Linguistic features of suicidal thoughts and behaviors: A systematic review

Homan, Stephanie; Gabi, Marion; Klee, Nina; Bachmann, Sandro; Moser, Ann-Marie; Duri', Martina; Michel, Sofia; Bertram, Anna-Marie; Maatz, Anke; Seiler, Guido; Stark, Elisabeth; Kleim, Birgit

doi:10.1016/j.cpr.2022.102161

Cited by 32 publications

(32 citation statements)

References 103 publications

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“…Absolutist language (superlatives and intensifiers) has also been linked with suicidal thoughts, and greater pronoun use has been linked with suicidal behavior (Homan et al, 2022). Again, however, these associations have never been tested using in-vivo private data streams such as text messages.…”

Section: Introductionmentioning

confidence: 99%

Prospective associations of text‐message‐based sentiment with symptoms of depression, generalized anxiety, and social anxiety

Stamatis

Meyerhoff

Liu

et al. 2022

Depression and Anxiety

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Objective Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety. Methods Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types. Results We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: −.10 to −.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: .12–.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: .12–.13, ps < .05). Conclusion Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive‐behavioral mechanisms and tailoring digital interventions.

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Section: Introductionmentioning

confidence: 99%

Prospective associations of text‐message‐based sentiment with symptoms of depression, generalized anxiety, and social anxiety

Stamatis

Meyerhoff

Liu

et al. 2022

Depression and Anxiety

View full text Add to dashboard Cite

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“…However, owing to the rise of social media this day, persons do post "suicidal" messages on social media platforms such as Twitter, which provides more objective data [17]. At the same time, the widespread use of new techniques in machine learning and natural language processing has made it possible to extract semantic information from text and speech, and this advancement provides potential predictors for STB prediction from the perspective of linguistic features [1]. In terms of classification method adoption, the simplest form of classification method is to divide some data instances into two categories using selected features.…”

Section: An Overview Of Methods To Detecting Suicidal Ideationmentioning

confidence: 99%

“…Suicide is harmful behavior with self-directed death [1]. Despite the huge progress in modern medicine in diagnosing and treating major mental disorders, suicide remains a difficult public health problem [2].…”

Section: Introductionmentioning

confidence: 99%

Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

Liu

Shi

Jiang

2022

IJERPH

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Suicide has become a serious problem, and how to prevent suicide has become a very important research topic. Social media provides an ideal platform for monitoring suicidal ideation. This paper presents an integrated model for multidimensional information fusion. By integrating the best classification models determined by single and multiple features, different feature information is combined to better identify suicidal posts in online social media. This approach was assessed with a dataset formed from 40,222 posts annotated by Weibo. By integrating the best classification model of single features and multidimensional features, the proposed model ((BSC + RFS)-fs, WEC-fs) achieved 80.61% accuracy and a 79.20% F1-score. Other representative text information representation methods and demographic factors related to suicide may also be important predictors of suicide, which were not considered in this study. To the best of our knowledge, this is the good try that feature combination and ensemble algorithms have been fused to detect user-generated content with suicidal ideation. The findings suggest that feature combinations do not always work well, and that an appropriate combination strategy can make classification models work better. There are differences in the information contained in different functional carriers, and a targeted choice classification model may improve the detection rate of suicidal ideation.

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“…Prior work has shown the positive predictive value of using diagnostic codes for suicide attempt to be as low as 58.63% in a sample of 5543 charts, though these estimates range up to near perfect performance 3,4 . Issues with ascertainment undermine accurate estimates of rates of suicidal outcomes, appropriate resource allocation, quality improvement, and risk assessment 2,5,6 .Like other behavioral health traits, alternative ascertainment approaches via patient self-report, health information exchange, public health surveillance, and natural language processing (NLP) have been tested to assess improvement from diagnostic codes [7][8][9][10][11][12] . The latter, NLP, has been used to improve ascertainment of social determinants of health to augment effective sample size for clinical modeling [13][14][15] .Suicidal ideation and suicide attempt share attributes common to clinical outcomes that lack biomarkers or reliable structured data representation.…”

mentioning

confidence: 99%

“…Like other behavioral health traits, alternative ascertainment approaches via patient self-report, health information exchange, public health surveillance, and natural language processing (NLP) have been tested to assess improvement from diagnostic codes [7][8][9][10][11][12] . The latter, NLP, has been used to improve ascertainment of social determinants of health to augment effective sample size for clinical modeling [13][14][15] .…”

mentioning

confidence: 99%

Improving ascertainment of suicidal ideation and suicide attempt with natural language processing

Bejan

Ripperger

Wilimitis

et al. 2022

Sci Rep

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Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC]: 98.6, 95% confidence interval [CI] 97.1–99.5) and suicide attempt (AUROC: 97.3, 95% CI 95.2–98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.

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Linguistic features of suicidal thoughts and behaviors: A systematic review

Cited by 32 publications

References 103 publications

Prospective associations of text‐message‐based sentiment with symptoms of depression, generalized anxiety, and social anxiety

Prospective associations of text‐message‐based sentiment with symptoms of depression, generalized anxiety, and social anxiety

Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

Improving ascertainment of suicidal ideation and suicide attempt with natural language processing

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