Attention deficit hyperactivity disorder (ADHD) is a common disorder for young people that can persist into adulthood (Biederman et al., 2011; Spencer, Biederman, & Mick, 2007). It has been estimated to affect 5% or 7.2% worldwide (Polanczyk et al., 2007; Thomas et al., 2015). ADHD is characterized by inattention and/or hyperactivity and impulsiveness, although ADHD symptoms seem to be more heterogeneous and subtle in adults than children (Kessler et al., 2006). For example, unlike in children, gross motor hyperactivity is relatively rare in adults whereas inattentiveness is frequent (Wilens et al., 2009). ADHD is associated with impaired social, emotional, familial, academic, and behavioural functioning (Wehmeier, Schacht, & Barkley, 2010). Those with a diagnosis sometimes blame socially undesirable behaviour on themselves (Honkasilta, Vehmas, & Vehkakoski, 2016), potentially causing additional distress and psychological harm.
A recent systematic review of 101 relevant studies has argued that “relatively little” is known about the subjective perceptions of ADHD by diagnosed children (including adolescents) and their parents (Wong et al., 2018). Relevant findings from prior research into the perspectives of adults and children with ADHD are introduced in the Discussion section. Perceptions are important because they influence ADHD medication adherence (Gajria et al., 2014) and so a better understanding may help healthcare professionals to support effective treatment regimes. More knowledge about how people with ADHD cope with, and communicate about, their disorder may also help professionals give better advice to reduce anxiety (Pliszka, 2019) and depression (McQuade et al., 2011), promote self-acceptance (O’Connor et al., 2018) and cope with daily living challenges.
Social media sites have the potential to give insights into the lives of people with ADHD because posts can be used to share life issues as they occur (Chew & Eysenbach, 2010; Gruebner et al., 2017) and show how people communicate about them online. For example, an ethnographic content analysis of postings to ADHD Facebook groups found medication advice and jokes about perceptions of the condition (Gajaria et al., 2011) and an investigation of attitudes towards cannabis in an online forum found the first evidence of a belief that it was therapeutic (Mitchell et al., 2016). Whilst surveys and interviews can more directly interrogate the patient perspective and ensure that all participants have been diagnosed with ADHD (e.g. Weisner et al., 2018), social media analysis offers a complementary approach by investigating how people communicate about ADHD in an everyday setting.
Social media use among people with ADHD seems to be similar to that of the general population, at least for teens (Dawson et al., 2019), although possibly with fewer online friends (Mikami et al., 2015). People with ADHD have a higher risk of Internet addiction (Evren et al., 2018; Stavropoulos et al., 2019; Yen et al., 2007) and social media addiction (Andreassen et al., 2016), such as Facebook overuse (Gul et al., 2018; Settanni et al., 2018). Twitter is a logical choice for social media analysis because ADHD is one of the two most common health topic conditions tweeted about by users under the age of 18 (Sadah et al., 2016; Weeg et al., 2015). Tweets from users with ADHD discuss causes and use the pronoun “they” (indicating that the users frequently tweet about others, perhaps because of the importance of others’ reactions) and tentative language more often than average for people with nine other mental health conditions (Coppersmith et al., 2015), and common themes include, “emotional dysregulation, self-criticism, substance abuse, and exhaustion” (Guntuku et al., 2019).
Although the studies mentioned above have reported themes found in the tweets of people reporting an ADHD diagnosis, a more detailed exploration and different methods may gain additional insights into this complex disorder. This study addresses this possibility with the following research questions. The first is a general exploratory question whereas the second addresses ADHD tweets from a different perspective that may give complementary insights. The second method is the novel methodological contribution of this paper (the first time it has been applied to ADHD), and the first method is included to assess whether the second method applied to the same dataset gives genuinely different results.
What are the main themes of personal ADHD discussions on Twitter?
How are ADHD discussions different from other medical discussions on Twitter?
The research design was to gather a large sample of tweets relating to lived experiences of ADHD and use thematic analysis to develop core relevant themes (RQ1). Thematic analysis, described in more detail below, is an iterative qualitative method that identifies relevant themes from sets of texts (Braun & Clarke, 2006). It is suitable for exploratory analysis as a relatively theory-free qualitative method. For RQ2, personal tweets about a range of other disorders and diseases were gathered and compared with the ADHD tweets using a word association analysis (which identifies words used statistically significantly more in one set of texts than another, as explained below) to identify themes unique to ADHD.
This study was exempt from ethical approval at the University of Wolverhampton because it analysed only fully public (searchable, no logon required) texts so the tweeters have no reasonable expectation of privacy (Eysenbach & Till, 2001; Wilkinson & Thelwall, 2011). Nevertheless, the original tweets will not be shared, and exact quotes are avoided so that no individuals can be identified from this paper.
