Moreover, most of the other biological or natural common causes of death, such as cardiovascular diseases or cancer have reached a relatively high and growing social awareness, with well-funded research infrastructure(, accessed on 2 November 2021) and a world-wide push to develop medical treatments or vaccines. However, compared to other purely biological and external common causes of death, including cancer and cardiovascular diseases, preconditions leading to suicides (e.g., depression, schizophrenia, hopelessness, PTSD, etc.) are notoriously difficult to diagnose, while the occurrence of the event of suicide itself is much more problematic to predict, which makes the problem of suicides a comparatively much more complex problem to solve on a global scale. Suicide is one of the global leading causes of death, with about eight hundred thousand people taking their own lives every year. 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. ![]() 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. 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. 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. 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. 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. ![]() ![]() 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. 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. 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. In this paper, we study language used by suicidal users on Reddit social media platform.
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