who you intend to study and who you ended up studying. (3) Present the demographics and characteristics of the participants. (4) If possible, consider using data analysis methods to account for selection bias and sample identifiability issues. (5) Do not make statements that are not supported for your study results. (6) Acknowledge that we are at the beginning of studying autistic adults. (7) Advocate for more funding for research in autistic adults.How will these recommendations help autistic adults now or in the future?: Online surveys are an important tool for researchers to generate hypotheses and connect with a wider range of participants. However, online surveys have unique methodological challenges. We hope that this perspective raises the topic of bias and misinterpretation in online surveys and researchers continue to produce valid and meaningful work that is crucial to improving lives of autistic adults.

Online Attitudes and Information-Seeking Behavior on Autism, Asperger Syndrome, and Greta Thunberg

The purpose of this study was to examine Internet trends data and sentiment in tweets mentioning autism, Asperger syndrome, and Greta Thunberg during 2019. We used mixed methods in analyzing sentiment and attitudes in viral tweets and collected 1074 viral tweets on autism that were published in 2019 (tweets that got more than 100 likes). The sample from Twitter was compared with search patterns on Google. In 2019, Asperger syndrome was closely connected to Greta Thunberg, as of the tweets specifically mentioning Asperger (from the total sample of viral tweets mentioning autism), 83% also mentioned Thunberg. In the sample of tweets about Thunberg, the positive sentiment expressed that Greta Thunberg was a role model, whereas the tweets that expressed the most negativity used her diagnosis against her and could be considered as cyberbullying. The Google Trends data also showed that Thunberg was closely connected to search patterns on Asperger syndrome in 2019. The study showed that being open about health information while being an active participant in controversial debates might be used against you but also help break stigmas and stereotypes.

Twitter Usage Using Common Reference to Tinnitus

Objective The objective of the study was to examine specific patterns of Twitter usage using common reference to tinnitus. Method The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results Around 70,000 tweets were identified and analyzed from May to October 2018. Of the 100 most active Twitter accounts, organizations owned 52%, individuals owned 44%, and 4% of the accounts were unknown. Commercial/for-profit and nonprofit organizations were the most common organization account owners (i.e., 26% and 16%, respectively). Seven unique tweets were identified with a reach of over 400 Twitter users. The greatest reach exceeded 2,000 users. Temporal analysis identified retweet outliers (> 200 retweets per hour) that corresponded to a widely publicized event involving the response of a Twitter user to another user's joke. Content analysis indicated that Twitter is a platform that primarily functions to advocate, share personal experiences, or share information about management of tinnitus rather than to provide social support and build relationships. Conclusions Twitter accounts owned by organizations outnumbered individual accounts, and commercial/for-profit user accounts were the most frequently active organization account type. Analyses of social media use can be helpful in discovering issues of interest to the tinnitus community as well as determining which users and organizations are dominating social network conversations.