Daejeon, July 27–31
Established in 2014, r/bpdlovedones, a subreddit on the content sharing platform Reddit, introduces itself as "a support forum and safe space for people to discuss the challenges and abuse they have endured at the hands of someone who has BPD [borderline personality disorder]". It currently has 115,000 members and is listed on the site as being in the top 2% of all subreddits in terms of membership, with an observed yearly increase of 18,000 members (GummySearch, 2025). Despite the growing significance of online peer-to-peer support communities for mental health, and clinical appeals that mental health initiatives should "actively engage and harness the support of family, friends and significant others" (Acoba 2024), little linguistic research has explored how social intimates of people with prevalent mental health conditions use language to construe symptoms and behaviours. This paper addresses this gap through a corpus linguistic methodology that asks: What unique words distinguish language on r/bpdlovedones from other subreddits? In doing so, it highlights the ideological functions of neologisms (‘invented words’) in online health communities and how these neologisms serve to 'package up' problematic lay assumptions about mental health and how they potentially blur the boundaries between clinical and lay knowledge. This approach advances beyond existing corpus studies of online health communities by uniquely combining large-scale keyness analysis (what are the unusually frequent words in this dataset?) with systematic examination of how information becomes packaged up in noun phrases, a methodological innovation that reveals how neologisms encode ideological assumptions at multiple linguistic levels simultaneously.
People with BPD comprise a significantly disadvantaged group which highlights the high stakes involved. Despite the fact that BPD affects approximately 1% of the population, it carries a suicide risk 45 times higher than the general population, accounting for 9-33% of all suicides (Chesney et al 2014). However, people with BPD face more severe stigma than those with other mental health conditions, with research showing they are less likely to elicit sympathy and more likely to be perceived as manipulative even by psychiatric professionals (Furnham et al, 2015; Klein et al 2022). This stigma occurs alongside clinical evidence that strong family support improves outcomes for people with BPD (Infurna et al 2016), though social network analysis reveals that women with BPD tend to have smaller networks with more reported conflict (Lazarus and Cheavens 2017). Understanding how social intimates linguistically construct BPD-related experiences is therefore crucial for helping to improve clinical outcomes for people with BPD.
This study adopts a corpus-assisted critical stylistic approach (McIntyre and Walker 2019), combining the empirical rigour of corpus linguistics with the interpretative precision of critical stylistics (Jeffries 2010). Following established practice in corpus approaches to health discourse (e.g. Brookes and Hunt, 2020; Hunt and Harvey, 2015) the study first employs keyness statistics to identify lexical items that are significantly more frequent in r/bpdlovedones than a representative corpus of all other subreddits. The target corpus comprises posts and comments contributed to r/bpdlovedones between 2011 and 2019, a dataset totalling 19,444,886 words. The reference corpus consists of data from 100 highly active subreddits drawn from the Cornell ConvoKit Reddit corpus (2020), totalling 69,428,488 words. Keywords are ranked by log-ratio (highlighting the words with the highest frequency difference between the two corpora) and a log-likelihood statistic is applied to ensure these differences are statistically significant. Following other established protocols in corpus-assisted discourse studies (e.g. Partington et al 2013), collocation analysis (unusually frequently co-occurring words) of keywords is conducted (ranked by MI3), and quantitative patterns are examined in more qualitative detail via concordance lines and extended contexts to understand how distinctive terms function within the community's discourse.
This represents the most comprehensive corpus-driven analysis of mental health discourse on Reddit to date, applying established CADS protocols to an unprecedented dataset scale while innovating through multi-level analysis that traces neologisms from quantitative frequency patterns through morphological structure to their ideological function.
