ShareIt: Mining SocialMedia Activities for Detecting Events

The list of social networking websites is diverse across the globe but the popularity of social media is indisputable. The 640M+ Facebook users, the 480M+ QZone users or the 200M+ Twitter users are used to share observations, opinions and media acting as citizen sensors of the society. This has given an unprecedented access to a vast amount of data on which research communities can perform social media analytics to support socially intelligent applications such as targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped countries. This lecture will address challenges for building applications using social media. It will focus on the notion of event for structuring the user online activities. We will briefly show how semantic web technologies can be used to break the silos in which each social network platform tends to lock the users. We will then present methods combining semantic inferencing and visual analysis for detecting events from social media activities and for finding automatically media (photos and videos) illustrating events. We will review the numerous works that try to make sense of microposts (e.g. challenges in extracting named entities and role of semantic/background knowledge enhanced techniques) in order to detect events. We will conclude this talk with research questions spanning multidisciplinary fields such as information retrieval, data mining, semantic web and web science communities.