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Conference Paper: A Deep Learning Approach to Modeling Temporal Social Networks on Reddit

TitleA Deep Learning Approach to Modeling Temporal Social Networks on Reddit
Authors
Issue Date2019
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810
Citation
Proceedings of 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China, 1-3 July 2019 , p. 68-73 How to Cite?
AbstractAs terrorists are losing against counter-terrorism efforts, they turn to manipulating cryptocurrency prices through online social communities to gain illicit profit to fund their operations. Modeling temporal online social networks (OSNs) of these communities can possibly help to provide useful intelligence about these malicious activities. However, existing techniques do not learn sufficiently from diverse features to enable prediction and simulation of online social behavior. Research on simulating temporal OSN behavior is not widely available. This research developed and validated a deep learning approach, named Temporal Network Model (TNM), to modeling the complex features and dynamic behavior exhibited in the temporal OSNs of online communities. Using extensive features extracted from fine-grained data, TNM consists of weighted time series models, user and link prediction models, and temporal dependency model that predict respectively the macroscopic behavior, microscopic user participation and events, and time stamps of the events. Evaluation was done in comparison with a benchmark approach to examine TNM's performance on predicting and simulating behavior of 42,627 users in 440,906 events on the Reddit cryptocurrency community during July-August of 2017. Results show that TNM outperformed the benchmark in 5 out of 8 simulation metrics. TNM achieved consistently better performance in user activity prediction, and performed generally better in structural (network-level) prediction. The research provides new findings on simulating temporal OSNs and new predictive analytics for understanding online social behavior.
Persistent Identifierhttp://hdl.handle.net/10722/278668
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChung, WY-
dc.contributor.authorToraman, C-
dc.contributor.authorHuang, Y-
dc.contributor.authorVora, M-
dc.contributor.authorLiu, J-
dc.date.accessioned2019-10-21T02:11:49Z-
dc.date.available2019-10-21T02:11:49Z-
dc.date.issued2019-
dc.identifier.citationProceedings of 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China, 1-3 July 2019 , p. 68-73-
dc.identifier.isbn978-1-7281-2505-3-
dc.identifier.urihttp://hdl.handle.net/10722/278668-
dc.description.abstractAs terrorists are losing against counter-terrorism efforts, they turn to manipulating cryptocurrency prices through online social communities to gain illicit profit to fund their operations. Modeling temporal online social networks (OSNs) of these communities can possibly help to provide useful intelligence about these malicious activities. However, existing techniques do not learn sufficiently from diverse features to enable prediction and simulation of online social behavior. Research on simulating temporal OSN behavior is not widely available. This research developed and validated a deep learning approach, named Temporal Network Model (TNM), to modeling the complex features and dynamic behavior exhibited in the temporal OSNs of online communities. Using extensive features extracted from fine-grained data, TNM consists of weighted time series models, user and link prediction models, and temporal dependency model that predict respectively the macroscopic behavior, microscopic user participation and events, and time stamps of the events. Evaluation was done in comparison with a benchmark approach to examine TNM's performance on predicting and simulating behavior of 42,627 users in 440,906 events on the Reddit cryptocurrency community during July-August of 2017. Results show that TNM outperformed the benchmark in 5 out of 8 simulation metrics. TNM achieved consistently better performance in user activity prediction, and performed generally better in structural (network-level) prediction. The research provides new findings on simulating temporal OSNs and new predictive analytics for understanding online social behavior.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810-
dc.relation.ispartofIEEE International Conference on Intelligence and Security Informatics (ISI)-
dc.rightsIEEE International Conference on Intelligence and Security Informatics (ISI). Copyright © IEEE.-
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleA Deep Learning Approach to Modeling Temporal Social Networks on Reddit-
dc.typeConference_Paper-
dc.identifier.emailChung, WY: wchun@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISI.2019.8823399-
dc.identifier.scopuseid_2-s2.0-85072973843-
dc.identifier.hkuros307648-
dc.identifier.spage68-
dc.identifier.epage73-
dc.publisher.placeUnited States-

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