Tweets related to ADHD and 99 other conditions were gathered in parallel from Twitter from July 9, 2019 to February 3, 2020 using a curated set of queries. This date range gave a sufficiently large volume of ADHD-related tweets (58,893) and spanned summer holidays and school term time, which present different challenges for children with ADHD. The quoted query “my ADHD” (not case sensitive) was used to identify tweets that were likely to be discussing the tweeter's condition. Omitting the “my” would match tweets from researchers, parents, and the media, which are not relevant here. It captures an incomplete sample of disorder discussions because there are many ways in which a person can discuss their condition without using the phrase “my ADHD”. A person claiming to have ADHD may not have been diagnosed with it and therefore the tweets are from Twitter users that implicitly claim to have the disorder.
For the set of related tweets needed for RQ2, 99 other queries for doctors or common disorders and diseases were submitted as phrase searches starting with the word “my” (e.g. “my depression”, “my doctor”, “my allergies”, “my flu”, “my acne”, “my cancer”) (see Appendix for a list). Tweets matching these were combined to create a reference set of personal health-related tweets (n=1,341,442) for comparison with the ADHD tweets. Whilst this is not an exhaustive list, it encompasses a wide range of common conditions.
Two analyses were carried out and written up independently and blinded from each other to give method (and investigator) triangulation. The results were compared only after both had been completed (see the Discussion section for the comparison).
For analysis 1, three researchers (the last three authors) conducted a standard thematic analysis (Braun & Clarke, 2006, 2013) on a random set of 200 tweets. The tweets were analysed separately rather than in the context of users’ timelines because each tweet may appear separately on the timeline of readers rather than forming a coherent whole with any prior tweets. When a tweet was a reply to a previous message, Twitter was searched to find that message and add context, when necessary.
Rooted within social constructionism, this thematic analysis followed an inductive approach based upon Braun and Clarke's (2006) six-phase method. In phase 1, the three researchers read and re-read the dataset to become familiar with the content. In phase 2, the researchers independently generated initial codes, paying close attention to patterns of similarities and variation. At this stage, emerging topics and sub-topics were discussed. For instance, medication and coping tools were considered topics within a broader theme: “managing my ADHD”. This coding and classification of topics into themes is subjective and relies on the researchers’ interpretation of each tweet and their knowledge of the general subject area. Initial coding was informed by current literature on ADHD research (e.g. Frigerio & Montali, 2016; Ringer, 2020) and then guided by the tweets. In phase 3, the three researchers independently assigned one or more tentative themes to each tweet. In phase 4, the complete set of codes across all data were reviewed and combined, disagreements among the researchers were discussed until reaching a consensus on the themes and sub-themes assigned to each tweet. In phase 5, themes and sub-themes were further defined and named, here the aim was to identify the core message of each theme; according to Braun and Clarke (2013), this exercise allows for the development of “a concise, punchy, and informative name for each theme”. In phase 6, thematic narratives and examples (rephrased quotes to respect Twitter users’ anonymity) were discussed among the researchers and written up.
For the comparison between ADHD tweets and other health-related tweets, a word frequency test was used with the social media data analysis software Mozdeh to identify words that were more common in ADHD tweets than in tweets about the other conditions. This is a qualitative big data method that avoids the face validity and understanding limitations of quantitative big data approaches (Mills, 2018) with a final qualitative stage. A 2x2 chi-square test was used for each word to check if it was in a higher proportion of ADHD tweets than non-ADHD tweets. For example, the word
Since the above procedure gives a huge number of simultaneous statistical tests, false positives (words that seem to have an association with ADHD but do not) are highly likely. A Benjamini-Hochberg (Benjamini & Hochberg, 1995) adjustment (in Mozdeh) was therefore used to protect the familywise statistical false positive rate. Terms were retained only if they were judged to be statistically significant with p≤0.001 after the Benjamini-Hochberg procedure. Thus, the probability that there are no errors in the set of terms found to have a word association is at least p=0.999.
Over 1,000 words occurred statistically significantly more often in ADHD tweets than in the other health-related tweets. These words were put into themes using thematic analysis with a single coder (the first author), different from the coders used for the first analysis to give investigator triangulation. The coder read a random sample of tweets containing each term to check its context for the thematic classification. This continued until saturation was judged to have been achieved (i.e. no new themes were emerging), after 200 terms. After this, the codes were revisited, clustered into major themes and reclassified when necessary to produce a set of meaningful themes.
A loophole in this process is that a term might be judged statistically significant because it had been used repeatedly by one or more users, violating the assumptions behind the chi-squared tests. To check for this this, the test was run again after filtering out multiple posts from the same user (choosing one at random). Although some words failed this second test (@usernames and event-related #hashtags), all themes were still supported. Thus, the results are robust against repeated use of a term by one or more users.
The results of the two investigations are reported separately, as they were conducted, and are contrasted in the second half of the Discussion.
Four main themes were generated by the thematic analysis of a random sample of 200 tweets matching the query “My ADHD”: (1) My ADHD feels like; (2) Managing my ADHD; (3) Understanding, Support & Awareness of ADHD; and (4) Embracing my ADHD. Four subthemes were also identified for the second theme. Quotes given below are substantial paraphrases of the original tweets.
