Different mental health conditions may lead to suicide, among which depression is one of the most common. One in six people affected by major depression dies by suicide, with suicide being the second leading cause of death among teenagers and young adults. Symptoms of suicidal risk often remain latent until the inevitable occurs, which highlights the significance of prevention when it comes to such conditions. Language encodes psychological aspects that reflect a s ubject’s p ersonality and state of mind and could be used as a proxy to identify shifts in a person’s mental health state. In this work we study how psychometric traits of language vary when a person moves from depression-related discourse to suicide-related discourse, analyzing posts from Reddit dedicated to these themes. For each user, we identify the changes in their psychometric profile when the discourse shifts, and try to pinpoint groups of users with the same behavior and psychometric evolution. Our findings highlight several psychometric features as the most involved in discourse shifting, setting the stage for easier identification of high-risk individuals from the way they express themselves through language.

In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.