Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

Chen, Xuetong; Sýkora, Martin; Jackson, Thomas W.; Elayan, Suzanne; Munir, Fehmidah

doi:10.24251/hicss.2018.421

Cited by 21 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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 ].…”

Section: Methodsmentioning

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 2 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

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Since early emotional reactions may predict longer-term mental health needs, this approach could further assist in the long-term allocation of services. EMOTIVE is currently being extended to also evaluate stress responses that in conjunction with a spatially explicit approach, as it has been applied here may help to estimate the development of symptoms indicative of depression and PTSD [ 49 , 50 ]. Given that social media use has dramatically increased worldwide since 2012, these data provide enormous potential for mental health research to study e.g., the functional relationship between socio-ecological factors and mental health.…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

Gruebner

Lowe

Sýkora

et al. 2018

IJERPH

Self Cite

View full text Add to dashboard Cite

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

show abstract

“…One study showed how emotions over time have predictive value in classifying likelihood of depression (building on Coppersmith’s approach), 15 while another closely replicated Coppersmith’s study on predicting the four mental health conditions, showing how emotion variables can improve predictive performance across these. 16 Such methods have been developed both for monitoring population mental health trends and patterns, and identifying risk factors for individuals. 17 …”

Section: Introductionmentioning

confidence: 99%

Ethical issues of collecting, storing, and analyzing geo-referenced tweets for mental health research

et al. 2022

Self Cite

View full text Add to dashboard Cite

Spatial approaches to epidemiological research with big social media data provide tremendous opportunities to study the relationship between the socio-ecological context where these data are generated and health indicators of interest. Such research poses a number of ethical challenges, particularly in relation to issues such as privacy, informed consent, data security, and storage. While these issues have received considerable attention by researchers in relation to research for physical health purposes in the past 10 years, there have been few efforts to consider the ethical challenges of conducting mental health research, particularly with geo-referenced social media data. The aim of this article is to identify strengths and limitations of current recommendations to address the specific ethical issues of geo-referenced tweets for mental health research. We contribute to the ongoing debate on the ethical implications of big data research and also provide recommendations to researchers and stakeholders alike on how to tackle them, with a specific focus on the use of geo-referenced data for mental health research purposes. With increasing awareness of data privacy and confidentiality issues (even for non-spatial social media data) it becomes crucial to establish professional standards of conduct so that compliance with ethical standards of conducting research with health-related social media data can be prioritized and easily assessed.

show abstract

Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

Cited by 21 publications

References 19 publications

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

Ethical issues of collecting, storing, and analyzing geo-referenced tweets for mental health research

Contact Info

Product

Resources

About