Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.
|Name||International Conference on Machine Learning and Cybernetics (ICMLC)|
|Conference||2018 International Conference on Machine Learning and Cybernetics|
|Abbreviated title||ICMLC 2018|
|Period||15/07/18 → 18/07/18|
- Text classification
- Machine Classifier
- Social media