Prediction of Community Behavior in News Social Media using Deep Learning

IntroductionRelated Work

Conceptual ApproachTimeplan

References

Prediction of Community Behavior in News Social Mediausing Deep Learning

Svetlana Pavlitskaya308415

12.05.2016

Supervisors: Prof. Dr. Matthias JarkeChair of Information SystemsRWTH Aachen University

Prof. Gerhard Lakemeyer, Ph.D.Knowledge-Based Systems GroupRWTH Aachen University

Advisor: Zinayida Petrushyna, Ph.D.Chair of Information Systems and Database TechnologiesRWTH Aachen University

Licensed under CC BY-SA 2.5 1



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Introduction

Problem: conflicts during discussions of controversial political issues

Solution: track news discussions to foresee and avoid aggressive behavior andattacks through adequate moderation

Goal: learn deep neural network to predict reaction on news1 user communities in news discussions in social media2 community transformation over time3 discussed topics4 attitude towards topics within community




References

Community Detection and EvolutionDeep Learning

Community Detection and Evolution

Community detection using propinquity dynamics [ZWWZ09]compute propinquity for each possible edgeiteratively update current graph topology to make it more consistent with propinquityperform 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




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Community Detection and EvolutionDeep Learning

Deep Learning

Single-layer perceptron Multilayer perceptron

Recurrent Neural Network




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ArchitectureCommunity Behavior Prediction Model

Proposed System Architecture




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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 andEuronews in 2015: > 23M!

strategy: limit the number of news posts using keywords, retrieve only limitednumber of comments per post




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Initial Dataset

dataset range - one yearsnapshot interval - one daythreshold for a number of news per day and comments per news post




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Dataset Extension using Community Detection and Evolution Library

communities for each snapshotcritical events involving users in communitieswhich community discussed which news




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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 sentimentsLicensed under CC BY-SA 2.5 9



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Recurrent Neural Network

given a sequence of feature vectors describing community behavior during severalsnapshots, predict sentiment score at the next snapshot

cross-validation (variant for time series using sliding window)




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Timeplan




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References

Sitaram Asur, Srinivasan Parthasarathy, and Duygu Ucar.An event-based framework for characterizing the evolutionary behavior ofinteraction 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 Knowledgediscovery and data mining, pages 997–1006. ACM, 2009.




References

Thank you!


Science

Prediction of Community Behavior in News Social Media using Deep Learning