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Data generated within social media platforms may present a new way to identify individuals who are experiencing mental illness. This study aimed to investigate the associations between linguistic features in individuals' blog data and their symptoms of depression, generalised anxiety, and suicidal ideation. Individuals who blogged were invited to participate in a longitudinal study in which they completed fortnightly symptom scales for depression and anxiety (PHQ-9, GAD-7) for a period of 36 weeks. Blog data published in the same period was also collected, and linguistic features were analysed using the LIWC tool. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and symptoms between subjects. Multivariate regression models were used to predict longitudinal changes in symptoms within subjects. A total of 153 participants consented to the study. The final sample consisted of the 38 participants who completed the required number of symptom scales and generated blog data during the study period. Between-subject analysis revealed that the linguistic features "tentativeness" and "non-fluencies" were significantly correlated with symptoms of depression and anxiety, but not suicidal thoughts. Within-subject analysis showed no robust correlations between linguistic features and changes in symptoms. The findings may provide evidence of a relationship between some linguistic features in social media data and mental health; however, the study was limited by missing data and other important considerations. The findings also suggest that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Broadcasting a suicide attempt on social media has become a public concern in China. Stigmatizing attitudes around such broadcast can limit help-seeking and increase the likelihood of death. To reduce stigmatizing attitudes, this paper aims to detect stigma expressions in social media posts through language use patterns and then identify suicide literacy in responses to such broadcast., Methods: Firstly, to examine linguistic patterns of stigma expressions, 6632 Weibo posts with keywords were collected and analyzed. Using 102 linguistic features, 2 classification models were built: one for differentiating between stigmatizing and nonstigmatizing attitudes, and one for differentiating between specific types of stigmatizing attitudes. Secondly, to identify the levels of suicide literacy, a content analysis was conducted on 4969 Weibo posts related to social media suicide., Results: Firstly, the model accuracy ranged from 66.15% to 72.79%. Secondly, a total of 11.67% of the Weibo posts (n = 580) contained misinformation about suicide. In the category of knowledge of signs, 27.93% and 18.10% of posts endorsed the stigmatizing views that "suicide happens without warning" and "people who want to attempt suicide cannot change their mind quickly," both of which were related to a stigmatizing belief that a suicide attempt on social media is not genuine. In the category of knowledge of treatments, 35.17% of posts endorsed the stigmatizing view that "people who have thoughts about suicide should not tell others about it.", Discussion: This paper presents an opportunity for the dissemination of targeted online campaigns to increase mental health literacy and help-seeking., (© 2018 John Wiley & Sons Australia, Ltd.)

Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not., Aims: This study aims to characterize the linguistic profiles of suicide-related Twitter posts., Method: Using a dataset of suicide-related Twitter posts previously coded for suicide risk by experts, Linguistic Inquiry and Word Count (LIWC) and regression analyses were conducted to determine differences in linguistic profiles., Results: When compared with matched non-suicide-related Twitter posts, strongly concerning suicide-related posts were characterized by a higher word count, increased use of first-person pronouns, and more references to death. When compared with safe-to-ignore suicide-related posts, strongly concerning suicide-related posts were characterized by increased use of first-person pronouns, greater anger, and increased focus on the present. Other differences were found., Limitations: The predictive validity of the identified features needs further testing before these results can be used for interventional purposes., Conclusion: This study demonstrates that strongly concerning suicide-related Twitter posts have unique linguistic profiles. The examination of Twitter data for the presence of such features may help to validate online risk assessments and determine those in need of further support or intervention.

This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a sample of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Social and professional support for mental health is lacking in many rural areas - highlighting the need for innovative ways to improve access to services. This study explores the potential of online social networking as an avenue for peer support. Using a cross sectional survey, 74 secondary students answered questions relating to internet use, online social network use and perceptions of mental health support. Over half of the sample had experienced a need for mental health support with 53% of participants turning to the internet. Results indicate that online social networking sites were used regularly by 82% of the sample and 47% believed these sites could help with mental health problems. The study concluded that online social networking sites may be able to link young people together with others in similar situations. The popularity and frequency of use may allow these sites to provide information, advice and direction for those seeking help.

Depression is a highly prevalent mental health problem and is a co-morbidity of other mental, physical, and behavioural disorders. The internet allows individuals who are depressed or caring for those who are depressed, to connect with others via online communities; however, the characteristics of these discussions have not yet been fully explored. This work aims to explore the textual cues of online communities interested in depression. A total of 5,000 posts were randomly selected from 24 online communities. Five subgroups of online communities were identified: Depression, Bipolar Disorder, Self-Harm, Grief/Bereavement, and Suicide. Psycholinguistic features and content topics were extracted from the posts and analysed. Machine learning techniques were used to discriminate the online conversations in the depression communities from the other subgroups. Topics and psycholinguistic features were found to be highly valid predictors of community subgroup. Clear discrimination between linguistic features and topics, alongside good predictive power is an important step in understanding social media and its use in mental health. [ABSTRACT FROM AUTHOR]