Exploring Suicide Factors in Online Discourse: Sentiment and Thematic Analysis of Reddit

Suicide remains a critical global health issue, with rising numbers claiming more lives each year despite ongoing prevention efforts. Current research has extensively explored factors influencing suicidal tendencies, emphasizing trauma, mental health disorders, and social relationships. However, traditional studies often relied on traditional data sources and often examined risk factors in isolation, which may not fully capture the dynamics observed in social media platforms. To address these limitations, our study utilizes data from r/SuicideWatch and r/Teenagers to analyze the emotional sentiment and explore themes associated with suicidal ideation, with r/Teenagers serving as a comparative reference. By leveraging natural language processing (NLP) techniques and statistical methodologies, including sentiment analysis and BERTopic modeling, we aim to gain deeper insights into the factors contributing to suicidal thoughts. Using TextBlob, our findings reveal a significant difference in sentiment between the two subreddits, with r/SuicideWatch posts predominantly expressing challenges and distressing emotions. Through BERTopic analysis, we identified key themes, such as emotional challenges related to romantic relationships, academic pressure, and substance use concerns in r/SuicideWatch, highlighting their strong association with suicidal ideation. While r/Teenagers had some similar themes regarding struggles with loneliness and academics, the topics were focused more on general adolescent concerns. These findings demonstrate that advanced NLP methods can effectively analyze large-scale social media data, providing valuable insights into the multifaceted nature of suicidal ideation and emphasizing the need for targeted intervention strategies. Suggested improvements include enhancing relationship counseling and peer support networks, implementing school-based mental health programs, and leveraging social media for real-time support and awareness campaigns. By understanding the emotional and thematic nuances of online discussions, these strategies can more effectively address the multifaceted factors contributing to mental health challenges and reduce the risk of suicidal behavior.

Authors

Institutions

Publication Details

Journal
ACM Transactions on the Web
Published
2025-02-21
DOI
https://doi.org/10.1145/3716546
Citations
2
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
4.88
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Suicide Factors in Online Discourse: Sentiment and Thematic Analysis of Reddit

E. Dan, Jianfeng Zhu, Ruoming Jin
2 citations
ACM Transactions on the Web
Mental Health via Writing
4.88
article

Exploring Suicide Factors in Online Discourse: Sentiment and Thematic Analysis of Reddit

E. Dan, Jianfeng Zhu, Ruoming Jin
article en
2 citations

Abstract

Suicide remains a critical global health issue, with rising numbers claiming more lives each year despite ongoing prevention efforts. Current research has extensively explored factors influencing suicidal tendencies, emphasizing trauma, mental health disorders, and social relationships. However, traditional studies often relied on traditional data sources and often examined risk factors in isolation, which may not fully capture the dynamics observed in social media platforms. To address these limitations, our study utilizes data from r/SuicideWatch and r/Teenagers to analyze the emotional sentiment and explore themes associated with suicidal ideation, with r/Teenagers serving as a comparative reference. By leveraging natural language processing (NLP) techniques and statistical methodologies, including sentiment analysis and BERTopic modeling, we aim to gain deeper insights into the factors contributing to suicidal thoughts. Using TextBlob, our findings reveal a significant difference in sentiment between the two subreddits, with r/SuicideWatch posts predominantly expressing challenges and distressing emotions. Through BERTopic analysis, we identified key themes, such as emotional challenges related to romantic relationships, academic pressure, and substance use concerns in r/SuicideWatch, highlighting their strong association with suicidal ideation. While r/Teenagers had some similar themes regarding struggles with loneliness and academics, the topics were focused more on general adolescent concerns. These findings demonstrate that advanced NLP methods can effectively analyze large-scale social media data, providing valuable insights into the multifaceted nature of suicidal ideation and emphasizing the need for targeted intervention strategies. Suggested improvements include enhancing relationship counseling and peer support networks, implementing school-based mental health programs, and leveraging social media for real-time support and awareness campaigns. By understanding the emotional and thematic nuances of online discussions, these strategies can more effectively address the multifaceted factors contributing to mental health challenges and reduce the risk of suicidal behavior.

ACM Transactions on the WebVol. 19(4)
Kent State University (US)
Openalex Percentile: Top 7%
Mental Health via Writing
4.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.