A novel imbalanced data classification approach for suicidal ideation detection on social media

Suicide has become a serious social health issue in modern society. Suicidal ideation is people’s thoughts about committing or planning suicide. Many factors, such as long-term exposure to negative feelings or life events, can lead to suicidal ideation and suicide attempts. Among these approaches to suicide prevention, early detection of suicidal ideation is one of the most effective ways. Using social networking services provides a platform for people to express their sufferings and feelings in the real world, which provides a source for a deeper investigation into models and approaches for the detection of suicidal intent to enable prevention. This paper addresses the early detection of suicide ideation through the associative classification approach applied to Twitter social media. However, since the number of suicide intention tweets is tiny compared to the number of all the tweets, this leads us to an imbalanced classification problem, in which, the minority class (suicide intention) is more important than the majority class (no suicide intention). In such a situation, classical classifiers usually yield very inaccurate results regarding minor classes, since they can easily discover rules predicting the majority class and overlook those related to the minor. This paper aims to contribute to this line of research by introducing a new interestingness measure to enhance the classification process. This measure highlights the two classes regardless of their imbalanced distribution. Carried out experiments proved that the adapted CBA outweighs in terms of prediction accuracy the original one, and other pioneering baseline classification approaches.