Analysing the connectivity and communication of suicidal users on twitter

Colombo, Gualtiero B.; Burnap, Pete; Hodorog, Andrei; Scourfield, Jonathan

doi:10.1016/j.comcom.2015.07.018

Cited by 116 publications

(50 citation statements)

References 37 publications

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“…There exists some research examining suicide in social media [58, 43, 15, 9, 28, 38]. Authors in [71] focused on South Korean blogs to predict nationwide suicide rate data (also see [36] for a similar study in the US context).…”

Section: Background and Prior Workmentioning

confidence: 99%

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

Choudhury

Kıcıman

Dredze

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

567

423

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History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

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Section: Background and Prior Workmentioning

confidence: 99%

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

Choudhury

Kıcıman

Dredze

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

567

423

View full text Add to dashboard Cite

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“…Jashinsky et al [16] emphatically highlighted the geographic correlation between the suicide mortality rates and the occurrence of risk factors in tweets. Colombo et al [17] studied the tweets containing suicide ideation based on the users' behavior in social network interactions resulting in a high degree of reciprocal connectivity and strengthening the bonds between the users.…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

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“…Recently, Burnap et al [2] built text classifiers that identify tweets discussing suicide ideation. They also studied the follower-friend networks of tweeters posting tweets that convey suicidal ideation [5]. …”

Section: Related Workmentioning

confidence: 99%

Classification of Helpful Comments on Online Suicide Watch Forums

Kavuluru

Ramos-Morales

Holaday

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Among social media websites, Reddit has emerged as a widely used online message board for focused mental health topics including depression, addiction, and suicide watch (SW). In particular, the SW community/subreddit has nearly 40,000 subscribers and 13 human moderators who monitor for abusive comments among other things. Given comments on posts from users expressing suicidal thoughts can be written from any part of the world at any time, moderating in a timely manner can be tedious. Furthermore, Reddit's default comment ranking does not involve aspects that relate to the “helpfulness” of a comment from a suicide prevention (SP) perspective. Being able to automatically identify and score helpful comments from such a perspective can assist moderators, help SW posters to have immediate feedback on the SP relevance of a comment, and also provide insights to SP researchers for dealing with online aspects of SP. In this paper, we report what we believe is the first effort in automatic identification of helpful comments on online posts in SW forums with the SW subreddit as the use-case. We use a dataset of 3000 real SW comments and obtain SP researcher judgments regarding their helpfulness in the contexts of the corresponding original posts. We conduct supervised learning experiments with content based features including n-grams, word psychometric scores, and discourse relation graphs and report encouraging F-scores (≈ 80 – 90%) for the helpful comment classes. Our results indicate that machine learning approaches can offer complementary moderating functionality for SW posts. Furthermore, we realize assessing the helpfulness of comments on mental health related online posts is a nuanced topic and needs further attention from the SP research community.

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Analysing the connectivity and communication of suicidal users on twitter

Cited by 116 publications

References 37 publications

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Classification of Helpful Comments on Online Suicide Watch Forums

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