Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3487553.3524700acmconferencesArticle/Chapter ViewAbstractPublication PageswebconfConference Proceedingsconference-collections
short-paper
Public Access

”I’m always in so much pain and no one will understand” - Detecting Patterns in Suicidal Ideation on Reddit

Published: 16 August 2022 Publication History
  • Get Citation Alerts
  • Abstract

    Social media has become another venue for those struggling with thoughts of suicide. Many turn to social media to express suicidal ideation and look for peer support. In our study we seek to better understand patterns in the behaviors of these users particularly on the social media platform Reddit. This study will explore how Reddit users move or progress between subreddits until they express active suicidal ideation. We also look at these users’ posting pattern in the time leading up to expressing suicidal ideation and the time after. We examined a large dataset of posts from users who created at least one thread on SuicideWatch during January 2019 - August 2019 and collected their posts starting in July 2018 to create a look back period of 6 months. This generated a total of 5,892,310 posts. We defined what it means to progress between subreddits and generated a graph of progressions of all users in our dataset. We found that these users mostly progressed to or from 8 different subreddits and each of these subreddits could point to a particular emotional difficulty that a user was having such as self harm or relationship problems. Furthermore, we examined the volume of posts and the proportion of posts with negative sentiment leading up to the first incident of active suicidal ideation and found that there is an increase in both negative sentiment and volume of posts leading up to the day of the first incident of suicidal ideation on Reddit. However, during the day of first incident of suicidal ideation, there is a precipitous drop in the number of posts which goes back up on the following day. Using this insight, we can better understand these users. This will allow for developing intervention for suicide prevention in social media platforms in the future.

    References

    [1]
    2019. Columbia-Suicide Severity Rating Scale (C-SSRS). https://www.hrsa.gov/behavioral-health/columbia-suicide-severity-rating-scale-c-ssrs Last Modified: 2019-08-02T18:06-04:00.
    [2]
    2022. FastStats: Suicide and Self-Harm Injury. https://www.cdc.gov/nchs/fastats/suicide.htm
    [3]
    2022. SuicideWatch - Peer support for anyone struggling with suicidal thoughts. https://www.reddit.com/r/SuicideWatch/
    [4]
    Philip J. Batterham, Jennie Walker, Liana S. Leach, Jennifer Ma, Alison L. Calear, and Helen Christensen. 2018. A longitudinal test of the predictions of the interpersonal-psychological theory of suicidal behaviour for passive and active suicidal ideation in a large community-based cohort. Journal of Affective Disorders 227 (Feb. 2018), 97–102. https://doi.org/10.1016/j.jad.2017.10.005
    [5]
    Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020. The Pushshift Reddit Dataset. Proceedings of the International AAAI Conference on Web and Social Media 14 (May 2020), 830–839. https://ojs.aaai.org/index.php/ICWSM/article/view/7347
    [6]
    G Borges and H Rosovsky. 1996. Suicide attempts and alcohol consumption in an emergency room sample.Journal of Studies on Alcohol 57, 5 (Sept. 1996), 543–548. https://doi.org/10.15288/jsa.1996.57.543 Publisher: Alcohol Research Documentation, Inc.
    [7]
    Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 3, 1 (March 2020), 1–11. https://doi.org/10.1038/s41746-020-0233-7 Number: 1 Publisher: Nature Publishing Group.
    [8]
    Glen Coppersmith, Ryan Leary, Patrick Crutchley, and Alex Fine. 2018. Natural Language Processing of Social Media as Screening for Suicide Risk. Biomedical Informatics Insights 10 (Jan. 2018), 117822261879286. https://doi.org/10.1177/1178222618792860
    [9]
    Glen Coppersmith, Kim Ngo, Ryan Leary, and Anthony Wood. 2016. Exploratory Analysis of Social Media Prior to a Suicide Attempt. In Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology. Association for Computational Linguistics, San Diego, CA, USA, 106–117. https://doi.org/10.18653/v1/W16-0311
    [10]
    Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen Coppersmith, and Mrinal Kumar. 2016. Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media. Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference 2016 (May 2016), 2098–2110. https://doi.org/10.1145/2858036.2858207
    [11]
    Behrooz Ghanbari, Seyed Kazem Malakouti, Marzieh Nojomi, Diego De Leo, and Khalid Saeed. 2016. Alcohol Abuse and Suicide Attempt in Iran: A Case-Crossover Study. Global Journal of Health Science 8, 7 (July 2016), 58–67. https://doi.org/10.5539/gjhs.v8n7p58
    [12]
    C. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media 8, 1 (May 2014). https://ojs.aaai.org/index.php/ICWSM/article/view/14550 Number: 1.
    [13]
    Alejandro Interian, Megan Chesin, Anna Kline, Rachael Miller, Lauren St. Hill, Miriam Latorre, Anton Shcherbakov, Arlene King, and Barbara Stanley. 2018. Use of the Columbia-Suicide Severity Rating Scale (C-SSRS) to Classify Suicidal Behaviors. Archives of Suicide Research 22, 2 (April 2018), 278–294. https://doi.org/10.1080/13811118.2017.1334610
    [14]
    Jiaxin Liu, Elissa R. Weitzman, and Rumi Chunara. 2017. Assessing Behavior Stage Progression From Social Media Data. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, Portland Oregon USA, 1320–1333. https://doi.org/10.1145/2998181.2998336
    [15]
    Jorge Lopez‐Castroman, Bilel Moulahi, Jérôme Azé, Sandra Bringay, Julie Deninotti, Sebastien Guillaume, and Enrique Baca‐Garcia. 2020. Mining social networks to improve suicide prevention: A scoping review. Journal of Neuroscience Research 98, 4 (April 2020), 616–625. https://doi.org/10.1002/jnr.24404
    [16]
    Daniel Low, Kelly Zuromski, Daniel Kessler, Satrajit S. Ghosh, Matthew K. Nock, and Walter Dempsey. 2021. It’s quality and quantity: the effect of the amount of comments on online suicidal posts. In Proceedings of the First Workshop on Causal Inference and NLP. Association for Computational Linguistics, Punta Cana, Dominican Republic, 95–103. https://aclanthology.org/2021.cinlp-1.8
    [17]
    Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, and Rajiv Ratn Shah. 2019. SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media. (2019), 10.
    [18]
    James O Prochaska, Sara Johnson, and Patricia Lee. 1998. The transtheoretical model of behavior change.(1998).
    [19]
    Jo Robinson, Maria Rodrigues, Steve Fisher, Eleanor Bailey, and Helen Herrman. 2015. Social media and suicide prevention: findings from a stakeholder survey. Shanghai Archives of Psychiatry 27, 1 (Feb. 2015), 27–35. https://doi.org/10.11919/j.issn.1002-0829.214133
    [20]
    Ramit Sawhney, Harshit Joshi, Saumya Gandhi, and Rajiv Ratn Shah. 2020. A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 7685–7697. https://doi.org/10.18653/v1/2020.emnlp-main.619
    [21]
    World Health Organization. 2021. Suicide worldwide in 2019: global health estimates. World Health Organization, Geneva. https://apps.who.int/iris/handle/10665/341728 Section: iv, 28 p.
    [22]
    Ayah Zirikly, Philip Resnik, Ozlem Uzuner, and Kristy Hollingshead. 2019. CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts. (2019), 10.

