Predicting user emotional tone in mental disorder online communities

Silveira, Bárbara; Silva, Henrique S.; Murai, Fabrício; Silva, Ana Paula Couto da

doi:10.1016/j.future.2021.07.014

Cited by 9 publications

(15 citation statements)

References 24 publications

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“…The most frequent objective of the included articles was to use text from social media (n=22) [ 15 - 17 , 34 , 36 - 44 , 48 , 49 , 51 , 54 , 59 - 63 ] or EHRs (n=3) [ 13 , 14 , 64 ] for prediction or classification purposes; for example, to predict a diagnosis of bipolar disorder based on features in the text. Among the 25 papers categorized into the objective of prediction and classification, 21 (84%) classified posts or users into a bipolar disorder class after comparison with a control group or with other mental health conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Among the 25 papers categorized into the objective of prediction and classification, 21 (84%) classified posts or users into a bipolar disorder class after comparison with a control group or with other mental health conditions. The aims of the remaining studies included; predicting the emotional tone in a bipolar disorder community, that is, how interactions in a web-based community affect people [ 15 ]; predicting the future occurrence of bipolar disorder based on a user's posts in a nonclinical subreddit before joining a bipolar disorder subreddit [ 63 ]; performing classification to measure subreddit uniqueness [ 40 ]; and using off-the-shelf algorithms to predict the demographic characteristics of people who self-reported a bipolar disorder diagnosis on Reddit [ 44 ]. There was large variation in the amount of data collected, with some authors reporting the number of relevant users and posts or comments and some reporting only the number of posts or users.…”

Section: Resultsmentioning

confidence: 99%

“…There were 4 articles, which used NLP methods for alternative classification purposes. Silveira et al [ 15 ] predicted how the emotional states of Reddit users would change after interacting on social media and framed this as a regression task that outperformed the baseline by a score of at least 12.9. Their results showed that general emotional states improved after interacting on the web and that the emotional tone of the final post by the thread author was generally more positive than their initial post.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Harvey¹,

Lobban²,

Rayson³

et al. 2022

JMIR Ment Health

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Background Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature. Objective This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods. Methods A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology. Results Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies. Conclusions The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Harvey¹,

Lobban²,

Rayson³

et al. 2022

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks

Kanaparthi

Patle

Naik

2023

Multimed Tools Appl

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Online social media networks have become a significant platform for persons with mental illnesses to discuss their struggles and obtain emotional and informational assistance in recent years. One such platform is Reddit, where sub-groups called ‘subreddits’ exist, based on a variety of topics including mental illnesses such as anxiety or depression. We analyse the user’s interactions to calculate the mental health status by formulating and using a parameter called ‘emotional tone’ representing the user’s emotional state. VADER sentiment analysis and TextBlob are used to categorise emotional tone and find distribution of emotional polarity and subjectivity of comments. For final tone prediction, RNN and State-Of-The-Art word embedding techniques are used to develop a predictive model. The resultant model provides end-to-end categorization and prediction of emotional tone. We obtain results with respect to Weighted L1 Loss that deals with extreme responses. The MODEL transcends all the baselines by at least 12.1% and the final emotional status of the authors is positive.

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Lived experience at the core: A classification system for risk-taking behaviours in bipolar

Harvey,

Rayson,

Lobban

et al. 2024

DIGITAL HEALTH

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Objective Clinical observations suggest that individuals with a diagnosis of bipolar face difficulties regulating emotions and impairments to their cognitive processing, which can contribute to high-risk behaviours. However, there are few studies which explore the types of risk-taking behaviour that manifest in reality and evidence suggests that there is currently not enough support for the management of these behaviours. This study examined the types of risk-taking behaviours described by people who live with bipolar and their access to support for these behaviours. Methods Semi-structured interviews were conducted with n = 18 participants with a lived experience of bipolar and n = 5 healthcare professionals. The interviews comprised open-ended questions and a Likert-item questionnaire. The responses to the interview questions were analysed using content analysis and corpus linguistic methods to develop a classification system of risk-taking behaviours. The Likert-item questionnaire was analysed statistically and insights from the questionnaire were incorporated into the classification system. Results Our classification system includes 39 reported risk-taking behaviours which we manually inferred into six domains of risk-taking. Corpus linguistic and qualitative analysis of the interview data demonstrate that people need more support for risk-taking behaviours and that aside from suicide, self-harm and excessive spending, many behaviours are not routinely monitored. Conclusion This study shows that people living with bipolar report the need for improved access to psychologically informed care, and that a standardised classification system or risk-taking questionnaire could act as a useful elicitation tool for guiding conversations around risk-taking to ensure that opportunities for intervention are not missed. We have also presented a novel methodological framework which demonstrates the utility of computational linguistic methods for the analysis of health research data.

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Predicting user emotional tone in mental disorder online communities

Cited by 9 publications

References 24 publications

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks

Lived experience at the core: A classification system for risk-taking behaviours in bipolar

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