Multi-agents systems are composed of autonomous and possibly heterogeneous software agents that act according to their own interests. Some coordination mechanism must be adopted to ensure a proper functioning of the whole system. Norms can be viewed as a powerful means to regulate and influence the behaviour of the agents by specifying, for instance, obligations, permissions, or prohibitions in a given context. A critical issue that must be considered in a system governed by multiple norms is the possible existence of normative conflicts. A conflict between norms is a situation in which the fulfilment of a norm causes a violation of another one. In this paper, we present several techniques that have been proposed to detect and resolve normative conflicts in multi-agent systems. Our aim is to organize the literature, present a classification of the techniques found, and provide a means to compare alternative approaches dealing with normative conflicts.

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Analyzing electoral trends in political scenarios using social media with data mining techniques has become popular in recent years. A problem in this field is to reliably annotate data during the short period of electoral campaigns. In this paper, we present a methodology to measure labeling divergence and an exploratory analysis of data related to the 2018 Brazilian Presidential Elections. As a result, we point out some of the main characteristics that lead to a high level of divergence during the annotation process in this domain. Our analysis shows a high degree of divergence mainly in regard to sentiment labels. Also, a significant difference was identified between labels obtained by manual annotation and labels obtained using an automatic annotation approach.