Within the sample of tweets,
In contrast, in some instances Twitter users seem to articulate a more positive narrative, often conveyed in a humorous way, in which they differentiate or try and
Tweets under the theme
Tweeters sometimes mention a lack of understanding from family and friends but also from society in general. Many ADHD tweeters feel that their family does not understand their disorder and how it affects them, despite their efforts to explain this; resulting in frustration when their parents blame the disorder on their children.
Within this theme, some tweets also reflect instances of poor treatment or lack of support from teachers and medical professionals and the resulting consequences, whereas some tweets also reveal how society's reaction can impact on a person's self-perception.
Some tweets are positive, showing how ADHD support and awareness should look like or how ADHD-related issues should be discussed or talked about more.
This theme is the least common within the sample and includes posts that reflect positive perceptions of ADHD, such as seeing it as a strength or superpower, and acceptance of ADHD or another co-morbid disorder, as part of everyday life or the ability to do something in a successful way despite the disorder.
The word association thematic analysis is based on 200 words that are in a statistically significantly higher percentage of tweets matching the query “My ADHD” than in the set of the comparable health-related tweets, and 19 themes were identified from them. Fifteen of these themes are listed below, together with an associated substantially modified quote, a description of the theme and examples of the terms that were classified as part of that theme. The final four themes are of little interest and are named but not described.
Several unsurprising themes also emerged:
Both studies have similar limitations derived from the common data source. There is sampling bias in the sense that not all people with ADHD use Twitter, and not all tweeters with ADHD use the phrase “my ADHD” in tweets about their condition. It is also possible that non-ADHD tweeters use the phrase “my ADHD” for humour or other purposes, generating false matches. Additional method-specific limitations are highlighted below in the comparison between methods. The results are discussed separately in relation to previous literature for each of the studies and then the two analyses are contrasted in the final section.
The themes identified from Analysis 1 confirm, but sometimes with a different perspective, the results of prior studies. Previous research noticed the need for people with ADHD to discuss their condition (Theme 1:
For Theme 3,
Themes that are non-trivial and different from Analysis 1, or giving a different perspective, are discussed here. Not all have been noted in previous research. The need for people to understand the
Two previous studies have noted formation issues related to ADHD that could be used to explain the
The importance of a
The
The likelihood of
The themes emerging from the two analyses overlap to some extent but not completely (Figure 1). From Analysis 1, the broad Theme 1 (
From Analysis 1, the broad Theme 2 (
The third and fourth Analysis 1 broad themes seemed to match some of the Analysis 2 themes in all respects. The term
Several of the remaining Analysis 2 themes did not match Analysis 1 themes. The relatively trivial themes can be ignored (
Based on the above comparison, the standard thematic analysis method is able to identify some themes that do not translate into language use differences, for example due to language use variations within the theme (e.g. perhaps many different words are used to create humour or express positivity). In addition, the standard thematic analysis method may identify themes that are important but not unique to the topic. For example, the two coping themes found (humour and other tools/mechanisms) seem likely to be important for many health conditions and may therefore not produce words unique to ADHD. Thus, word association thematic analysis may overlook generic issues. In contrast, the word association method is better able to identify linguistic themes and can identify themes that may be overlooked without benchmark data to compare against.
Comparing the word association thematic analysis to the standard thematic analysis from a methodological perspective may shed light on any differences between the two methods. Whilst only one standard theme or subtheme (Medication) was identical to a word association theme, most themes overlapped to some extent. In general, the first method found more general themes and the second found more themes. In principal a standard thematic analysis could have produced more themes, making them more specific, perhaps by analysing additional texts. In contrast, the focus on words in the second method probably makes it difficult to generate larger themes because each word-based theme is relatively discrete, based on a set of words, and therefore relatively different from other themes based on different sets of words. Thus, standard thematic analysis may be inherently better at detecting general themes. In contrast the word association thematic analysis found more specific themes that could easily be overlooked without a benchmark collection or that may have been overlooked because they did not occur often enough to be recognised from reading texts. An advantage of the word association method here is in finding differences that are statistically significant and need to be explained (put into a theme), drawing the attention of the analyst to information that might otherwise be overlooked.
Despite the above argument, the differences between the two analyses cannot be conclusively shown to be methodological rather than accidental or due to different coders. It would be unreasonable to use the same coders with the two methods to compare the methods directly, however, since they could not forget the first method results when applying the second. Thus, the results suggest, but do not prove, that the novel method (in this context) can give novel insights compared to existing methods.
The themes found by the two analyses point to a wide variety of dimensions from which to view the perspective of those that implicitly claim to have ADHD on Twitter. This list of themes may help professionals to get insights into the patient perspective in a way that may enrich an understanding of the symptoms, or support discussion-based interventions (e.g. Gisladottir & Svavarsdottir, 2017).
The themes that do not seem to have been explicitly remarked on in previous analyses of patient perspectives are particularly important. The tendency of people with ADHD to distance or detach themselves from their brains as a strategy to understand their behaviour or explain it to others seems to be widely adopted, suggesting that it is a useful coping strategy. Communicating about behaviour to others, particularly in terms of the appropriate target of blame—the disorder or “hellbrain”, also seems important and a useful strategy. Finally, the concept of neurodivergence seemed to help people with ADHD to be positive about their condition.