The paper begins by showing how the keyness analysis reveals a distinctive and institutionalised lexicon of neologisms that function to construct what can be termed a "folk symptomatology", a lay diagnostic framework that parallels but sometimes extends beyond or contradicts clinical definitions. The analysis first isolates those keywords that are pseudo-technical and do not occur in the reference corpus at all (n = 45). It then proceeds to identify those that appear to originate from either the subreddit itself or the related subreddits (n = 22). High strength keywords include pwbpd, meaning "person with BPD" (LR = 139.02) and ubpd, meaning "undiagnosed [person with] BPD" (LR = 135.98). The word my functions as a top 10 collocate for most neologisms indicating that these neologisms often occur within personal narratives (e.g. 'when I lived with my pwBPD...'). However, another top 10 collocate for both pwbpd and ubpd, that, reveals how users also legitimise their identified lay symptoms in relation to previous contributions (e.g. 'my exBPD also raged at me like that'). The presence of the definite article (the) as a strong collocate of most high frequency neologisms (e.g. the pwbpd, the lovebomb) suggests a process where lay symptoms gradually become reified within the discourse as a shared "folk symptomatology".
However, the analysis then calls the accuracy of the folk symptomatology into question by revealing how clinical and lay diagnoses become blurred. Drawing on Jeffries' (2010) framework of 'packaging up' information in noun phrases, the analysis proceeds to examine a range of key terms which refer to "undiagnosed diagnoses", where ‘U’ stands for “undiagnosed” (i.e. ubpd, exubpd, exwubpd, pwubpd, ubpdex, upwbpd). Collocation analysis of comparable neologisms (ubpdex and bpdex) shows how (seemingly) clinical and lay-diagnoses are used in identical ways. For instance, the seven strongest collocates for the two terms, while ranked differently, are otherwise identical (my, with, was, and, the, to, me, of). Finally, the analysis proceeds to carry out a closer critical linguistic analysis of ubpd and related terms, highlighting how the 'lay' nature of these diagnostic terms is textually 'packaged up'. Namely, the "undiagnosed" meaning is packaged up (1) morphologically as a derivational prefix (un-), (2) as a modifier in the noun multi-word string (undiagnosed BPD), (3) initialised (UBPD), and (4) as a modifier of another noun (my ubpd wife). Thus, the large scale quantitative analysis of the entire dataset and closer stylistic analysis both work together to show how problematic assumptions become increasingly buried in institutionalised lexicon of neologisms of the subreddit as taken for granted assumptions.
The analysis shows how users of r/bpdlovedones use neologism to distil a 'folk symptomatology' from shared personal narratives. However, because packaging up (e.g. ubpd, exubpd) simultaneously obscures the lay origins of diagnostic labels, blurring the boundaries between clinical and unverified diagnoses, the legitimacy of the narrative pool from which this folk symptomatology is distilled is called into question. This gives rise to a potentially destructive feedback loop: as lay-diagnosed cases enter the pool, the folk symptomatology expands to encompass an ever-broader range of behaviours reified as core symptoms, which may in turn encourage further lay diagnosis. This may partly explain the high activity and growing membership of the site.
This research advances digital humanities scholarship in three significant ways. First, it represents the largest corpus-driven study of healthcare discourse on Reddit to date (19.4 million words). Second, it is the first study to systematically examine neologism formation and function in mental health forums, revealing mechanisms of folk symptomatology construction previously unexplored in corpus linguistics. Third, methodologically, it innovates by integrating multi-level linguistic analysis, from corpus keyness statistics through morphological packaging to stylistic interpretation, providing a replicable framework that advances beyond current single-level approaches to online health discourse. Theoretically, the analysis reveals how neologism can function ideologically to render the lay origins of informal diagnostic frameworks covert, with implications for understanding how online communities engage with and transform medical knowledge more broadly. This has implications for understanding digital health literacy and the democratisation of medical knowledge online.
For mental health research and practice, the findings raise important questions about how online support communities might inadvertently reinforce stigma while attempting to provide support. The paper does not aim to criticise individual users or dismiss the genuine need these communities serve, but rather to critically reflect on how dominant linguistic patterns might be at odds with the goals of supporting recovery (see Acoba, 2024). Understanding these dynamics is crucial for developing more effective approaches to involving social intimates in mental health care. Finally, this research contributes to broader discussions about expertise and knowledge construction on social media and digital spaces more broadly. The development of folk symptomatologies represents a form of collective knowledge production that exists in tension with professional medical frameworks. Understanding this tension is essential as healthcare increasingly intersects with digital media and as patients and their social intimates play more active roles in seeking out and sharing information about health.