Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-28T23:44:59.390Z Has data issue: false hasContentIssue false
  • English
  • Français

Insideout project: Using big data and machine learning for prevention in psychiatry

Published online by Cambridge University Press:  13 August 2021

F. Fiori Nastro ,
D. Croce ,
S. Schmidt ,
R. Basili  and
F. Schultze-Lutter
F. Fiori Nastro*
Affiliation:
Department Of Systems Medicine, University of Rome “Tor Vergata”, Roma, Italy
D. Croce
Affiliation:
Department Of Enterprise Engineering, University of Rome “Tor Vergata”, Roma, Italy
S. Schmidt
Affiliation:
Clinical Psychology And Psychotherapy, University of Bern, Bern, Switzerland
R. Basili
Affiliation:
Department Of Enterprise Engineering, University of Rome “Tor Vergata”, Rome, Italy
F. Schultze-Lutter
Affiliation:
Department Of Psychiatry And Psychotherapy, Heinrich-Heine-University, Düsseldorf, Germany
*
*Corresponding author.

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Introduction

Social Media might represent an amazing and valuable source of information on mental health and well-being. Several researches revealed that adolescents aged 13 to 17 years old go “online” daily or stay online “almost constantly”.

Objectives

The aim of this project is to identify distress in pre-clinical stages using Social media screening methods. The system can be modelled to centre on different several health-related topics.

Methods

We created a digital system able to analyse scripts written by adolescents on Twitter. InsideOut works using machine learning techniques and computational linguistic items to catch significant and sense of written messages and it improves its performances with iterations. The system is able to automatically identify semantic information relevant to different topics: in this case “distress in teenagers”.

Results

The task of our system is considered correct when it is able to identify triples of Life Event, Sentiment and Experience of a tweet in agreement with the Gold Standard established among the annotators. The system has around 70% of accuracy in identifying triples. The analysis has been carried out both in Italian and English collecting over 4 million Italian tweets and 30 million English tweets. Comparative analysis with self-report questionnaires show that tweet analysis is able to suggest similar statistics.

Conclusions

This study analyzed contents of messages posted on Social Media Twitter meta-dating them with psychological and health-related information. Using InsideOut, we can plan clinical intervention in district and regions where high levels of uneasiness are revealed.

Keywords

distresspreventionmachine learninge-mental health
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S343
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
Submit a response

Comments

No Comments have been published for this article.