Advances in Event Detection

Social networks like twitter reveals a lot of what is going on around in a city. Posts and tweets of people visiting events, concerts or attending civil demonstrations are on the norm of social networks. By mining such data, social networks can reveal the hotspots of events or activities demonstrating the pulse of a Smart City in real time. This paper presents the current state of the art in the field of event detection in social networks. After covering the basics of event detection, we present several selected publications covering diverse approaches, pointing out their advantages and limitations. We further identify open research gaps presenting our current efforts towards a more robust and complete event detection technique.

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[2]  Shaowen Wang,et al.  Mapping the global Twitter heartbeat: The geography of Twitter , 2013, First Monday.

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[12]  Daniel B. Neill,et al.  Non-Parametric Scan Statistics for Disease Outbreak Detection on Twitter , 2014, Online Journal of Public Health Informatics.

[13]  Michael Gertz,et al.  EvenTweet: Online Localized Event Detection from Twitter , 2013, Proc. VLDB Endow..

[14]  Wael Khreich,et al.  A Survey of Techniques for Event Detection in Twitter , 2015, Comput. Intell..

[15]  Arthur Zimek,et al.  Outlier Detection and Trend Detection: Two Sides of the Same Coin , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[16]  Mauricio Quezada,et al.  Location-Aware Model for News Events in Social Media , 2015, SIGIR.

[17]  Nikou Günnemann,et al.  Finding non-redundant multi-word events on Twitter , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[18]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[19]  Oren Etzioni,et al.  Open domain event extraction from twitter , 2012, KDD.

[20]  Hans-Peter Kriegel,et al.  SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds , 2014, KDD.

[21]  Daniel B. Neill,et al.  Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs , 2014, KDD.

[22]  Joemon M. Jose,et al.  Building a large-scale corpus for evaluating event detection on twitter , 2013, CIKM.