@inbook{a1c36c8549c140e892fd2f298fda642c,
title = "Zombies Walk Among Us: Cross-platform data mining for event monitoring",
abstract = "Data mining across multiple social websitescan reveal valuable factual information for both monitoring andreconstructing events. Crowdsourcing can be used on these sitesto monitor {\textquoteleft}flash mob{\textquoteright} group behaviours, loosely or formallyplanned activities, such as {\textquoteleft}zombie walks{\textquoteright}. In certain contexts,these walks are {\textquoteleft}marketed{\textquoteright} to social site users in order topromote charitable or social engagements; in others, analysissuggests that participation is a form of political engagement. Weuse multi-platform information extraction to build an atlas ofgeographic and demographic events, which leads us to compelling,yet imperfect, understanding of why events are successful andregionally relevant factors that encourage people to pick upbehaviours or participate in movements. More generally, this casestudy confirms to us that that {\textquoteright}big-data{\textquoteright}-led analysis of this kind isreasonably straightforward and rewarding, as well as providing auseful basis for Bayesian reasoning: the use of available evidenceto evaluate propositions about this type of event.",
author = "Tonkin, {Emma L} and Heather Pfeiffer",
year = "2013",
language = "English",
booktitle = "Proceedings of ICDM EEML 2013",
publisher = "IEEE Computer Society",
address = "United States",
note = "International Workshop on Experimental Economics and Machine Learning , in conjunction with ICDM 2013 ; Conference date: 08-12-2015",
}