Social media have greatly modified the way we produce, diffuse and consume information, and have become powerful information vectors. The goal of this project was to help in the understanding of the information diffusion phenomenon in social media by providing means of modeling and analysis.
First, we’ve designed a probabilistic model based on the network structure underlying social media for predicting information diffusion and proposed an efficient way to estimate its parameters: T-BASIC (work presented at MSND@WWW 2012). Next, we’ve developped a statistical method for automatically detecting events that most interest social media users from the stream of messages they publish, based on mention anomaly: MABED (work presented at ASONAM 2014 and published in Social Network Analysis and Mining, vol. 5). We’ve also developed an open-source software to analyze events and identify influential users from social media data (work presented at SIGMOD 2013).