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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,

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Presentation on theme: "Mention-anomaly-based Event Detection and Tracking in Twitter Adrien Guille & Cécile Favre ERIC Lab, University of Lyon 2, France IEEE/ACM ASONAM 2014,"— Presentation transcript:

1 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

2 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

3 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

4 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

5 Proposed Method August 20, A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter

6 Problem Formulation  Input  Corpus C containing N tweets partitioned into n time-slices  Vocabularies V and  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

7 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 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

8 PHASE 1 Proposed Method August 20, A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter

9 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:

10 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  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

11 Detecting Events with Mention Anomaly August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 11  Example

12 PHASE 2 Proposed Method August 20, A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter

13 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

14 Selecting Words Describing Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 14  Example

15 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

16 Generating the List of Top k Events August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 16  Example

17 Evaluation August 20, A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter

18 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

19 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

20 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

21 Quantitative Evaluation August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 21  Impact of σ on MABED

22 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

23 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]

24 Included in the open-source social media data mining tool SONDY [Guille13] Implementation August 20, A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter

25 Time-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 25

26 Impact-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 26

27 Topic-oriented Interface August 20, 2014 A. Guille & C. Favre: Mention-Anomaly-Based Event Detection in Twitter 27

28 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

29 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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