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Article: Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

TitleInteraction Models for Detecting Nodal Activities in Temporal Social Media Networks
Authors
KeywordsBusiness analytics
Social media analytics
Interaction models
Social network analysis
Dynamic graph modeling
Issue Date2019
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tmis.acm.org/
Citation
ACM Transactions on Management Information Systems, 2019, v. 10 n. 4, article no. 14 How to Cite?
AbstractDetecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peer-to-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assume network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013–2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models’ strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.
Persistent Identifierhttp://hdl.handle.net/10722/278765
ISSN
2015 SCImago Journal Rankings: 0.665
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChung, W-
dc.contributor.authorRao, B-
dc.contributor.authorWang, L-
dc.date.accessioned2019-10-21T02:13:39Z-
dc.date.available2019-10-21T02:13:39Z-
dc.date.issued2019-
dc.identifier.citationACM Transactions on Management Information Systems, 2019, v. 10 n. 4, article no. 14-
dc.identifier.issn2158-656X-
dc.identifier.urihttp://hdl.handle.net/10722/278765-
dc.description.abstractDetecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peer-to-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assume network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013–2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models’ strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://tmis.acm.org/-
dc.relation.ispartofACM Transactions on Management Information Systems-
dc.subjectBusiness analytics-
dc.subjectSocial media analytics-
dc.subjectInteraction models-
dc.subjectSocial network analysis-
dc.subjectDynamic graph modeling-
dc.titleInteraction Models for Detecting Nodal Activities in Temporal Social Media Networks-
dc.typeArticle-
dc.identifier.emailChung, W: wchun@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/3365537-
dc.identifier.hkuros307637-
dc.identifier.volume10-
dc.identifier.issue4-
dc.identifier.spagearticle no. 14-
dc.identifier.epagearticle no. 14-
dc.identifier.isiWOS:000504422900002-
dc.publisher.placeUnited States-

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