“My ADHD Hellbrain”: A Twitter Data Science Perspective on a Behavioural Disorder

Thelwall, Mike; Makita, Meiko; Stuart, David

doi:10.2478/jdis-2021-0007

Cited by 9 publications

(7 citation statements)

References 63 publications

(78 reference statements)

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“…In this case the texts are Twitter self-descriptions and the subsets are self-descriptions created by different genders or gender groups. This is more appropriate than content analysis or thematic analysis because it can identify fine-grained differences at a level that would not be identified by human coders (Thelwall et al, 2021). The method has three stages (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Thelwall

Fairclough

2021

Journal of Data and Information Science

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PurposeAlthough gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of difference, potentially overlooking other differences and ignoring nonbinary users.Design/methodology/approachWord association thematic analysis was used to systematically check for fine-grained statistically significant gender differences in Twitter profile descriptions between 409,487 UK-based female, male, and nonbinary users in 2020. A series of statistical tests systematically identified 1,474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes.FindingsThe results reflect offline variations in interests and in jobs. They also show differences in personal disclosures, as reflected by words, with females mentioning qualifications, relationships, pets, and illnesses much more, nonbinaries discussing sexuality more, and males declaring political and sports affiliations more. Other themes were internally imbalanced, including personal appearance (e.g. male: beardy; female: redhead), self-evaluations (e.g. male: legend; nonbinary: witch; female: feisty), and gender identity (e.g. male: dude; nonbinary: enby; female: queen).Research limitationsThe methods are affected by linguistic styles and probably under-report nonbinary differences.Practical implicationsThe gender differences found may inform gender theory, and aid social web communicators and marketers.Originality/valueThe results show a much wider range of gender expression differences than previously acknowledged for any social media site.

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Section: Methodsmentioning

confidence: 99%

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Thelwall

Fairclough

2021

Journal of Data and Information Science

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“…This has two advantages: a time series analysis of trends, and the possibility to gather enough tweets to analyse topics that are rarely discussed. In the past, long term monitoring of Twitter would have been needed to capture relevant tweets (e.g., Thelwall et al, 2021), but now it is quick and straightforward to capture them retrospectively.…”

Section: Discussionmentioning

confidence: 99%

Researchers’ attitudes towards the h-index on Twitter 2007–2020: criticism and acceptance

Thelwall

Kousha

2021

Scientometrics

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The h-index is an indicator of the scientific impact of an academic publishing career. Its hybrid publishing/citation nature and inherent bias against younger researchers, women, people in low resourced countries, and those not prioritizing publishing arguably give it little value for most formal and informal research evaluations. Nevertheless, it is well-known by academics, used in some promotion decisions, and is prominent in bibliometric databases, such as Google Scholar. In the context of this apparent conflict, it is important to understand researchers' attitudes towards the h-index. This article used public tweets in English to analyse how scholars discuss the h-index in public: is it mentioned, are tweets about it positive or negative, and has interest decreased since its shortcomings were exposed? The January 2021 Twitter Academic Research initiative was harnessed to download all English tweets mentioning the h-index from the 2006 start of Twitter until the end of 2020. The results showed a constantly increasing number of tweets. Whilst the most popular tweets unapologetically used the h-index as an indicator of research performance, 28.5% of tweets were critical of its simplistic nature and others joked about it (8%). The results suggest that interest in the h-index is still increasing online despite scientists willing to evaluate the h-index in public tending to be critical. Nevertheless, in limited situations it may be effective at succinctly conveying the message that a researcher has had a successful publishing career.

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“…Previously, researchers analyzed how ADHD users construct their sentences and identified their preferences for using first-person narrative and negative-tone words in tweets [ 18 ]. Other scholars conducted a case study to explore ADHD users’ concerns about their disorder [ 19 ]. However, it is still incomprehensive and unclear as to how ADHD users behave and interact on Twitter.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions

Chen¹,

Jeong²,

Simpkins³

et al. 2023

J Med Internet Res

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Background With the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media. Objective This study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets. Methods First, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users’ posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups. Results In contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001). Conclusions This study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection.

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“My ADHD Hellbrain”: A Twitter Data Science Perspective on a Behavioural Disorder

Cited by 9 publications

References 63 publications

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration

Researchers’ attitudes towards the h-index on Twitter 2007–2020: criticism and acceptance

Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions

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