In 2018, the Lancet Commission on global mental health and sustainable development reported that the global burden of disease related to mental health disorders has risen in all countries and that mental health services are frequently of a lower quality than those provided for physical health [1]. The 2013 Global Burden of Disease study [2] described depression as the predominant mental health problem worldwide, followed by anxiety, schizophrenia, and bipolar disorder, and the 2019 Global Burden of Disease study suggested that 1.2% (>815,000 cases) of the UK population has been diagnosed with bipolar disorder [3]. Bipolar disorder is a mood disorder associated with recurring episodes of extreme moods, ranging from severe depression to mania and with episodes lasting up to weeks at a time. Bipolar disorder has been shown to affect psychosocial functioning in areas of work, finance, cognition, and relationships [4], and people living with bipolar disorder are at a high risk for self-harm [5]. Of those diagnosed with bipolar disorder, 10%-20% will die by suicide, and therefore, the prevention of future episodes and the management of depressive and manic episodes are the major goals of treatment in bipolar disorder [6]. It is difficult to understand the lived experience of bipolar disorder through clinical practice alone, primarily because clinicians may only see their patients under a restricted set of conditions and those are likely to frame any discussion about the experiences of the patients.

The clinical diagnosis of bipolar disorder is a lengthy and costly process that takes an average of 9 years to complete [7]. A delayed diagnosis can have major implications for misdiagnosed individuals and may lead to inadequate or inappropriate treatments, a greater severity and frequency of mood episodes, and an increased risk for suicide among individuals who are later diagnosed with bipolar disorder [8]. Considering the economic implications, it is estimated that the total costs associated with bipolar disorder in the United Kingdom, including service costs and lost employment costs, could reach £8.2 billion (US $10.6 billion) by 2026 [9].

The World Health Organization states that health systems must do more to respond to the burden of mental health disorders and that many people living with mental illness do not receive the care that they need. The development of strengthened information systems to provide evidence for population health monitoring and mental health surveillance is 1 of the 4 major objectives of the World Health Organization Mental Health Action Plan 2013-2020 [10]. The Lancet Commission also stated that digital technology can be used both to provide support and tools to people living with mental illness and to facilitate the screening and diagnosis of mental disorders using big data approaches. The increasing use of social media combined with the computational infrastructure of health care systems in the advent of the maturation of natural language processing (NLP) and machine learning (ML) technologies [11] provides exciting possibilities to investigate large amounts of data at the population and individual level.

Le Glaz et al [12] explained how language plays an important role in mental health technologies and how NLP uses the language resources available to analyze text both qualitatively and quantitatively to provide deeper insights into these data. NLP methods can focus on various features, including lexical choices, syntax, and semantics, to perform tasks such as topic modeling, clustering, and classification. Le Glaz et al [12] described that NLP in mental health research comprises the following four main stages: (1) corpus creation—the most common corpora include electronic health records (EHRs), social media data (eg, Reddit and Twitter posts), and transcribed patient interviews; (2) corpus processing—extracting medical terms or processing blocks of language using specific searches; (3) classification methods—ML techniques including deep learning; and (4) goal—the ultimate goal of validating a hypothesis or studying the behavior of a specific population.

Mental health research related to bipolar disorder can benefit from NLP methods in several ways. First, large amounts of longitudinal data from health records can be analyzed to provide population-level insights and to contribute to the creation of semiautomated systems, for example, to improve the specificity and speed of diagnosis [13,14]. Second, NLP methods can also be used for more fine-grained analyses at an individual level by analyzing lived experience accounts of bipolar disorder. This could include monitoring the sentiment and effect of web-based interactions over time [15], using textual cues in web-based communication to shed light on language features that relate to a bipolar disorder diagnosis [16], or using emotion detection methods to learn more about how emotions fluctuate over time [17]. Using NLP methods in the study of bipolar disorder could contribute to greater personalization of care through in-depth analysis of large amounts of textual data [18] and may yield insights that would be difficult to obtain in a formal health care setting owing to financial and time constraints. Analyzing the language used in nonclinical settings also provides an opportunity to learn more about what people with bipolar disorder say unprompted in situations that are not framed by clinicians or researchers. Becker et al [19] suggested that there is a need for a common language between the data science community and the health care community. This common language would enable data scientists to understand the technologies that are needed and how these can be implemented with clients, and enable health care workers to understand technical capabilities and the type of data that is most useful in developing automated systems. Carr [20] also explained that patient and public involvement and the incorporation of knowledge of domain experts (such as people with personal experience of bipolar disorder) are vital for ethical decision-making, because it enables a more robust understanding of language and context.

