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Mention-anomaly-based Event Detection and Tracking in Twitter Adrien Guille & Cécile Favre ERIC Lab, University of Lyon 2, France IEEE/ACM ASONAM 2014, Beijing, China August 20, 2014
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What is Twitter & why study it? Twitter: micro-blogging service 140-character messages Ever growing number of Twitter users Pro: Timely source of information Con: Information overload How can we use Twitter for automated event detection and tracking? August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 2
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Related Work Idea: spot bursty patterns Term-weighting-based approaches Peaky Topics [Shamma11], Trending Score [Benhardus13] Possible ambiguity, lack of context Topic-modeling-based approaches On-line LDA [Lau12], ET-LDA [Yuheng12] Lack of scalability Clustering-based approaches EDCoW [Weng11], TwEvent [Li12], ET [Parikh13] Noisy event descriptions August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 3
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Issues & Proposal August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 4 Shortcomings of existing methods Event duration is a fixed parameter Only the textual content of tweets is considered We propose a novel approach and method that Dynamically estimate each event duration Exploit the social aspect of tweet streams through mentions
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Proposed Method August 20, 2014 5 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter
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Problem Formulation Input Corpus C containing N tweets partitioned into n time-slices Vocabularies V and V @ Output The k most impactful events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 6 Event: A bursty topic and a value Mag translating its magnitude of impact Bursty Topic: A time interval I, a main term t, a set S of weighted related terms
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Overview of the proposed method August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 7 Two-phase flow 1: Analyse the mention frequency of each word in V @ to detect events (Mag,I,t, Ø ) 2: Select related words and generating the final list of the k most impactful events while controling redundancy MABED, Mention-Anomaly-Based Event Detection
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PHASE 1 Proposed Method August 20, 2014 8 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter
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Detecting Events with Mention Anomaly August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 9 Computing the anomaly at a point i for word t Requires computing the expected volume of tweets containing at least one mention and t, at i Normal distribution: Expectation: Anomaly: Measuring the magnitude of impact Integrating anomaly:
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Detecting Events with Mention Anomaly August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 10 For each word t in V @ Solve a « Maximum Contiguous Subsequence Sum » type of problem: Eventually, each event is described by A main word t A period of time I The magnitude of its impact Mag
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Detecting Events with Mention Anomaly August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 11 Example
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PHASE 2 Proposed Method August 20, 2014 12 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter
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Selecting Words Describing Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 13 Identifying candidate words Set of p words that co-occur the most with t during I Selecting the most relevant words Measure the similarity between candidate words and the main word frequency [Erdem12] Apply a threshold θ
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Selecting Words Describing Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 14 Example
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Generating the List of Top k Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 15 Event graph & redundancy graph Detecting duplicated events Connectivity of main terms in the event graph Overlap between intervals, threshold σ Merging duplicated events Identifying connected components in the redundancy graph
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Generating the List of Top k Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 16 Example
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Evaluation August 20, 2014 17 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter
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Experimental Setup August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 18 Corpora C(en): 1,437,126 tweets published in November 2009 C(fr): 2,086,136 tweets published in March 2012 Baselines for comparison Trending Score (TS) [Benhardus13] and ET [Parikh13] α -MABED Parameter setting ( α -)MABED: 30-min time-slices, p=10, θ =0.7, σ =0.5 Trending Score, ET: 1-day time-slices
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Evaluation Metrics August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 19 Manual annotation Two human annotators judging the significancy of the top 40 events detected by each method ( κ = 0.72) Precision Significant events / All detected events Recall Distinct significant events / All detected events DERate [Li12] Duplicated events / Significant events
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Quantitative Evaluation August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 20 Performance of the five methods on the two corpora
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Quantitative Evaluation August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 21 Impact of σ on MABED
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Qualitative Evaluation August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 22 Improved readability Excerpt of the list of events detected in C(en) by MABED
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Qualitative Evaluation August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 23 Improved temporal precision & reduced redundancy Importance of dynamically estimating events duration Politics-related events tend to be discussed longer [Romero11]
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Included in the open-source social media data mining tool SONDY [Guille13] http://mediamining.univ-lyon2.fr/people/guille/mabed.php Implementation August 20, 2014 24 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter
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Time-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 25
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Impact-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 26
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Topic-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 27
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Conclusion & Future Work August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 28 Propose a novel approach and method for detecting events in Twitter Verified hypothesis Considering mentions helps detecting significant events Experimental results on two different datasets demonstrate the accuracy and the robustness of the proposed method Future work More features to model discussions between users
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References August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 29 [Shamma11] D. A. Shamma, L. Kennedy, and E. F. Churchill, “Peaks and persistence: modeling the shape of microblog conversations,” in CSCW, 2011 [Benhardus13] J. Benhardus and J. Kalita, “Streaming trend detection in twitter,” IJWBC, vol. 9, no. 1, 2013 [Lau12] J. H. Lau, N. Collier, and T. Baldwin, “On-line trend analysis with topic models: #twitter trends detection topic model online,” in COLING, 2012 [Yuheng12] H.Yuheng, J.Ajita, D.S.Dorée, and W.Fei, “What were the tweets about? topical associations between public events and twitter feeds,” in ICWSM, 2012 [Weng11] J. Weng and B.-S. Lee, “Event detection in twitter,” in ICWSM, 2011 [Li12] C. Li, A. Sun, and A. Datta, “Twevent: Segment-based event detection from tweets,” in CIKM, 2012 [Parikh13] R. Parikh and K. Karlapalem, “Et: events from tweets,” in companion WWW, 2013 [Erdem12] O. Erdem, E. Ceyhan, and Y. Varli, “A new correlation coefficient for bivariate time- series data,” in MAF, 2012 [Guille13] A. Guille, C. Favre, H. Hacid, and D. Zighed, “Sondy: An open source platform for social dynamics mining and analysis,” in SIGMOD, 2013
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