Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study

Hswen, Yulin; Gopaluni, Anuraag; Brownstein, John S.; Hawkins, Joseph E.

doi:10.2196/12264

Cited by 48 publications

(37 citation statements)

References 37 publications

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“…A higher number in “analytic” reflects formal, logical, and hierarchical thinking. These results are not consistent with Hswen et al (2019) who found the emotional patterns consistent with the diagnosis of ASD in tweets of self-identified individuals. This difference could be attributed to the samples in each study.…”

Section: Discussioncontrasting

confidence: 96%

“…Beykikhoshk et al (2015) compared ASD-related tweets with non-ASD-related tweets and identified differences in the types of words used. Of interest, the words “son” and “boy” occurred with higher frequency than “female” and “girl.” Hswen et al (2019) examined the feasibility of using Twitter as a platform to enhance the diagnosis of ASD. The authors extracted Twitter data from individuals who self-identified with ASD and analyzed text content and timing of tweets.…”

Section: Introductionmentioning

confidence: 99%

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Twitter usage about autism spectrum disorder

Bellon-Harn

Manchaiah

2020

Autism

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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.

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Section: Discussioncontrasting

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Twitter usage about autism spectrum disorder

Bellon-Harn

Manchaiah

2020

Autism

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“…Previous work incorporating Twitter data has proven useful in early recognition and characterization in a public emergency ( Cassa et al, 2013 ) and sleep profiles possibly linked with psychosocial issues ( McIver et al, 2015 ), influenza predictions ( Nagar et al, 2014 ) and identification of food poisoning ( Harris et al, 2017 ). Additionally, Twitter data analysis has been consistent with clinical characteristics of autism spectrum disorder ( Hswen et al, 2019 ) and schizophrenia ( Hswen, Naslund, Brownstein, & Hawkins, 2018a , 2018b ). Literature suggests the potential to leverage Twitter data in order to support public health initiatives such as early illness detection ( Hswen et al, 2018a , 2018b ) and suicide prevention ( Hswen et al, 2019 ).…”

Section: Introductionsupporting

confidence: 56%

Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis

Huerta

Hawkins

Brownstein

et al. 2021

SSM - Population Health

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As policies are adjusted throughout the COVID-19 pandemic according to public health best practices, there is a need to balance the importance of social distancing in preventing viral spread with the strain that these governmental public safety mandates put on public mental health. Thus, there is need for continuous observation of public sentiment and deliberation to inform further adaptation of mandated interventions. In this study, we explore how public response may be reflected in Massachusetts (MA) via social media by specifically exploring temporal patterns in Twitter posts (tweets) regarding sentiment and discussion of topics. We employ interrupted time series centered on (1) Massachusetts State of Emergency declaration (March 10), (2) US State of Emergency declaration (March 13) and (3) Massachusetts public school closure (March 17) to explore changes in tweet sentiment polarity (net negative/positive), expressed anxiety and discussion on risk and health topics on a random subset of all tweets coded within Massachusetts and published from January 1 to May 15, 2020 (n = 2.8 million). We find significant differences between tweets published before and after mandate enforcement for Massachusetts State of Emergency (increased discussion of risk and health, decreased polarity and increased anxiety expression), US State of Emergency (increased discussion of risk and health, and increased anxiety expression) and Massachusetts public school closure (increased discussion of risk and decreased polarity). Our work further validates that Twitter data is a reasonable way to monitor public sentiment and discourse within a crisis, especially in conjunction with other observation data.

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“…Tweets are limited to 280 characters and have been recognized as a source of organic sentiment and opinions [ 25 ]. The Twitter platform has been widely recognized as a source to monitor public sentiment across a spectrum of health-related issues, including mental health [ 26 - 28 ], vaccination [ 29 ], and smoking [ 30 ]. Most recently, Twitter has emerged as a potential source of information for capturing hospital and health care experiences of sexual minorities [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter

Hswen¹,

Hawkins²,

Sewalk³

et al. 2020

J Med Internet Res

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Background Racial and ethnic minority groups often face worse patient experiences compared with the general population, which is directly related to poorer health outcomes within these minority populations. Evaluation of patient experience among racial and ethnic minority groups has been difficult due to lack of representation in traditional health care surveys. Objective This study aims to assess the feasibility of Twitter for identifying racial and ethnic disparities in patient experience across the United States from 2013 to 2016. Methods In total, 851,973 patient experience tweets with geographic location information from the United States were collected from 2013 to 2016. Patient experience tweets included discussions related to care received in a hospital, urgent care, or any other health institution. Ordinary least squares multiple regression was used to model patient experience sentiment and racial and ethnic groups over the 2013 to 2016 period and in relation to the implementation of the Patient Protection and Affordable Care Act (ACA) in 2014. Results Racial and ethnic distribution of users on Twitter was highly correlated with population estimates from the United States Census Bureau’s 5-year survey from 2016 (r2=0.99; P<.001). From 2013 to 2016, the average patient experience sentiment was highest for White patients, followed by Asian/Pacific Islander, Hispanic/Latino, and American Indian/Alaska Native patients. A reduction in negative patient experience sentiment on Twitter for all racial and ethnic groups was seen from 2013 to 2016. Twitter users who identified as Hispanic/Latino showed the greatest improvement in patient experience, with a 1.5 times greater increase (P<.001) than Twitter users who identified as White. Twitter users who identified as Black had the highest increase in patient experience postimplementation of the ACA (2014-2016) compared with preimplementation of the ACA (2013), and this change was 2.2 times (P<.001) greater than Twitter users who identified as White. Conclusions The ACA mandated the implementation of the measurement of patient experience of care delivery. Considering that quality assessment of care is required, Twitter may offer the ability to monitor patient experiences across diverse racial and ethnic groups and inform the evaluation of health policies like the ACA.

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Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study

Cited by 48 publications

References 37 publications

Twitter usage about autism spectrum disorder

Twitter usage about autism spectrum disorder

Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis

Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter

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