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Prediction of Community Behavior in News Social Media using Deep Learning
- 1. Introduction Related Work Conceptual Approach Timeplan References Prediction of Community Behavior in News Social Media using Deep Learning Svetlana Pavlitskaya 308415 12.05.2016 Supervisors: Prof. Dr. Matthias Jarke Chair of Information Systems RWTH Aachen University Prof. Gerhard Lakemeyer, Ph.D. Knowledge-Based Systems Group RWTH Aachen University Advisor: Zinayida Petrushyna, Ph.D. Chair of Information Systems and Database Technologies RWTH Aachen University Licensed under CC BY-SA 2.5 1
- 2. Introduction Related Work Conceptual Approach Timeplan References Introduction Problem: conflicts during discussions of controversial political issues Solution: track news discussions to foresee and avoid aggressive behavior and attacks through adequate moderation Goal: learn deep neural network to predict reaction on news 1 user communities in news discussions in social media 2 community transformation over time 3 discussed topics 4 attitude towards topics within community Licensed under CC BY-SA 2.5 2
- 3. Introduction Related Work Conceptual Approach Timeplan References Community Detection and Evolution Deep Learning Community Detection and Evolution Community detection using propinquity dynamics [ZWWZ09] compute propinquity for each possible edge iteratively update current graph topology to make it more consistent with propinquity perform depth-first search over the resulting graph to find communities Community evolution: an event-based framework[APU09] network states at consecutive timestamps (snapshots) critical events for communities and for nodes Licensed under CC BY-SA 2.5 3
- 4. Introduction Related Work Conceptual Approach Timeplan References Community Detection and Evolution Deep Learning Deep Learning Single-layer perceptron Multilayer perceptron Recurrent Neural Network Licensed under CC BY-SA 2.5 4
- 5. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Proposed System Architecture Licensed under CC BY-SA 2.5 5
- 6. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Experimental Data Collection all news in 2015 with keyword ”refugee(s)” from Facebook pages of CNN, BBCWorldNews and Euronews average number of comments for one news post: > 1K => expected number of comments for all news from CNN, BBCWorldNews and Euronews in 2015: > 23M! strategy: limit the number of news posts using keywords, retrieve only limited number of comments per post Licensed under CC BY-SA 2.5 6
- 7. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Initial Dataset dataset range - one year snapshot interval - one day threshold for a number of news per day and comments per news post Licensed under CC BY-SA 2.5 7
- 8. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Dataset Extension using Community Detection and Evolution Library communities for each snapshot critical events involving users in communities which community discussed which news Licensed under CC BY-SA 2.5 8
- 9. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Dataset Extension using AlchemyAPI for each comment: sentiment score between -1 and +1 for each news: relevancy-ranked list of concepts for each community and news: common sentiment or distribution of sentiments Licensed under CC BY-SA 2.5 9
- 10. Introduction Related Work Conceptual Approach Timeplan References Architecture Community Behavior Prediction Model Recurrent Neural Network given a sequence of feature vectors describing community behavior during several snapshots, predict sentiment score at the next snapshot cross-validation (variant for time series using sliding window) Licensed under CC BY-SA 2.5 10
- 11. Introduction Related Work Conceptual Approach Timeplan References Timeplan Licensed under CC BY-SA 2.5 11
- 12. Introduction Related Work Conceptual Approach Timeplan References References Sitaram Asur, Srinivasan Parthasarathy, and Duygu Ucar. An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Transactions on Knowledge Discovery from Data (TKDD), 3(4):16, 2009. Yuzhou Zhang, Jianyong Wang, Yi Wang, and Lizhu Zhou. Parallel community detection on large networks with propinquity dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 997–1006. ACM, 2009. Licensed under CC BY-SA 2.5 12
- 13. Introduction Related Work Conceptual Approach Timeplan References Thank you! Licensed under CC BY-SA 2.5 13