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.
Contributions
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).
Selected Publications
- A predictive model for the dynamics of information diffusion in online social networks by Adrien Guille, Hakim Hacid. International Workshop on Mining Social Network Dynamics @ International ACM World Wide Web Conference (MSND @ WWW), 2012
- Information diffusion in online social networks: a survey by Adrien Guille, Hakim Hacid, Cécile Favre, Djamel A. Zighed. ACM SIGMOD Record, vol. 42 (2), 2013 - Highly Cited: ranks in the top 1% by citations for field and year (Thomson Reuters Web of Science)
- SONDY: an open source platform for social dynamics mining and analysis by Adrien Guille, Cécile Favre, Hakim Hacid, Djamel A. Zighed. International ACM Conference on Management of Data (SIGMOD), 2013 - Core A*
- Mention-anomaly-based event detection and tracking in Twitter by Adrien Guille, Cécile Favre. International IEEE/ACM Conference on Advances in Social Network Analysis and Mining (ASONAM), 2014
- Event detection, tracking and visualization in Twitter: a mention-anomaly-based approach by Adrien Guille, Cécile Favre. Springer Social Network Analysis and Mining, vol. 5 (1), 2015
Code