ArticleJournal of big data2022
Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to COVID-19: topic modelling and sentiment analysis.
Article in Journal of big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Low Battery Alarm; A Scoping Review of Autistic Burnout.Journal of autism and developmental disorders · 2026Article
- Sentiment and Topic Analysis of Autism Spectrum Disorder Discussions on Chinese Social Media: Evidence From Bilibili and Rednote.Journal of autism and developmental disorders · 2026Article
- Topic Modeling of Social Media Discourse of Autism Support Groups.Behavioral sciences (Basel, Switzerland) · 2026Article
- Comparing qualitative thematic analysis and machine-based topic modelling in the analysis of autistic and ADHD young people's accounts of emotions.Scientific reports · 2026Article
- Discovering patterns and trends in customer service technologies patents using large language model.Heliyon · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Characterizing Precision Nutrition Discourse on Twitter: Quantitative Content Analysis.Journal of medical Internet research · 2023Article
- Public concerns and attitudes towards autism on Chinese social media based on K-means algorithm.Scientific reports · 2023Article
- Article
- Digital trends in autism: a scoping review exploring coverage of autism across YouTube, Twitter, and Facebook.Frontiers in digital health · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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