Topical cohesion of communities on Twitter

Nowadays, Online Social Networks (OSN) are commonly used by groups of users to communicate. Members of a family, colleagues, fans of a brand, political groups... There is an increasing demand for a precise identification of these groups, coming from brand monitoring, business intelligence and e-reputation management. However, a gap can be observed between the communities detected by many data analytics algorithms on OSN, and effective groups existing in real life: the detected communities often lack of meaning and internal semantic cohesion. Most of existing literature on OSN either focuses on the community detection problem in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. In this article, we support the hypothesis that communities extracted on OSN should be topically coherent. We therefore propose a model to represent the groups of interaction on Twitter, the reference on micro-blogging OSN, and two metrics to evaluate the topical cohesion of the detected communities. As an evaluation, we measure the topical cohesion of the groups of users detected by a baseline community detection algorithm. (C) 2017 The Authors. Published by Elsevier B.V.