Nicolas Malandain

Measures for topical cohesion of user 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: the demand for a precise identification of these groups is increasing 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 interaction between users on Twitter, the reference on micro-blogging OSN, and 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, using two measures inspired from the classification domain, and one measure inspired from the NLP domain. A detailed analysis is performed on a big tweet dataset, from which a user graph is built. Introduced measures are compared with statistics to better picture the experiment, and yield interesting insights on a social and textual corpus.

Topical cohesion of communities on Twitter

Abstract 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.

Relevant Sources of Information Are Not Necessarily Popular Ones

The constant growth of the Web in recent years has made more difficult the discovery of new sources of information on a given topic. This is a prominent problem for Experts in Intelligence Analysis (EIA) who are faced to the search of pages on specific and sensitive topics. Because of their lack of popularity or because they are poorly indexed due to their sensitive content, these pages are hard-to-find with traditional search engines. In this article, we describe a new Web source discovery system called DOWSER (Discovery Of Web Sources Evaluating Relevance). The goal of this system is to provide users with new sources of information related to their needs without considering the popularity of a page unlike classic Information Retrieval tools. The expected result is a balance between relevance and originality, in the sense that the wanted pages are not necessary popular. DOWSER is based on a user profile to focus its exploration of the Web in order to collect and index only related Web documents. As requests can be insufficient to express sensitive and specific needs, the users information needs are specified using users interests represented by DBPedia resources [1] and keywords, both extracted from Web pages provided by the user. A series of experiments provides an empirical evaluation of DOWSER.

Extracting Contextonyms from Twitter for Stance Detection.

Opinion mining on tweets is a challenge: short texts, implicit topics, inventive spellings and new vocabulary are the rule. We aim at efficiently determining the stance of tweets towards a given target. We propose a method using the concept of contextonyms and contextosets in order to disambiguate implicit content and improve a given stance classifier. Contextonymy is extracted from a word co-occurrence graph, and allows to grasp the sense of a word according to its surrounding words. We evaluate our method on a freely available annotated tweet corpus, used to benchmark stance detection on tweets during SemEval2016.