To understand how NLP methodologies have been used to better understand bipolar disorder, we conducted a scoping review. Scoping reviews enable the researcher to present an overview of a diverse body of literature and allow for the synthesis of a range of study designs and methodologies without narrowing it down to a focused research question as in a systematic review [21]. The goal of this scoping review is in line with the definition by Daudt et al [22], which states that a scoping review aims “to map the literature on a particular topic or research area and provide an opportunity to identify key concepts; gaps in the research; and types and sources of evidence to inform practice, policy making, and research.”

An initial broad search of the published literature suggested that no previous studies have systematically reviewed the literature describing how NLP has been used to better understand bipolar disorder. Although there are reviews that have focused on the use of ML methods or big data in the study of bipolar disorder [23,24] or the use of ML and NLP for mental health more widely [12,25,26], this scoping review focused specifically on bipolar disorder and the application of NLP methods to this condition.

This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and also to identify which aspects of bipolar disorder are underresearched and could be aided by computational linguistic methods (a definition of terms can be found in Multimedia Appendix 1 [27,28].

The four research questions that were used to guide this scoping review were as follows:

This scoping review was conducted with reference to the framework proposed by Arksey and O’Malley [29], expanded by Levac et al [30] and Daudt et al [22], and was informed by the guidance provided in the Joanna Briggs Institute (JBI) manual for evidence synthesis in scoping reviews [31]. This scoping review has been reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [32] and was also informed by the guidance provided in the JBI manual for evidence synthesis in scoping reviews [33].

A systematic and computerized search was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology. The search was conducted between January 25, 2021, and August 27, 2021, and the search strategy was developed with informed advice from a topic librarian. There were no restrictions on the date of publication.

The search strategy used index terms and free-text terms to cover two core themes: (1) bipolar disorder and (2) NLP. Adjacency operators were used when incorporating free-text terms to ensure the specificity of the returned results. The full search terms are shown in Figure S1 in Multimedia Appendix 2 [13-17,34-66].

The final list of studies that were eligible for screening was imported into the Mendeley Reference Manager for duplicate removal before it was uploaded to Covidence [67], which was used for abstract and full-text screening and data extraction. Citation chaining was conducted on the final set of full-text papers used in this review.

Only papers written in English and published as peer-reviewed papers, full-text workshops, or conference proceedings were included in this review. It should be noted that the need for a faster review process has made conference proceedings the dominant form of published research in Computer Science and NLP [68]. To be included, studies needed to explicitly describe the application of an NLP method to the study of bipolar disorder (including those studies in which bipolar disorder was one of multiple psychological disorders being studied, but only when the data for bipolar disorder were separable). Studies that described quantitative, qualitative, or mixed method designs were eligible for inclusion, and papers were only included if they described completed research. Study designs and protocols were excluded from the study.

Papers were excluded from the scoping review if they only included an abstract and if the methodology described ML, deep learning, or big data approaches that did not rely on language features, for example, using magnetic resonance imaging data for bipolar disorder classification. Systematic and scoping review papers were also excluded from this study.

Initial screening of the titles and abstracts was conducted independently by the lead reviewer (DH) and the second reviewer (AW) using Covidence [67] to assess the suitability of the studies identified during the search for inclusion in the review. The eligibility criteria were tested in a pilot of 25 studies to ensure that the criteria were suitable for the review. The JBI [33] recommends that an agreement of 75% demonstrates that the inclusion and exclusion criteria performed well, and the agreement between the first and second reviewers for the pilot screening was 84%. After establishing that the eligibility criteria were valid, the reviewers screened the remaining papers independently and resolved any conflicts through discussion.

For all papers that passed the title and abstract screening, the lead reviewer located the full texts and screened them for eligibility in the review. The second reviewer (AW) screened 20.4% (23/113) of the papers at the full-text screening stage to verify their inclusion or exclusion, and a 100% agreement was achieved between the first and second reviewers. Data were extracted from the final papers that were eligible for inclusion by the first reviewer (DH) using a customized data extraction template that was designed and implemented in Covidence [67] and is shown in Table S5 in Multimedia Appendix 2. The data extraction template was piloted with 3 papers by the lead reviewer and was verified for accuracy by another member of the review team (PR). Minor changes were recommended for the data extraction template, including changing the phrasing of some fields and adding two fields that measured the reproducibility of computational linguistic papers—whether the authors released their code and data set with the paper. A reviewer (DH) extracted all data from the 35 included articles. The extracted data are available in Table S1 in Multimedia Appendix 2.

