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Detection of suicidal Twitter posts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

As web data evolves, new technological challenges arise and one of the contributing factors to these challenges is the online social networks. Although they have some benets, their negative impact on vulnerable users such as the spread of suicidal ideation is concerning. As such, it is vital to ne tune the approaches and techniques in order to understand the users and their context for early intervention. Therefore, in this study, we measured the impact of data manipulation and feature extraction, specifically using N-grams, on suicide-related social network text (tweets). We propose a diversified ensemble approach (multi-classifier fusion) to improve the detection of suicide-related text classification. Four machine classifiers were used for the fusion: Support Vector Machine, Random Forest, Naive Bayes and Decision Tree. The results of our proposed approach have shown that the multi-classifier fusion has improved the detection of suicide-related text and, also, that Support Vector Machine has shown some promising results when dealing
with multi-class datasets.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationUKCI 2019
EditorsZhojie Ju, Longzhi Yang, Chenguang Yang, Alexander Gegov, Dalin Zhou
Number of pages12
ISBN (Electronic)978-3-030-29933-0
ISBN (Print)978-3-030-29932-3
Publication statusPublished - 30 Aug 2019
Event19th UK Workshop on Computational Intelligence - Portsmouth, United Kingdom
Duration: 4 Sep 20195 Sep 2019
Conference number: 19

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Workshop19th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2019
CountryUnited Kingdom
OtherThe UKCI 2019 covers both theory and applications in computational intelligence. The topics of interest include
Fuzzy Systems
Neural Networks
Evolutionary Computation
Evolving Systems
Machine Learning
Data Mining
Cognitive Computing
Intelligent Robotics
Hybrid Methods
Deep Learning
Applications of Computational Intelligence
Internet address


  • UKCI2019

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Ju Z., Yang L., Yang C., Gegov A., Zhou D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. The final authenticated version is available online at:

    Accepted author manuscript (Post-print), 395 KB, PDF document

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