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Suicide Classification for News Media Using Convolutional Neural Networks
Hugo J Bello1, Nora Palomar-Ciria2, Enrique Baca-García3,4,5,6,7,8,9,10,11,12
1Department of Applied Mathematics, Universidad de Valladolid.
Health Communication
|May 9, 2022
Summary
Artificial intelligence (AI) can analyze media data to identify suicide-related topics, improving the accuracy of suicide prevention. This approach helps track and understand suicide origins from media coverage.
Area of Science:
- Computational linguistics
- Public health informatics
- Artificial intelligence
Background:
- Suicide evaluation is currently subjective, limiting prevention effectiveness.
- Artificial intelligence (AI) offers potential for analyzing large datasets to identify suicide risk factors.
- Media coverage of suicide is prevalent but lacks specific tagging for analysis.
Purpose of the Study:
- To develop an AI model for extracting suicide-related topics from media texts.
- To investigate the thematic relationships and impact of suicide news in media.
- To enable better tracking and understanding of suicide origins through media data.
Main Methods:
- Utilized AI tools to process and extract topics from press and social media.
- Trained a neural network model using tweets with suicide-related hashtags.
- Developed a model to identify suicide-related content in text data.
Main Results:
- Demonstrated the significant impact of suicide cases in media coverage.
- Identified intrinsic thematic connections within suicide-related news.
- Successfully trained a model to detect suicide topics in text.
Conclusions:
- AI-driven topic extraction from media can provide interpretable suicide data.
- This methodology can enhance the tracking and understanding of suicide.
- Improved data interpretation may lead to more effective suicide prevention strategies.
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