On-line Trend Analysis with Topic Models: #twitter Trends Detection Topic Model Online

We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.

[1]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[2]  Nello Cristianini,et al.  Tracking the flu pandemic by monitoring the social web , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[3]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Bu-Sung Lee,et al.  Event Detection in Twitter , 2011, ICWSM.

[5]  Andrew McCallum,et al.  Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.

[6]  Ee-Peng Lim,et al.  Finding Bursty Topics from Microblogs , 2012, ACL.

[7]  James Allan,et al.  Introduction to topic detection and tracking , 2002 .

[8]  M. Osborne,et al.  Bieber no more : First Story Detection using Twitter and Wikipedia , 2012 .

[9]  Miles Osborne,et al.  Streaming First Story Detection with application to Twitter , 2010, NAACL.

[10]  Timothy Baldwin,et al.  Automatic Evaluation of Topic Coherence , 2010, NAACL.

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

[12]  John A. Carroll,et al.  Applied morphological processing of English , 2001, Natural Language Engineering.

[13]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[14]  Fabio Massimo Zanzotto,et al.  Linguistic Redundancy in Twitter , 2011, EMNLP.

[15]  Daniel Barbará,et al.  On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[16]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[17]  Kirill Kireyev.  Applications of Topics Models to Analysis of Disaster-Related Twitter Data , 2009 .

[18]  Nick Koudas,et al.  TwitterMonitor: trend detection over the twitter stream , 2010, SIGMOD Conference.

[19]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Bertrand De Longueville,et al.  "OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires , 2009, LBSN '09.

[22]  Yang Song,et al.  Identifying Event-related Bursts via Social Media Activities , 2012, EMNLP.