To enhance the transparency of the research process, as recommended by the JBI [33], the protocol for this scoping review has been registered with Figshare and is available for public access [69].

A narrative synthesis [70] of the included studies was undertaken to map the literature as outlined in the research questions. The data were presented using descriptive frequency tables and charts and summarized according to inductively developed objectives.

The initial search yielded 507 documents after deduplication. Of these, 394 (77.7%) were excluded after title and abstract screening because of ineligibility, leaving 113 (22.2%) for full-text review. After full-text screening, a further 81 (71.6%) articles were excluded. The reasons for the exclusions are shown in Figure 1. After full-text screening, 32 (91%) papers were included in the review, and 3 (9%) additional papers were included after citation screening of these papers, totaling 35 papers for inclusion in the scoping review. The results of the search and the study inclusion process are presented in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart in Figure 1 [71].

Because of the broad variation in the specific aims of each article and the ever-increasing number of NLP tools available to researchers, there is large variation in the tools and methods that were used in the included papers. The following subsections group the articles using the aforementioned 4 objectives, describe the methods identified across the articles, and provide a qualitative summary of the results. Table S1 in Multimedia Appendix 2 provides more fine-grained details of the methods and results reported for each article in this review.

It is important to understand why NLP methods have been applied to the study of mental health conditions and if this type of research is grounded in real-life implementations, particularly when large amounts of potentially sensitive social media data have been used.

The articles used in this review cited various reasons that make this type of study clinically relevant. Many authors have suggested that applying NLP methods to social media data could aid clinicians in their evaluations of bipolar disorder and that improved suicide prevention methods could be designed by combining ML methods and the medical community [34,39]. Sekulić et al [61] stated that the high incidence of suicide in bipolar disorder demonstrates the importance of early detection, and many authors suggested that applying NLP methods to social media data could contribute to the understanding of bipolar disorder and its detection and diagnosis [36,37,48,49,53,54,62,63]. Coppersmith et al [38] also suggested that using social media for large-scale data collection could complement existing methods and potentially make individual and population analyses quicker and cheaper. A number of the authors described that building a varied representation of bipolar disorder (eg, using features such as semantic deficit or attention weights) could provide a better understanding of the user experience, aid in diagnosis [16,42,58,64,66], and generate hypotheses for the clinical settings that may inform the provisioning of appropriate therapeutic resources [51].

Another practical application cited by the authors was the implementation of intervention systems based on flagging social media data for the moderator's attention [15,41,43]. Chen et al [17] and Park and Conway [55] described how different linguistic features can show how mental health conditions fluctuate over time and how these could help to identify worsening mental health. Gkotsis et al [40] suggested that urgency markers could be implemented for targeted interventions. Saha et al [59] reported that NLP methods could be used to screen and monitor health groups, and Saravia et al [60] and Silveira et al [15] suggested that social media data could be used to assist in the potential distribution of treatment to populations that are difficult to reach through traditional approaches. Ethical questions related to invasion of privacy, particularly when referring to populations who may have undetected mental illnesses, are raised by these possible innovations. It must be questioned whether the collection of data from social media platforms from a possibly unsuspecting population is ethical and it is also unclear who would be responsible for such an intervention.

In terms of the relevance of social media itself to people living with bipolar disorder, Kramer et al [50] described the hypothesis that 24 hour access to other people living with the same problem could reduce social isolation, improve coping skills, and improve patient knowledge about their own condition. Jagfeld et al [44] suggested that being aware of the demographics of web-based communities may help clinicians in recommending forums to their clients. Budenz et al [35] also described that social media advocacy can increase the amount of social support for people living with bipolar disorder to minimize the stigmatizing content posted on the web.

Finally, considering the use of NLP and medical records, Castro et al [13] and Dai et al [14] described that specific and predictive diagnostic algorithms could be created to assist with the diagnosis and to improve accuracy, achieving results that are comparable with diagnostic interviews. Other authors demonstrated how data, extracted using NLP, could improve care management and demonstrated the need, for example, to screen for the presence of instability on a routine basis or improve the assessment of smoking behavior [52,56,57,65].

