Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
- Title
- Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
- Creator
- Adeola, Lare; Iwendi, Celestine; Sharma, Vandana; Al-Khasawneh, Mahmoud Ahmad
- Description
- Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from users tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users tweets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
- Source
- Learning and Analytics in Intelligent Systems;Volume;43;pp.475-488
- Date
- 01-01-2025
- Publisher
- Springer Nature
- Subject
- Applied machine learning; Document similarity; Natural language processing; Suicide detection
- Coverage
- Adeola L., University of Greater Manchester, Manchester, United Kingdom; Iwendi C., University of Greater Manchester, Manchester, United Kingdom; Sharma V., Christ University, Bengaluru, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, Sharjah, United Arab Emirates
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 26623447;
- Format
- online
- Language
- English
- Type
- Book chapter
Collection
Citation
Adeola, Lare; Iwendi, Celestine; Sharma, Vandana; Al-Khasawneh, Mahmoud Ahmad, “Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24170.