Controversy refers to content attracting different point-of-views, as well as positive and negative feedback on a specific event, gathering users into different communities. Research on controversy led to two main categories of works: controversy detection/quantification and controversy explainability. When the former aims to quantify controversy on a topic, the latter aims to understand why a topic is controversial or not. This paper mainly contributes to the controversy explainability. We analyze topic discussions on Twitter from the community perspective to investigate the power of text in classifying tweets into the right community. We propose a SHAP-based pipeline to quantify impactful text features on predictions of three tweet classifiers. We also rely on the use of different text features namely BERT, TF − IDF, and LIWC. The results we obtain from both SHAP plots and statistical analysis show clearly significant impacts of some text features in classifying tweets.It also highlights the relevance of the study as well as the potential benefits of combining text and user interactions to quantify controversy.

Mental and physical health are strongly linked in a bidirectional relationship. Due to the stigma, ignorance, prejudice, fear, and many other reasons, there exists a large universal treatment gap for people with mental disorders. This could motivate those at-risk individuals to find their way into social networks, asking for information or emotional support. Language could provide a natural eyepiece for the study and detection of such at-risk individuals through their writings on social media platforms. In this paper, we consider the problem of detecting at-risk users with clear signs of depression, anorexia, self-harm, and suicidal thoughts. We introduce NCNL, a novel deep learning ensemble architecture that makes use of multiple noisy base learners in Negative Correlation Learning (NCL) configuration for text classification. NCNL is designed to be, backbone-independent, and we examine it with modern Transformer-based architectures. We evaluate our models on six different tasks for at-risk user detection and classification. Our models achieve significant improvements over existing state-of-the-art results reported for five out of the six tasks. Extensive experiments show how NCNL improves diversity over the classical conventional ensemble and the effect of using noisy base learners.