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Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

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Liqiang WangAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 10, Issue 4

Article No.: 14, Pages 1 - 30

https://doi.org/10.1145/3365537

Published: 18 December 2019 Publication History

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Abstract

Detecting 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.

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https://dl.acm.org/doi/10.1016/j.im.2023.103773
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Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 10, Issue 4

December 2019

98 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3374918

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Copyright © 2019 ACM.

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Publication History

Published: 18 December 2019

Accepted: 01 September 2019

Revised: 01 August 2019

Received: 01 March 2018

Published in TMIS Volume 10, Issue 4

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Cited By

Chung WLai V(2023)A temporal graph framework for intelligence extraction in social media networksInformation and Management10.1016/j.im.2023.10377360:4Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.im.2023.103773
Rao BLiu ZZhang HLu SWang L(2021)SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis2021 IEEE 14th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD53861.2021.00058(433-444)Online publication date: Sep-2021
https://doi.org/10.1109/CLOUD53861.2021.00058
Xing YHe WCao GLi Y(2021)Using data mining to track the information spreading on social media about the COVID-19 outbreakThe Electronic Library10.1108/EL-04-2021-0086ahead-of-print:ahead-of-printOnline publication date: 25-Nov-2021
https://doi.org/10.1108/EL-04-2021-0086
Chung WMustaine EZeng D(2020)A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSSEnterprise Information Systems10.1080/17517575.2020.1827299(1-23)Online publication date: 27-Oct-2020
https://doi.org/10.1080/17517575.2020.1827299

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