Analysis of Social Media Streams

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1 Fakultätsname 24 Fachrichtung 24 Institutsname 24, Professur 24 Analysis of Social Media Streams Florian Weidner Dresden,

2 Outline 1.Introduction 2.Social Media Streams Clustering Summarization 3.Topics Detection Tracking 4.Conclusion TU Dresden, Analyse von Social Media und sozialen Netzen; Florian Weidner Folie 2 von 24

3 1. Introduction A lot of data hidden and obvious information Important for users, organization, Algorithms for static data well researched However: Processing of streams is still in it s early stages [1] State of the art overview TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 3 von 24

4 2. Social Media Streams High frequency Continious Different kind of data Text, links, pictures, meta-data Human language is a problem! TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 4 von 24

5 2.1 Social Media Streams - Clustering Find groups of similar instances without prior knowledge! Curse of dimensionality outliers #bigdata A C F #catfact D #clustering B E TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 5 von 24

6 2.1.1 Social Media Streams Clustering Cluster Droplets, Similarity & Fading Functions Cluster Droplet (CD): statistical information (recency, #tweets, weights, ) Similarity function: cosine similarity, dice coefficient, Fading Function: decay of cluster TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 6 von 24

7 2.1.2 Social Media Streams Clustering Variable Feature Sets Feature Set Validity Index (VI) Clustering Threshold (CT) Reselection Threshold (RT) TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 7 von 24

8 2.1.2 Social Media Streams Clustering Variable Feature Sets 1. Get Text 2. Insert into cluster 3. Calculate VI 4. Compare with CT & RT TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 8 von 24

9 2.2 Social Media Streams - Summarization Input stream is huge Summarize based on intervals Cluster can still contain a huge amount of data Summarize clusters Single sentence vs. Multiple sentence New text vs. Text from stream Noise TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 9 von 24

10 2.2.1 Social Media Streams Summarization Word-Variance Based Approach Phrase Reinforcement Algorithm builds a tree Output: Set of sentences which summarize stream! TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 10 von 24

11 2.2.1 Social Media Streams Summarization Word-Variance Based Approach 1. A tragedy: Ted Kennedy died today of cancer 2. Ted Kennedy died today 3. Ted Kennedy was a leader 4. Ted Kennedy died at Age 77 TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 11 von 24

12 2.2.2 Social Media Streams Summarization Distance Metrics -Cluster-Vector (timestamp, meta) Goal: extract k s which cover as much content as possible Distance of to cluster centroid Size of cluster Centrality Scores TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 12 von 24

13 3. Topics Abstract topic vs. real-life topic (event) Small-scale vs. large-scaled short duration and less info vs. long lasting and a lot of data Semantic features important! For events, the location is important! Semantic features and weblinks TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 13 von 24

14 3.1 Topics - Detection Topic augmentation external topic as input Topic detection w/o prior knowledge Clustering is important/simplifies the topic detection TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 14 von 24

15 3.1.1 Topics Detection Word-Variance Topics are time-dependent! Simple solution: increase of certain words (i.e. earthquake ) Count words in intervals and compare! TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 15 von 24

16 3.1.1 Topics Detection Word-Variance 1. Preprocessing 2. Calculate word frequencies of incoming data for each time window 3. If there is a significant increase (threshold), keep word 4. Calculate correlations for all remaining words and cluster them TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 16 von 24

17 3.1.2 Topics Detection Location Filter and cluster incoming data according to their location (just longitude/latitude) Weight s and clusters with help of features (textual, other) If weight > threshold Topic TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 17 von 24

18 3.1.3 Topics Detection Authority Score & Influence Key users + selected users Key words + selected words Repository Authority Score: Importance of the authors of the tweets in the cluster Topical Influence How many important keywords are in the cluster? TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 18 von 24

19 3.1.3 Topics Detection Authority Score & Influence 1. Cluster incoming data frequently with similarity function 2. Calculate Topical User Authority Score & Topical Influence of each cluster 3. Weight words and rank them emerging topic 4. Machine Learner (6 features) hot emerging topic TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 19 von 24

20 3.3 Topics and Events - Tracking Track topic during a period of time display (only) related content Track spatial development evaluate geotags and keywords TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 20 von 24

21 3.3.1 Topics and Events Tracking Tracking of an interesting topic Background Corpus Content Model Quality Features Semantic Features Query for topic Foreground Corpus Feedback Model Compare with x previous and best descriptive s Display??? TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 21 von 24

22 4. Conclusion Many different solutions: Cluster Droplets, Fading & Similarity Functions Variable Feature Sets Word-Variance Distance Scores (Authority, Influence) Content & Feedback Model No holistic solution Filtered stream Utilization of data sources just single purpose solutions Many restrictions! Few open source framework (lot of conceptual work) TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 22 von 24

23 Vielen Dank für die Aufmerksamkeit! TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 23 von 24

24 5. References [1] Gong L. - Text Clustering algorithm based on adaptive feature selection, Expert Systems with Applications, 2011 [2] Aggarwal C. - On clustering massive text and categorical data streams, Knowledge and Information Systems, 2009 [3] Sharifi B. - Summarizing Microblogs Automatically, HLT '10, 2010 [4] Chakrabati D. Event Summarization Using s, AAAI '11, 2011 [5] Shou L. - Sumblr: continuous summarization of evolving tweet streams, ACM SIGIR '13, 2013 [6] Olariu A. - Hierarchical clustering in improving microblog stream summarization, Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing, 2013 [7] Chen Y. - Emerging topic detection for organizations from microblogs, ACM SIGIR '13, 2013 [8] Hong Y. - Exploiting topic tracking in real-time tweet streams, UnstructuredNLP '13, 2013 [9] Hong L. - Discovering geographical topics in the twitter stream, WWW 12, 2012 TU Dresden, Analysis of Social Media Streams; Florian Weidner Folie 24 von 24

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