Many online collaborative games, e-sports in particular, heavily rely on teamwork. However, players can act in an antisocial way during the match, creating dissent into the match. This kind of behavior is referred to as toxic. We aim to discover the influence brought by toxic behavior in a popular e-sport, League of Legends, through the study of communication patterns of players during the match. We discovered that different communication patterns exist, and that they are directly related to player performance and level of toxic behavior. We also propose metrics to analyze players' performance and the toxic contamination level, which measures the negative impacts of the toxic behavior. Our analysis contributes to shed light on how players behave in an online game, and opens ways to provide a better ambience on the online video game community.

Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. In spite of many solutions proposed for the automatic recognition of depression, fewer exist for anxiety and its comorbidity with depression. In this paper, we propose DAC Stacking, a solution that leverages stacking ensembles and Deep Learning (DL) to automatically identify depression, anxiety, and their comorbidity, using data extracted from Reddit. The stacking is composed of single-label binary classifiers, that either distinguish between specific disorders and control users (experts), or between pairs of target conditions (differentiating). A meta-learner explores these base classifiers as a context for reaching a multi-label decision. We assessed alternative ensemble topologies, exploring roles for base models, DL architectures, and word embeddings. All base classifiers and ensembles outperformed the baselines for depression and anxiety (f-measures near 0.79). The ensemble topology with the best performance (Hamming Loss of 0.29 and Exact Match Ratio of 0.46) combines base classifiers of three DL architectures, and includes expert and differentiating base models. The analysis of the influential classification features according to SHAP revealed the strengths of our solution and provided insights on the challenges for the automatic classification of the addressed mental conditions.

Sentiment analysis has been adopted in software engineering for problems such as software usability and sentiment of developers in open-source projects. This paper proposes a method to evaluate the sentiment contained in tickets for IT (Information Technology) support.IT tickets are broad in coverage (e.g. infrastructure, software), and involve errors, incidents, requests, etc. The main challenge is to automatically distinguish between factual information, which is intrinsically negative (e.g. error description), from the sentiment embedded in the description. Our approach is to automatically create a Domain Dictionary that contains terms with sentiment in the IT context, used to filter terms in ticket for sentiment analysis. We experiment and evaluate three approaches for calculating the polarity of terms in tickets. Our study was developed using 34,895 tickets from five organizations, from which we randomly selected 2,333 tickets to compose a Gold Standard. Our best results display an average precision and recall of 82.83% and 88.42%, which outperforms the compared sentiment analysis solutions.