A total of 60% (21/35) of articles used for the review referenced ethical considerations, and 40% (14/35) did not reference any ethical decision-making or design. The ethical considerations that were implemented are shown in Table 2, and Table 3 describes how the authors managed the code and data set release. It is interesting to note that the papers published until 2016 included limited discussion regarding ethical considerations, with only 47% (7/15) of papers published between 2004 and 2016 acknowledging ethical decision-making. In these earlier papers, discussion was generally limited to short statements, such as all collected data were publicly posted to Twitter between 2008 and 2015 [39] or clarification that ethics approval had been granted.

Ethical considerations became more frequent in papers published from 2017 onward, with 67% (14/21) papers published in 2017-2021 incorporating (generally much more robust) ethics statements. The increased focus on ethics correlates with the drive toward open science, and recent guidelines were implemented by scientific communities, such as the Association for Computational Linguistics, that require authors to upload a checklist for responsible NLP research alongside any paper submission [82] and to include a discussion about positive and negative societal impacts that could stem from the research.

Several articles in this review provided a more detailed discussion of ethical issues. Benton et al [34] suggested that NLP models could be overgeneralized or used to identify specific people, and Cohan et al [37] stated that risks to individuals as a consequence of social media research should always be considered. Various articles [38,55,60,61] described that mental health analyses must be approached sensitively and [55] also described the nature of Reddit and the throwaway accounts that can protect users from social discrimination. The studies by Jagfeld et al [44] and Thorstad and Wolff [63] both described the issue of dual use in which research can be misused to harm the public (eg, by insurance companies) and also suggested that a possible solution to violating user privacy would be to inform people that the casual comments they make on social media may be mined [63]. Finally, Huang et al [42] stated that the practical application of their proposed model would only be used if both health care practitioners and patients agreed to use it.

This scoping review highlights the heterogeneity in the existing research that has used NLP methods to study bipolar disorder. The review suggests that the literature has been produced predominantly in the United States and the United Kingdom (21/35, 60%) and that 66% (23/35) of the studies used Twitter or Reddit as a source of data. The studies were predominantly led by authors from the computational and informatics fields (31/35, 88%), with only 3 articles being first authored by a health care expert. The articles were grouped into four inductively developed objectives: (1) prediction and classification, (2) characterization of the language of bipolar disorder, (3) use of EHRs to measure health outcomes, and (4) using EHRs for phenotyping, with most of the articles using NLP methods for prediction and classification purposes. The review suggests that using NLP for the study of mental health and bipolar disorder specifically is a growing field and it seems to have been influenced by the study of Coppersmith et al [38] when they provided a framework for obtaining quantifiable data in mental health research using Twitter.

The range of technologies that have been applied in the field reflects the ever-increasing possibilities for conducting research on language, with the most recent articles mainly favoring deep learning methodologies and word embeddings. The results from the existing research are varied and promising and indicate the usefulness of NLP methods to aid in diagnosis, predict the emotional impact of web-based interactions, characterize the language used by people living with bipolar disorder, and use phenotypes to better assist in care management. The 13 articles that characterized the language of bipolar disorder provided evidence to suggest that there are some observable linguistic traits that can be identified in a population with bipolar disorder; for example, an increased use of both first-person pronouns and negative emotion expressions, which could be useful in providing a better representation of bipolar disorder and developing early detection or intervention systems.

There are 4 areas for further research that are proposed based on the results of this review. First, Sekulić et al [61] referred to the high incidence of suicide in bipolar disorder and suggested that early detection systems could be developed. The use of signposting systems that could flag at-risk users for moderator intervention also has been discussed by several authors included in this review. Considering that bipolar spectrum disorders are associated with significant disinhibition and poor judgment, which can lead to the commission of risky and dangerous behaviors [83], a key area of future research should be to look at how risky behaviors (not just suicide) are discussed. This study would help to better understand how people living with bipolar disorder can be supported by health care providers to facilitate and improve their quality of life. Examples of risk-taking behaviors referenced in the literature on bipolar disorder include binge eating, excessive drinking, gambling, self-injury, and risky spending [83].

