Monitoring Adolescents' Distress using Social Web data as a Source:,the InsideOut Project
Authors: Roberto Basili, Valentina Bellomaria, Niels J. Bugge , Danilo Croce, Francesco De Michele, Federico Fiori Nastro, Paolo Fiori Nastro, Chantal Michel , Stefanie J. Schmidt, Frauke Schultze-Lutter (Università di Roma Tor Vergata, Reveal srl, University of Bern, University of Geneva, Sapienza Università di Roma, Heinrich-Heine University, 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The role of Social Media in the psychological and social development of adolescents and young adults is increasingly important as it impacts on the quality of their interpersonal communication dynamics. The InsideOut project explores the possibility to use Social Web mining methodologies and technologies to collect information about adolescents' distress from their micro-blogging activities. The project is promoting a complex language processing workflow to approach the collection, enrichment and summarization of user generated contents over Twitter. This paper presents the general architecture of the InsideOut Web Platform and the resources produced by an integrated effort among computer science and mental health professionals.

Introduction
Among adolescents, the use of Social Media, such as Twitter, Facebook or Instagram, has grown exponentially in the past years. This makes them a valuable source of information on the well-being of adolescents, but also concerning on their mental health. Mental disorders are the main cause of disability in adolescents and young adults AUTHOR, affecting an average of 10 to 20% of youth worldwide AUTHOR. Thus, for the emerging complex relationship between the use of Social Media, mental health and well-being AUTHOR, Social Media are a valuable source of information on the mental health and well-being of adolescents. Social Media thus play an increasingly important role in the psychological and social development of adolescents as it impacts on the quality of their social interactions and networks. Any attempt to study and govern mental health in young communities (adolescents, students, interest groups) must take into account an effective and large scale methodology to monitor all the behaviors on the Web that exhibit and impact on mental habits, trends and social practices. The possibility of predicting writers demographics from their writings is an important research topic in the Computational Linguistic Community. In fact, the idea that a writer's style may reveal age, gender or other sociodemographic information has been also targeted in the ``Plagiarism analysis, Authorship identification, and Near-duplicate detection'' (PAN) (e.g., AUTHOR) or other experiences AUTHOR whose aim was to infer a user's gender, age, native language or personality traits, by analyzing the respective texts. In this paper, the InsideOut project is presented. It explores the possibility to use Social Web mining methodologies and technologies to collect information about adolescents' distress from their micro-blogging activities. The project is promoting a complex language processing workflow to approach the collection, enrichment of user generated contents on Twitter: messages written by a set of targeted community of users (e.g. from a school) are enriched with semantic metadata reflecting the expressed topics (e.g. social vs intimate relationships) and the attitude of the writers. The goal is to use this large scale evidence to support a comprehensive psychological characterization of adolescent communities and to pave the way towards effective applications of preventive and intervention efforts. The general architecture of the InsideOut Web Platform and the resources produced by an integrated effort of computer science specialists and mental health professionals will be presented. These data supported the exploratory evaluation where inter-annotation agreement scores and the performance over real data in the task of psychologically enriching user writings have been obtained. In the rest of the paper, Section sec:platform describes the overall workflow underlying the InsideOut Platform. Section sec:model describes the semantic models at the base of the semantic annotation process whose first result is the annotated corpus and the exploratory evaluation presented in Sections sec:corpus and sec:eval, respectively. Section sec:concl derives the conclusion.

Conclusion
This paper summarizes the InsideOut project where the possibility to use Social Web mining methodologies and technologies to gather evidence about the adolescents' mental distress.%, using as sources micro-blogging activities of users, is investigated. The semantic model defined here and the annotated resource pave the way to a long-term joint research between computer science specialists and mental health professionals. The outcomes suggest the applicability of the devised methodology to larger communities and different languages. Since the system is currently active over Twitter, the final version of the paper will discuss about 5 months of continuous monitoring outcomes towards Italian and English speaking communities, with interesting evidences about the future of our project as a novel and ambitious Social Computational Science application.