Social Users Interactions Detection Based on Conversational Aspects

Rami Belkaroui (1, 2) , Rim Faiz (2) , Aymen Elkhlifi (2)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus

Last years, people are becoming more communicative through expansion of services and multi-platform applications such as blogs, forums and social networks which establishes social and collabo-rative backgrounds. These services like Twitter, which is the main domain used in our work can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works have proposed tools for tweets search focused only to retrieve the most recent but relevant tweets that address the information need. Therefore, users are unable to explore the results or retrieve more relevant tweets based on the content and may get lost or become frustrated by the information overload. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on mi-croblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. The proposed method has been implemented and evaluated by comparing it to Google and Twitter Search engines and we obtained very promising results.

Mots clés: Twitter; Conversation retrieval; Social user interactions; Social Network; Conversational context

Domaines: Informatique [cs]

Rami Belkaroui, Rim Faiz, Aymen Elkhlifi. Social Users Interactions Detection Based on Conversational Aspects. New Trends in Intelligent Information and Database Systems, 598, pp.161 - 170, 2015, ⟨10.1007/978-3-319-16211-9_17⟩. ⟨hal-01389804⟩