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Purpose: Most of the existing literature on online social networks (OSNs) either focuses on community detection in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. The purpose of this paper is to characterise the semantic cohesion of such groups through the introduction of new measures. Design/methodology/approach: A theoretical model for social links and salient topics on Twitter is proposed. Also, measures to evaluate the topical cohesiveness of a group are introduced. Inspired from precision and recall, the proposed measures, called expertise and representativeness, assess how a set of groups match the topic distribution. An adapted measure is also introduced when a topic similarity can be computed. Finally, a topic relevance measure is defined, similar to tf.idf (term-frequency, inverse document frequency). Findings: The measures yield interesting results, notably on a large tweet corpus: the metrics accurately describe the topics discussed in the tweets and enable to identify topic-focused groups. Combined with topological measures, they provide a global and concise view of the detected groups. Originality/value: Many algorithms, applied on OSN, detect communities which often lack of meaning and internal semantic cohesion. This paper is among the first to quantify this aspect, and more precisely the topical cohesion and topical relevance of a group. Moreover, the proposed indicators can be exploited for social media monitoring, to investigate the impact of a group of people: for instance, they could be used for journalism, marketing and security purposes.

This study describes a new heuristic to discover behavioural patterns in annotated dialogues: firstly, dialogues are transcribed and annotated; then, two-dimensional patterns are extracted; finally, the semantic pertinence of the extracted patterns can be evaluated by an expert. The dialogues are annotated by using a grid designed by the expert. Recurrent patterns are extracted in the annotated dialogues using dynamic programming with the help of a substitution matrix specifically designed for the task. This article focuses on the method developed for extracting the patterns and presents as application some extracted patterns on annotations of dialogues between parents and children during the narration of child stories.