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Presentation, case study_event detection in twitter

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Presentation, case study_event detection in twitter

  1. 1. Social Media Mining Case Study : Event Detection in Twitter Based on 'Event Detection in Twitter' by Jianshu Weng, Bu-Sung Lee ICWSM 2011 Presented by : Yue HE & Falitokiniaina RABEARISON
  2. 2. Motivation Case Study : Event Detection in Twitter - 01
  3. 3. Outline Related work Presentation of EDCoW Experiments and Results Conclusion Discussion Case Study : Event Detection in Twitter - 03
  4. 4. Related Work Case Study : Event Detection in Twitter - 05
  5. 5. Related Work Term-weighting-based approaches Topic-modeling-based approaches Clustering-based approaches Case Study : Event Detection in Twitter - 06
  6. 6. PRESENTATION of EDCoWCase Study : Event Detection in Twitter - 07
  7. 7. EDCoW [ Workflow] Case Study : Event Detection in Twitter - 08
  8. 8. Case Study : Event Detection in Twitter - 09 EDCoW [Components]
  9. 9. EXPERIMENTS and RESULTS Case Study : Event Detection in Twitter - 10
  10. 10. Dataset Description and Preparation Case Study : Event Detection in Twitter - 11
  11. 11. Signal Construction Case Study : Event Detection in Twitter - 12
  12. 12. “santa + wine” Case Study : Event Detection in Twitter - 13
  13. 13. Signal Construction Q: How can you group the sets of words? A: Similarity between words Case Study : Event Detection in Twitter - 14
  14. 14. Auto Correlation Computation Filter: Median Absolute Deviation(MAD) : 2400 words 32 keywords Case Study : Event Detection in Twitter - 15
  15. 15. Cross Correlation Computation Case Study : Event Detection in Twitter - 16
  16. 16. Cross Correlation Computation 22 nodes 19 edges Case Study : Event Detection in Twitter - 17 32 nodes 465 edges (nodes=signals/keywords)
  17. 17. Reduction Case Study : Event Detection in Twitter - 18
  18. 18. Modularity-based Graph Partition Network contains 22 nodes and 19 edges. After using Newman Alg, we find 6 clusters(event). The events’ significance is presented by weights of the edges between keywords and the lengths of the event. Case Study : Event Detection in Twitter - 19
  19. 19. Event Case Study : Event Detection in Twitter - 20
  20. 20. Detect Event Case Study : Event Detection in Twitter - 21
  21. 21. Detect Event Case Study : Event Detection in Twitter - 22
  22. 22. CONCLUSION Case Study : Event Detection in Twitter - 23
  23. 23. Conclusion ● After studying and implementing the Event Detection with Clustering of Wavelet-based Signals Algorithm in Java, we find it works in different time period. We also try to use different parameters to evaluate it on the twitter dataset. ● Some places to improve: ● How to control the parameters? ● How to let the keywords(events) make sense? ● It need some background to translate the combined keywords into event. ● How to utilize the results from public opinion? Case Study : Event Detection in Twitter - 24
  24. 24. Integrate EDCoW into SONDY Case Study : Event Detection in Twitter - 25
  25. 25. EDCoW [Javadoc] Case Study : Event Detection in Twitter - 26
  26. 26. Case Study : Event Detection in Twitter - 27
  27. 27. Acknowledge Thank Jairo Cugliari & Adrien Guille for your guides, comments and discussions! Case Study : Event Detection in Twitter - 28
  28. 28. DISCUSSION Thanks for your attention! ;) Case Study : Event Detection in Twitter - 29

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