Mental illness and bipolar disorder on Twitter: implications for stigma and social support

Budenz, Alexandra; Klassen, Ann C.; Purtle, Jonathan; Tov, Elad Yom; Yudell, Michael; Massey, Philip M.

doi:10.1080/09638237.2019.1677878

Cited by 68 publications

(50 citation statements)

References 65 publications

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“…The main goal of this study was to infer how mental health is understood and used by the general public on Twitter. Our findings suggest that, as opposed to a more problematic framing of mental health by the traditional media channels, the online environment is tilted toward mental health awareness, while mental health stigma is mostly related to trivialization and self-stigma, rather than danger and violence similar to findings for the specific mental health disorders -see Budenz et al (2019), Reavley and Pilkington (2014). We identified that 'Awareness' framing is engaged by a broader public than 'Stigma', and that when 'Stigma' framing does relate to violence and danger, it tends to do so in a politicized discourse (gun laws, justifications for funding, etc.…”

Section: Empirical Findingssupporting

confidence: 53%

“…However, these unique frames might be consistent frames across discourses on different mental health disorders. For example, research has shown that stigma-related frames are common in discussing certain mental health disorders (e.g., schizophrenia, bipolar disorder) (Budenz et al, 2019;Robinson et al, 2019).…”

Section: Individual Framesmentioning

confidence: 99%

“…On the one hand, the findings showed that mental illness related discourse on Twitter was more supportive and neutral than that of the traditional media, focused on the increased awareness, sharing research, and other consumer resources. On the other hand, Twitter discourse was not immune from more stigmatizing frames, even though these were somewhat different -more focused on trivialization of mental health problems (e.g., "snapping out of it") or sharing of inaccurate beliefs (e.g., confusion of schizophrenia with the bipolar disorder) (Budenz et al, 2019;Joseph et al, 2015;Reavley & Pilkington, 2014;Robinson et al, 2019). While these studies can give an idea about the framing of mental health discourse, the research objectives have either been focused on specific mental health disorders or assessed levels of stigma.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

“Mental Health” as Defined by Twitter: Frames, Emotions, Stigma

Pavlova

Berkers

2020

Health Communication

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Section: Empirical Findingssupporting

confidence: 53%

Section: Individual Framesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“Mental Health” as Defined by Twitter: Frames, Emotions, Stigma

Pavlova

Berkers

2020

Health Communication

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“…To examine the association between the negative tweets and the four main categories of social characteristics, i.e., income, employment, education, and crime, this research used the logistic regression model in keeping with previous studies on social media analysis [44,45]. The dependent variable used binary coding to represent being negative (code 1, negative) for a tweet's emotions (see Equation 3).…”

Section: Social Characteristics Associated With Negative Tweetingmentioning

confidence: 99%

Space-Time Surveillance of Negative Emotions after Consecutive Terrorist Attacks in London

Dai

2020

IJERPH

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Terrorist attacks pose significant threats to mental health. There is dearth information about the impact of consecutive terrorist attacks on space-time concentrations of emotional reactions. This study collected (1) Twitter data following the two terrorist attacks in London in March and June of 2017, respectively, and (2) deprivation data at small areal levels in the United Kingdom. The space-time permutation model was used to detect the significant clusters of negative emotions, including fear, sadness, and anger in tweets. Logistic regression models were used to examine the social deprivation of communities associated with negative tweeting. The results reported two significant clusters after the March attack, one was in London, ten days after the attack, and the other was far from the attack site between Manchester and Birmingham, three days after the attack. Attention to the reoccurring attack in June diminished quickly. The socially deprived communities experienced double disadvantage—sending fewer tweets but expressing more negative emotions than their counterparts. The findings suggest that terrorism can affect public emotions far and broad. There is a potential for surveillance to rapidly identify geographically concentrated emotions after consecutive or prolonged disasters using social media data.

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“…Journal of Data and Information Science et al, 2008;Sciutto, Terjesen, & Frank, 2000). Misconceptions regarding ADHD among healthcare professionals (Julivia Murtani et al, 2020) and society (Gilmore, 2010;Pescosolido et al, 2008) have also been reported, although Twitter itself seems to be overall supportive towards mental illness (Budenz et al, 2020). This lack of understanding from others is often perceived as the most negative thing about having ADHD (Gajaria et al, 2011).…”

Section: Research Papermentioning

confidence: 99%

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

Thelwall

Makita

Stuart

2020

Journal of Data and Information Science

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PurposeAttention deficit hyperactivity disorder (ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs.Design/methodology/approachTweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1: A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.FindingsThe themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image.Research limitationsThis is a single case study and the results may differ for other topics.Practical implicationsHealth professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.Originality/valueWord association thematic analysis can give new insights into the (self-reported) patient perspective.

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Mental illness and bipolar disorder on Twitter: implications for stigma and social support

Cited by 68 publications

References 65 publications

“Mental Health” as Defined by Twitter: Frames, Emotions, Stigma

“Mental Health” as Defined by Twitter: Frames, Emotions, Stigma

Space-Time Surveillance of Negative Emotions after Consecutive Terrorist Attacks in London

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

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