1. Presenter: Liu, Ya Tian, Yujia Pham, Anh TwitterMonitor: Trend Detection over the Twitter Stream EvenTweet: Online Localized Event Detection from Twitter

2. Michael Mathioudakis, Nick Koudas TwitterMoniter: Trend Detection over the Twitter Stream

3. INTRODUCTION TwitterMonitor, a system that performs trend detection over the Twitter stream. Identifies emerging topics on Twitter in real time and provides analytics that synthesize and accurate description of each topic. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

4. TREND DETECTION AND ANALYSIS Trend detection in two steps. Analyzes trends in a third step: Identifies ‘bursty’ keywords, Groups bursty keywords into trends, Extracts additional information to discover interesting aspects of it. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

5. Detecting Bursty Keywords Keyword: An unusually high rate in the stream. New topic emerged and seeks to explore in the further. Algorithm: QueueBurst 1) One-pass. 2) Real-time. 3) Adjustable against ‘spurious’ bursts. 4) Adjustable against spam. 5) theoretically sound. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

6. From Bursty Keywords to Trends Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

7. Trend Analysis Compose a more accurate description: Identify more keywords associated with it. Context extraction algorithms (PCA, SVD, etc.) search the recent history and reports the most correlated keywords. Grapevine’s entity extractor to identify the entities. Frequently cited sources are added to the trend description. Identifies frequent geographical origins. A chart will be produced for each trend and gets updated. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

8. Architecture Index Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 1156, 2010

9. Architecture: Back-End The StreamListener module receives sample which consists 10M out of 50M tweets per day, via the Twitter API. Then seperates tweet information into fields and exports two feeds: Reporting tweets with all their fields to an Index module Reporting only the text and timestamp of tweets to Bursty Keywords Detection module Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

10. Architecture: Back-End(Cont.) After bursty keywords are identified and grouped into trends, the Index is contacted by the Trend Analysis module to retrieve information on tweets that belong to each trend. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

11. Architecture: Front-End Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 1157, 2010

12. Architecture: Front-End (Cont.) A webpage reports recent trends in real time An interface allows users to rank trends by recency or current activity rate and submit their own short description for trends. Use an additional tab to display daily trends. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

13. Demonstration Every trend will be represented by the entities, by the related bursty keywords. The audience will have the option to use the interface in order to acquire more information. Œ They will be shown additional keywords and skim through representative tweets  They will be able to track a trend’s popularity over time and spot the origin. Ž They will interact with the system by tracking the displayed trends according different criteria and submitting descriptions. Michael Mathioudakis, Nick Koudas, Nick Koudas, TwitterMonitor: trend detection over the twitter stream., In: SIGMOD Conference, pp. 155-1158, 2010

14. Hamed Abdelhaq, Christian Sengstock, and Michael Gertz EvenTweet: Online Localized Event Detection from Twitter

15. 1. Introduction 2. Localized Event Detection Temporal Keyword Extraction Spatial Keyword Identification Keyword Clustering Cluster Scoring 3. System overview 4. Demonstration

16. INTRODUCTION EvenTweet, a system to detect localized events from a stream of tweets in real-time. Only about 1% of tweets are georeferenced. Focuses on detecting localized events from a stream of tweets in real-time. Adopts a continuous analysis of the most recent tweets within a time-based sliding window. Described by 1) related keywords & 2) estimation of the start time and the geographic location. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

17. INTRODUCTION Tracks evolution over time: a fine-grained temporal resolution. A scoring scheme the gives a score of each event over time. Don’t estimate geo-coordinates for non-geotagged tweets, but be able to identify localized events using a possibly small amont of geo-tagged tweets: Both geo- and non-geo-tagged tweets are used to identify words best describing events. Only geo-tagged tweets are used to estimate the spatial distribution of such words. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

18. 1. Introduction 2. Localized Event Detection Temporal Keyword Extraction Spatial Keyword Identification Keyword Clustering Cluster Scoring 3. System overview 4. Demonstration

19. Localized Event Detection Basic Definitions Event: a phenomenon that stimulates people to post messages for a certain period of time. Localized events: Events happen within a small region, having a small spatial extent. (e.g., concerts, soccer matches, road works) Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

20. Localized Event A localized event is described as a tuple: le = (el, et, K) el is event location, represented as a small set of connected rectangular. et is the start time. K is a set of words frequently published during the event time and at that location. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

21. Online Detection Basic Notation ： Each tweet tw = (W, uid, l, t) W: a set of words uid: a user id l = (lon, lat): a geographic location t: timestamp Use a timeline divided into a sequence of equal-length time frames (…f c-1, f c ), where f c denotes the current time frame. Each time frame represents a short time interval during which tweets are posted. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

22. Basic Notation (cont.) We use a time-based sliding window win k fc composed of k time frames and f c as its end point. The detection procedure of EvenTweet is triggered every time a new time frame elapses. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

23. 1. Introduction 2. Localized Event Detection Temporal Keyword Extraction Spatial Keyword Identification Keyword Clustering Cluster Scoring 3. System overview 4. Demonstration

24. Temporal Keyword Extraction Extraction of words showing a bursty frequency in the current time frame (these words are called keywords, Y c ) Given a set of words W c from the tweets published during the recent time frame f c, extract a subset Y c ⊆ W c which represents words likely to describe localized events. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

25. Temporal Keyword Extraction (cont.) Use discrepancy paradigm to extract keywords based on their burstiness. Assume: during timeframe f c u(w, c): normalized by the number of users publishing tweets containing word w Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

26. Temporal Keyword Extraction (cont.) In addition, hist w = (u(w, 1), u(w, 2), …, u(w, m)) is a fixed historical sequence of usage values for w collected before the current time frame f c, such that m < c. It is used when the system needs to describe the normal behavior of word w over previous time frames. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

27. Temporal Keyword Extraction (cont.) The discrepancy paradigm measures the deviation between the word usage value u(w,c) in the current time frame and an expected word usage baseline, b(w), which estimated from hist w. hist w is drawn from Gaussian distribution with mean b(w). μ and deviation b(w). σ Higher deviation, higher burstiness degree Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

28. Temporal Keyword Extraction (cont.) The burtinesss degree of a word w is the z-score defined: b_degree(w, c) :=( u(w,c)−b(w). μ )/b(w). σ Choose words whose burstiness degree is larger than two standard deviations above the mean as keywords. Keywords observed for the first time will have μ =0 and σ =0. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

29. 1. Introduction 2. Localized Event Detection Temporal Keyword Extraction Spatial Keyword Identification Keyword Clustering Cluster Scoring 3. System overview 4. Demonstration

30. Spacial Keyword Identification Find keywords which are highly localized. Only use georeferenced tweets. g grid G Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

31. Spacial Keyword Identification Only use georeferenced tweets. g -Calculate Entropy H(S i ) -Discard all keywords with entropy larger than a threshold ρ. Why? -We’ll have Y c = set of filtered keywords Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

32. Keyword Clustering Each S i is a vector. Clustering event keywords using their S i Similarity calculation: Cosine similarity Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013) Cosine Similarity, Wikipedia, http://en.wikipedia.org/wiki/Cosine_similarity

33. Keyword Clustering -There is a distance threshold Т -If a new keyword falls out of the threshold, it forms a new cluster itself. Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013) Saed Sayad, Kmeans clustering, http://www.saedsayad.com/clustering_kmeans.htm

34. Cluster Scoring To determine which clusters of keywords is more likely being referred to localized events, filter out spurious clusters. To score a cluster: 1. Score each keyword 2. Sum up all scores Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

35. Cluster Scoring 1 2 3 Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

36. 1. Introduction 2. Localized Event Detection Temporal Keyword Extraction Spatial Keyword Identification Keyword Clustering Cluster Scoring 3. System overview 4. Demonstration

37. System Overview Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)

38. Demonstration Hamed Abdelhaq, Christian Sengstock, Michael Gertz: EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6(12): pp. 1326-1329 (2013)