Loughborough University
Browse

Detecting suicide ideation in the era of social media: the population neuroscience perspective

Download (655.08 kB)
journal contribution
posted on 2022-04-27, 08:25 authored by Rosalba Morese, Oliver Gruebner, Martin SykoraMartin Sykora, Suzanne ElayanSuzanne Elayan, Marta Fadda, Emiliano Albanese
Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale.

Funding

Zurich Foundation

History

School

  • Business and Economics

Department

  • Business

Published in

Frontiers in Psychiatry

Volume

13

Publisher

Frontiers Media SA

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Frontiers Media under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-03-21

Publication date

2022-04-14

Copyright date

2022

eISSN

1664-0640

Language

  • en

Depositor

Dr Martin Sykora. Deposit date: 25 April 2022

Article number

652167

Usage metrics

    Loughborough Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC