Social Sensors Early Detection of Contagious Outbreaks in Social Media

Cascades of information in social media (like Twitter, Facebook, Reddit, etc.) have become well-established precursors to important societal events such as epidemic outbreaks, flux in stock patterns, political revolutions, and civil unrest activity. Early detection of such events is important so that the contagion can either be leveraged for applications like viral marketing and spread of ideas [4] or can be contained so as to quell negative campaigns [2] and minimize the spread of rumors. In this work, we algorithmically design social sensors, a small subset of the entire network, who can effectively foretell cascading behavior and thus detect contagious outbreaks. While several techniques (for example, the friendship paradox [3]) to design sensors exist, most of them exploit the social network topology and do not effectively capture the bursty dynamics of a social network like Twitter, since they ignore two key observations (1) Several viral phenomenal have already cascaded in the network (2) most contagious outbreaks are a combination of network flow and external influence.