    Cited By

    View all
    • (2024)Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory ModelJournal of Medical Internet Research10.2196/4890726(e48907)Online publication date: 8-Aug-2024
    • (2023)Report on the 8th International Workshop on Mining Actionable Insights from Social Networks (MAISoN'22) - Special Edition on Mental Health and Social Media at TheWebConf 2022ACM SIGIR Forum10.1145/3636341.363634957:1(1-6)Online publication date: 4-Dec-2023
    • (2023)Temporal Arcs of Mental Health: Patterns Behind Changes in Depression over Time2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388098(1-4)Online publication date: 10-Sep-2023

    Index Terms

    1. ”I’m always in so much pain and no one will understand” - Detecting Patterns in Suicidal Ideation on Reddit

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      WWW '22: Companion Proceedings of the Web Conference 2022
      April 2022
      1338 pages
      ISBN:9781450391306
      DOI:10.1145/3487553
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 16 August 2022

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. health informatics
      2. mental health
      3. social media

      Qualifiers

      • Short-paper
      • Research
      • Refereed limited

      Funding Sources

      • NSF

      Conference

      WWW '22
      Sponsor:
      WWW '22: The ACM Web Conference 2022
      April 25 - 29, 2022
      Virtual Event, Lyon, France

      Acceptance Rates

      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)183
      • Downloads (Last 6 weeks)27
      Reflects downloads up to 09 Aug 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Identifying Reddit Users at a High Risk of Suicide and Their Linguistic Features During the COVID-19 Pandemic: Growth-Based Trajectory ModelJournal of Medical Internet Research10.2196/4890726(e48907)Online publication date: 8-Aug-2024
      • (2023)Report on the 8th International Workshop on Mining Actionable Insights from Social Networks (MAISoN'22) - Special Edition on Mental Health and Social Media at TheWebConf 2022ACM SIGIR Forum10.1145/3636341.363634957:1(1-6)Online publication date: 4-Dec-2023
      • (2023)Temporal Arcs of Mental Health: Patterns Behind Changes in Depression over Time2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388098(1-4)Online publication date: 10-Sep-2023

      View Options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Get Access

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media