Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Adeola, Lare; Iwendi, Celestine; Sharma, Vandana; Al-Khasawneh, Mahmoud Ahmad Title A name given to the resource Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Learning and Analytics in Intelligent Systems;Volume;43;pp.475-488 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9855-1_34" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9855-1_34</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007033888?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007033888?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Applied machine learning; Document similarity; Natural language processing; Suicide detection Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 26623447; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online