Second, several articles described the potential benefits of using social media for people living with bipolar disorder (eg, becoming more informed about bipolar disorder [53], improved emotional state after web-based interactions [15], and improved readability scores over time [55]). Further research could be conducted on how people living with bipolar disorder can best be supported on the web, and specific evaluative frameworks could be implemented for this purpose [50].

Third, although gender was not discussed in detail in this review, there are some contradictory results with regard to the portrayal of gender on social media by people living with bipolar disorder. Although Cohan et al [37] proposed that their Reddit corpus may be gendered toward men (because of the large amount of references to women), Jagfeld et al [44] used predictive algorithms to suggest that more than half of their Reddit corpus comprises women and that feminine-gender–identifying people with a BD diagnosis seem to be more likely to use Reddit and disclose their diagnosis. This area of research was also touched upon by Huang et al [42] who built a set of gender-specific syntactic patterns for bipolar disorder recognition. Further computational linguistic research could be conducted to determine how gender is presented by social media users and whether this correlates with demographic statistics for the diagnosis of bipolar disorder. Future work may demonstrate if there are demographic groups, which are currently undersupported by health care services and are instead seeking help online.

Finally, impaired social and occupational functioning in bipolar disorder has been presented frequently in the wider literature, although a review of the range of functioning in bipolar disorder has demonstrated that 16% of individuals diagnosed with the condition function at a high level and that functioning with bipolar disorder may have been underestimated by some clinical measures [84]. This area has not yet been explored using NLP methods, which presents an opportunity to provide a more balanced perspective on the wide range of ways in which people live with bipolar experiences based on lived experience narratives that are free from the potential ceiling effect of some clinical measures.

It is crucial that ethical design underpins research that uses NLP to study bipolar disorder, because there are serious ethical concerns relating to data use and anonymization and the concept of dual use. Although the research community must uphold rigorous ethical standards for data collection and protection, researchers are simultaneously moving toward open science to ensure transparency of research practices and to enable easy access to the data from which important conclusions have been drawn. Therefore, researchers are now faced with a conflict between the objectives of the open science movement and the need to uphold data privacy. Dennis et al [85] described that privacy and open science are on a collision course. A number of ideas have been proposed to manage this conflict, although there is still no clearly defined solution [85-87]. The British Psychological Society stated that internet-mediated research should obtain valid consent when it “cannot be reasonably argued that online data can be considered ‘in the public domain’” and that any data disseminated through the research should maintain the anonymity of the author [88]. They also stated that research should maximize benefits and minimize harm (to the research participants), referring to the fourth main principle of the Code of Human Research Ethics [89]. Friedrich and Zesch [90] described that ethics should be integrated into any NLP project and that NLP researchers should be mindful of the implications of developing any language technology. Benton et al [91] provided guidelines for ethical research using social media data stating that all research should consider the benefits and risks involved from the outset, thus enabling the implementation of strategies to make research as risk averse as possible.

The literature included in this scoping review suggests that there is ambiguity around the best practice for the ethical design of NLP methodologies, with 40% (14/35) of the articles making no reference to ethical decision-making and a wide range of methodologies for the articles that do. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and researchers should be aware that the best intentions could still have potentially harmful consequences. Although researchers are likely to be governed by the principals of open science, any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis with consideration for the risk to the data participants and ensuring their privacy.

Although this scoping review was conducted according to a scoping review methodology and a previous protocol, there were some limitations that are worth noting.

First, as described throughout the review, there was large variation in the way NLP methods were described and indexed, and so it is possible that some relevant articles were not included in this review through the terms used in the search query.

Second, the data were extracted by only 1 reviewer because of the relatively high number of studies identified in the data extraction phase. An attempt was made to ensure accurate extraction by using a verified and standardized extraction form; however, the data that were extracted and used within the scoping review were predominantly qualitative, so it is likely that there could be researcher bias.

Finally, this review was conducted in an area of research that is constantly growing and developing and therefore only provides a time-stamped representation of the field.

This scoping review provided an overview of 35 papers that applied NLP methods to the study of bipolar disorder. The data indicate that there are increasing opportunities for interaction between the clinical and NLP communities, and existing research shows how the analysis of language can be used to assist with and improve the provision of care for people living with bipolar disorder. There are 4 areas in bipolar disorder research that have been identified that may benefit from NLP methods, including the study of risk-taking behaviors, the research and design of web-based support groups specific to bipolar disorder, the study of social and occupational functioning, and the study of gender representation in bipolar disorder populations on the web.