Monitoring Adolescents' Distress using Social Web data as a Source: the InsideOut Project

English. 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. Italiano. Il ruolo dei Social Media nella crescita psicologica e sociale risulta essere sempre più importante poiché influisce sulla qualità e sulle dinamiche di comunicazioni interpersonali, specialmente riguardo le ultime generazioni. Il progetto InsideOut esplora la applicabilità di metodologie e tecnologie che consentono l’individuazione nel Web di evidenze riferibili a sorgenti di stress negli adolescenti. Il progetto propone un workflow di elaborazione linguistica in grado di gestire la raccolta, l’arricchimento e la sintesi dei contenuti generati dagli utenti su Twitter. Nel paper verrà presentata l’architettura generale della piattaforma Web InsideOut e le risorse che derivano dal lavoro congiunto di ricercatori provenienti dall’ambito informatico e medico.

[1]  Benno Stein,et al.  Overview of the 2 nd Author Profiling Task at PAN 2014 , 2014 .

[2]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[3]  Cristina Bosco,et al.  Subjective Well-Being and Social Media. A Semantically Annotated Twitter Corpus on Fertility and Parenthood , 2016, CLiC-it/EVALITA.

[4]  P. Best,et al.  Online communication, social media and adolescent wellbeing: A systematic narrative review , 2014 .

[5]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Preslav Nakov,et al.  SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.

[8]  C. Kieling,et al.  Child and adolescent mental health worldwide: evidence for action , 2011, The Lancet.

[9]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[10]  Roberto Basili,et al.  KeLP: a Kernel-based Learning Platform for Natural Language Processing , 2015, ACL.

[11]  Benno Stein,et al.  Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations , 2016, CLEF.

[12]  Malvina Nissim,et al.  Overview of the Evalita 2016 SENTIment POLarity Classification Task , 2014, CLiC-it/EVALITA.

[13]  Benno Stein,et al.  Overview of the 3rd Author Profiling Task at PAN 2015 , 2015, CLEF.

[14]  C. Mathers,et al.  Global burden of disease in young people aged 10–24 years: a systematic analysis , 2011, The Lancet.