Social media platforms assemble people to discuss various types of topics and to share information. Increasingly, the use of social media is exceeding the scope of its purposes. For instance, some people utilize social media to mobilize an important number of proponents to organize different types of demonstrations, political demands, and political movements and events. In this paper, we are interested in investigating the evolution of the Western exit (Wexit) movement in social media. Specifically, we jointly perform sentiment analysis and topic models to track over time the sentiment polarity of Wexit-related topics, and to discover ever-growing communities and the influence that they have over other communities. The experimental study on the Wexit movement data showed the performance of our proposed method and its suitability for tracking communities over time compared to state-of-the art methods.

Psychology research findings suggest that personality is related to differences in friendship characteristics and that some personality traits correlate with linguistic behavior. In this paper, we investigate the influence that personality may have on affinity formation. To this end, we derive affinity relationships from social media interactions, examine personality based on language use to discover the emotional stability of affinity relationships, and measure semantic similarity at the personality type level to understand the logic behind the development of affinity. Specifically, we conduct extensive experiments using a publicly available dataset containing information on individuals who self-identified with a Myers-Briggs personality type. Our results identify certain influential personality types that weigh more heavily on affinity relationships and show that personality can be predicted from spontaneous language with an F-1 score superior to 0.76. Future research avenues are proposed.

Understanding the factors that lead or contribute to emotional instability in highly motivated high-conflict dialogues such as law enforcement interviews can be of crucial importance. In this paper, we extract psycholinguistic features to assess emotional stability scale development and identify patterns that are relevant to emotional breakdown. To this end, we utilize zero-shot text classification to investigate the temporal evolution of emotion during law enforcement interviews. We conduct ex-tensive experiments using publicly available police interrogation transcripts. Our results are promising and suggest avenues for future research.

Early detection of behavioral deterioration can be of great importance in preventing individuals' misbehavior from escalating in severity. This paper addresses the problem of behavioral deterioration in the context of online discussion forums. We propose a novel method that builds behavioral sequences from temporal information to gain a better understanding of behaviors exhibited by forum members, and then explores n-gram features to predict behavioral deterioration from consecutive combinations of sequential patterns corresponding to misbehavior. We conduct extensive experiments using real-world datasets and demonstrate the ability of our method to predict behavioral deterioration with a high degree of accuracy, as evaluated by F-1 scores. Our quantitative analysis of the model's performance yields F-1 scores of over 0.7. Specifically, we find that the best-performing model is linear SVM, with an average F-1 score of 0.74. Some future research avenues are proposed.

Stance detection is the task of automatically determining whether the author of a piece of text is in favor of, against, or neutral towards a target such as a topic, entity, or claim. In this paper, we propose a method based on RoBERTa to classify stances by capturing the context of the discussion through the examination of pairs of stances and relational structures of debates specific to each topic within the defined window of each forum participant's interventions. Furthermore, we examine the degree of disagreement and neutrality in various debate topics to measure divergence of opinion in the course of the debate and estimate the emotional state manifested in different debate topics. We conduct extensive experiments using two publicly available datasets and demonstrate that our method considers more stance classes, provides better results and yields statistical improvements over existing techniques. Our quantitative analysis of model performance yields F-1 scores of over 0.745. Interestingly, we obtained the highest F-1 score, 0.814, on a stance class which was not taken into consideration in prior work. We report that none of the metrics utilized to measure divergence of opinion yield values exceeding 50 % and the correlations between the same topics over 10-fold cross-validation are statistically significant for the majority of them (p <; 0.005). Several future research avenues are proposed.

This paper addresses the problem of discovering hidden affinity relationships in online communities. Online discussions assemble people to talk about various types of topics and to share information. People progressively develop the affinity, and they get closer as frequently as they mention themselves in messages and they send positive messages to one another. We propose an algorithm, named HAR-search, for discovering hidden affinity relationships between individuals. Based on Markov Chain Models, we derive the affinity scores amongst individuals in an online community. We show that our method allows to track the evolution of the affinity over time and to predict affinity relationships arisen from the influence of certain community members. The comparison with the state-of-the-art method shows that our method results in robust discovery and considers minute details.