IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v610y2023ics0378437122008949.html
   My bibliography  Save this article

Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities

Author

Listed:
  • Joseph, Simmi Marina
  • Citraro, Salvatore
  • Morini, Virginia
  • Rossetti, Giulio
  • Stella, Massimo

Abstract

Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding “feel”, stem for “to feel” and “feelings”, enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of “regret”, “unhealthy lifestyle” and “low mental well-being”. Importantly, negative associations and emotions co-existed with trustful/positive language, focused on “getting better”. This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.

Suggested Citation

  • Joseph, Simmi Marina & Citraro, Salvatore & Morini, Virginia & Rossetti, Giulio & Stella, Massimo, 2023. "Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    • Handle: RePEc:eee:phsmap:v:610:y:2023:i:c:s0378437122008949
    DOI: 10.1016/j.physa.2022.128336
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122008949
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128336?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Olga Valba & Alexander Gorsky & Sergei Nechaev & Mikhail Tamm, 2021. "Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    2. Camilo Akimushkin & Diego Raphael Amancio & Osvaldo Novais Oliveira Jr., 2017. "Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.
    3. Dong Wang & Jiexun Li & Kaiquan Xu & Yizhen Wu, 2017. "Sentiment community detection: exploring sentiments and relationships in social networks," Electronic Commerce Research, Springer, vol. 17(1), pages 103-132, March.
    4. Cynthia S. Q. Siew & Dirk U. Wulff & Nicole M. Beckage & Yoed N. Kenett, 2019. "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics," Complexity, Hindawi, vol. 2019, pages 1-24, June.
    5. Shaoxiong Ji & Celina Ping Yu & Sai-fu Fung & Shirui Pan & Guodong Long, 2018. "Supervised Learning for Suicidal Ideation Detection in Online User Content," Complexity, Hindawi, vol. 2018, pages 1-10, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yun Gu & Deyuan Chen & Xiaoqian Liu, 2022. "Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results," IJERPH, MDPI, vol. 20(1), pages 1-11, December.
    2. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    3. Gisela Redondo-Sama & Teresa Morlà-Folch & Ana Burgués & Jelen Amador & Sveva Magaraggia, 2021. "Create Solidarity Networks: Dialogs in Reddit to Overcome Depression and Suicidal Ideation among Males," IJERPH, MDPI, vol. 18(22), pages 1-15, November.
    4. Stefan Claus & Massimo Stella, 2022. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts," Future Internet, MDPI, vol. 14(10), pages 1-18, October.
    5. Liu, Yanyan & Li, Keping & Yan, Dongyang & Gu, Shuang, 2022. "A network-based CNN model to identify the hidden information in text data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    6. Espitia, Diego & Larralde, Hernán, 2020. "Universal and non-universal text statistics: Clustering coefficient for language identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    7. Ammar Al-Sharaa & Mastura Adam & Amer Siddiq Amer Nordin & Riyadh Mundher & Ameer Alhasan, 2022. "Assessment of Wayfinding Performance in Complex Healthcare Facilities: A Conceptual Framework," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    8. Yoshiaki Fujita & Michael S. Vitevitch, 2022. "Using network analyses to examine the extent to which and in what ways psychology is multidisciplinary," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    9. Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "Community Detection Based on Node Influence and Similarity of Nodes," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    10. Swarup Chattopadhyay & Tanmay Basu & Asit K. Das & Kuntal Ghosh & Late C. A. Murthy, 2021. "Towards effective discovery of natural communities in complex networks and implications in e-commerce," Electronic Commerce Research, Springer, vol. 21(4), pages 917-954, December.
    11. Stella, Massimo, 2020. "Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    12. Olga Valba & Alexander Gorsky & Sergei Nechaev & Mikhail Tamm, 2021. "Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    13. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    14. Theyazn H. H. Aldhyani & Saleh Nagi Alsubari & Ali Saleh Alshebami & Hasan Alkahtani & Zeyad A. T. Ahmed, 2022. "Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    15. Qing Huan & Niu ZhanWen, 2018. "Knowledge management in consultancy involved LPS implementation projects via social media," Electronic Commerce Research, Springer, vol. 18(1), pages 89-107, March.
    16. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
    17. Wei Pan & Xianbin Wang & Wenwei Zhou & Bowen Hang & Liwen Guo, 2023. "Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches," IJERPH, MDPI, vol. 20(3), pages 1-12, February.
    18. Kejia Chen & Jian Jin & Zheng Zhao & Ping Ji, 2022. "Understanding customer regional differences from online opinions: a hierarchical Bayesian approach," Electronic Commerce Research, Springer, vol. 22(2), pages 377-403, June.
    19. Michal Ptaszynski & Monika Zasko-Zielinska & Michal Marcinczuk & Gniewosz Leliwa & Marcin Fortuna & Kamil Soliwoda & Ida Dziublewska & Olimpia Hubert & Pawel Skrzek & Jan Piesiewicz & Paula Karbowska , 2021. "Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media—A Pragmalinguistic Approach," IJERPH, MDPI, vol. 18(22), pages 1-49, November.
    20. Brian, Kieran & Stella, Massimo, 2023. "Introducing mindset streams to investigate stances towards STEM in high school students and experts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:610:y:2023:i:c:s0378437122008949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.