Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media-A Pragmalinguistic Approach.

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

Mental Health and the Symptoms of PTSD in People with Depression and Anxiety Disorders during the COVID-19 Pandemic.

The purpose of this study was to examine the severity of post-traumatic stress disorder (PTSD) symptoms related to the COVID-19 pandemic in people with no diagnosis of mental illness, as well as in people who were diagnosed with depression or anxiety. Moreover, this study aimed to investigate the interplay between PTSD symptoms and self-assessed mental health associated with well-being. The 210 participants were divided into 3 groups: mentally healthy, participants with diagnosed depression, and participants with anxiety disorders. To evaluate the subjective well-being of the participants, the Polish adaptation of the Mental Health Continuum-Short Form (MHC-SF) was applied. The Impact Event Scale-Revised (IES-R) was used to measure the severity of PTSD symptoms. At least a moderate worsening of PTSD symptoms was observed in participants of all groups. The results were as follows: healthy participants M = 37.35 (SD = 18.46); participants with depression M = 36.05 (SD = 18.02); participants with anxiety M = 44.52 (SD = 18.08). The participants diagnosed with depression showed the lowest level of mental well-being M = 41.58 (SD = 15.02). Conclusion: People diagnosed with depression had both the lowest level of well-being and the lowest severity of symptoms specific to PTSD. In all three groups, lower emotional well-being was linked to greater PTSD symptoms.