Suicide Classificaction for News Media Using Convolutional Neural Network

TL;DR

This paper presents a neural network model trained on suicide-related tweets to classify news media content for suicide contagion, aiming to improve understanding and prevention strategies.

Contribution

It introduces a novel approach using social media data and neural networks to identify suicide-related content in news media, addressing the lack of explicit tags.

Findings

High impact of media on suicide cases

Intrinsic thematic relationship in suicide news

Potential for better suicide data interpretation

Abstract

Currently, the process of evaluating suicides is highly subjective, which limits the efficacy and accuracy of prevention efforts. Artificial intelligence (AI) has emerged as a means of investigating large datasets to identify patterns within "big data" that can determine the factors on suicide outcomes. Here, we use AI tools to extract the topic from (press and social) media text. However, news media articles lack of suicide tags. Using tweets with hashtags related to sucide, we train a neuronal model which identifies if a given text has a suicidade-related contagion. Our results suggest a high level of the impact of mediatic into suicide cases, and a intrinsic thematic relationship of suicide news. These results pave the way to build more interpretable suicide data, which may help to better track, understand its origin, and improve prevention strategies.