Zombies Walk Among Us: Cross-Platform Data Mining for Event Monitoring

Abstract

Social networks such as Facebook, Orkut and so on, are repositories of information that are viewed as users' opinions. However, data mining across multiple social websites can reveal valuable factual information for both monitoring and reconstructing events. Crowdsourcing can be used on these sites to monitor 'flash mob' group behaviors, loosely or formally planned activities, such as 'zombie walks'. In certain contexts, these walks are 'marketed' to social site users in order to promote charitable or social engagements; in others, analysis suggests that participation is a form of political engagement. We use multi-platform information extraction to build an atlas of geographic and demographic events, which leads us to compelling, yet imperfect, understanding of why events are successful and regionally relevant factors that encourage people to pick up behaviors or participate in movements. More generally, this case study confirms that 'big-data'-led analysis of this kind is reasonably straightforward and rewarding, as well as providing a useful basis for Bayesian reasoning: the use of available evidence to evaluate propositions about this type of events.

"Big Data" Analytics: Available Approaches, Problems and Solutions

“…The period between the data acquisition and availability may be shorter than that needed for the implementation of operations, when establishing DW. For example, the monitoring carried out to detect the information quickly spread on social networks and to identify its sources, to define active users, and to detect negative expressions or the facts of confidential information leakage [54].…”