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Deciphering Emotional and Linguistic Patterns in Reddit Suicidal Discourse

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Abstract

Recognizing and understanding the characteristics of suicidal posts on social media are crucial for early intervention and suicide prevention efforts. This study aims to comprehend various traits associated with suicidal posts on Reddit, a prominent social media platform. By analyzing user posts gathered from the Reddit forum ‘r/SuicideWatch’, we aim to scrutinize the various emotional and linguistic signals intertwined with suicidal content. Furthermore, we seek to identify some of the key factors contributing to the development of suicidal ideation. Our analysis reveals a substantially higher prevalence of negative emotions, particularly anger and sadness, in suicidal posts. Linguistic analysis uncovers several distinguishing signals associated with suicidal posts, such as a high presence of verbs and a significant frequency of negations. An initial content analysis reveals a diverse array of key factors, including social isolation, health issues, personal failures, and traumatic life events, among others. Overall, our investigation unveils diverse characteristics of suicidal posts and key factors contributing to suicidal ideation that can facilitate the automated detection of suicidal tendencies and support the implementation of effective preventive measures.

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References

  1. Abdul-Mageed, M., Ungar, L.: EmoNet: fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 718–728. Association for Computational Linguistics, Vancouver (2017)

    Google Scholar 

  2. De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 7, pp. 128–137 (2013)

    Google Scholar 

  3. Honnibal, M., Montani, I.: spaCy 2: natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing (2017)

    Google Scholar 

  4. Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014)

    Google Scholar 

  5. Im, Y., Oh, W.O., Suk, M.: Risk factors for suicide ideation among adolescents: five-year national data analysis. Arch. Psychiatr. Nurs.31(3), 282–286 (2017)

    Article  Google Scholar 

  6. Ji, S., Pan, S., Li, X., Cambria, E., Long, G., Huang, Z.: Suicidal ideation detection: a review of machine learning methods and applications. IEEE Trans. Comput. Social Syst.8(1), 214–226 (2020)

    Article  Google Scholar 

  7. Lao, C., Lane, J., Suominen, H., et al.: Analyzing suicide risk from linguistic features in social media: evaluation study. JMIR Format. Res.6(8), e35563 (2022)

    Article  Google Scholar 

  8. Levine, L.: The distribution of deontic modals in jane austen’s mature novels. In: Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 70–74 (2022)

    Google Scholar 

  9. Mohammad, S., Turney, P.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34 (2010)

    Google Scholar 

  10. Ramírez-Cifuentes, D., et al.: Detection of suicidal ideation on social media: multimodal, relational, and behavioral analysis. J. Med. Internet Res.22(7), e17758 (2020)

    Article  Google Scholar 

  11. Sazzed, S.: Banglabiomed: a biomedical named-entity annotated corpus for bangla (Bengali). In: Proceedings of the 21st Workshop on Biomedical Language Processing, pp. 323–329 (2022)

    Google Scholar 

  12. Sazzed, S.: A comparative study of affective and linguistic traits in online depression and suicidal discussion forums. In: Proceedings of the 34th ACM Conference on Hypertext and Social Media, pp. 1–6 (2023)

    Google Scholar 

  13. Sazzed, S.: Discourse mode categorization of Bengali social media health text. In: Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pp. 52–57 (2023)

    Google Scholar 

  14. Shing, H.C., Nair, S., Zirikly, A., Friedenberg, M., Daumé III, H., Resnik, P.: Expert, crowdsourced, and machine assessment of suicide risk via online postings. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 25–36 (2018)

    Google Scholar 

  15. Sinha, P.P., Mishra, R., Sawhney, R., Mahata, D., Shah, R.R., Liu, H.: # suicidal-a multipronged approach to identify and explore suicidal ideation in twitter. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 941–950 (2019)

    Google Scholar 

  16. Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of depression-related posts in reddit social media forum. IEEE Access7, 44883–44893 (2019)

    Article  Google Scholar 

  17. Zirikly, A., Resnik, P., Uzuner, O., Hollingshead, K.: Clpsych 2019 shared task: predicting the degree of suicide risk in reddit posts. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 24–33 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Georgia Southern University, Statesboro, GA, USA

    Salim Sazzed

  2. University of Memphis, Memphis, TN, USA

    Salim Sazzed

Authors
  1. Salim Sazzed

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Correspondence toSalim Sazzed.

Editor information

Editors and Affiliations

  1. United States Military Academy, Army Cyber Institute, West Point, NY, USA

    Robert Thomson

  2. The University of the Cumberlands, Williamsburg, KY, USA

    Aravind Hariharan

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Scott Renshaw

  4. Creighton University, Omaha, NE, USA

    Samer Al-khateeb

  5. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Annetta Burger

  6. Carnegie Mellon University, Pittsburg, PA, USA

    Patrick Park

  7. Army Cyber Institute, West Point, NY, USA

    Aryn Pyke

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Ethical Statement and Limitation

This study utilizes Reddit data that is openly accessible on Kaggle (https://www.kaggle.com/). The research ensures that no personal user information is gathered, utilized, or revealed throughout the analysis or subsequently. Since this study exclusively analyzes suicidal posts sourced from Reddit, there could be inherent bias in the dataset, as it is derived solely from one social media platform and may not capture the diversity of viewpoints and experiences present across other digital platforms.

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Sazzed, S. (2024). Deciphering Emotional and Linguistic Patterns in Reddit Suicidal Discourse. In: Thomson, R.,et al. Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2024. Lecture Notes in Computer Science, vol 14972. Springer, Cham. https://doi.org/10.1007/978-3-031-72241-7_13

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