research-article

Effects of ego networks and communities on self-disclosure in an online social network

Authors:

Reza Hadi Mogavi,

Pan HuiAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 17 - 24

https://doi.org/10.1145/3341161.3342881

Published: 15 January 2020 Publication History

Abstract

Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level.

In this paper, we conduct a quantitative study at different granularities of networks (ego networks and user communities) to understand users' self-disclosing behaviors better. As our first contribution, we characterize users into three types (open, closed, and moderate) based on the Communication Privacy Management theory and extend the analysis of the self-disclosure of users to a large-scale OSN dataset which could represent the entire network structure. As our second contribution, we show that our proposed features of ego networks and positional and structural properties of communities significantly affect self-disclosing behavior. Based on these insights, we present the possible relation between the propensity of the self-disclosure of users and the sociological theory of structural holes, i.e., users at a bridge position can leverage advantages among distinct groups. To the best of our knowledge, our study provides the first attempt to shed light on the self-disclosure of users using the whole network structure, which paves the way to a better understanding of users' self-disclosing behaviors and their relations with overall network structures.

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Cited By

Ma LZheng L(2023)Ego-networks, SNSs affordances, and personalities: understanding individuals’ selfie posting on SNSs based on Actor-Network TheoryBehaviour & Information Technology10.1080/0144929X.2023.2177824(1-16)Online publication date: 14-Feb-2023
https://doi.org/10.1080/0144929X.2023.2177824
Vaibhav SDhananjay Kumar MHosamani TPatil VNatarajan S(2023)Ego Network Analysis Using Machine Learning AlgorithmsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-99-4626-6_29(343-352)Online publication date: 11-Oct-2023
https://doi.org/10.1007/978-981-99-4626-6_29
Lin ZZhang YGong QChen YOksanen ADing A(2022)Structural Hole Theory in Social Network Analysis: A ReviewIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30703219:3(724-739)Online publication date: Jun-2022
https://doi.org/10.1109/TCSS.2021.3070321
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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

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Published: 15 January 2020

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Ma LZheng L(2023)Ego-networks, SNSs affordances, and personalities: understanding individuals’ selfie posting on SNSs based on Actor-Network TheoryBehaviour & Information Technology10.1080/0144929X.2023.2177824(1-16)Online publication date: 14-Feb-2023
https://doi.org/10.1080/0144929X.2023.2177824
Vaibhav SDhananjay Kumar MHosamani TPatil VNatarajan S(2023)Ego Network Analysis Using Machine Learning AlgorithmsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-99-4626-6_29(343-352)Online publication date: 11-Oct-2023
https://doi.org/10.1007/978-981-99-4626-6_29
Lin ZZhang YGong QChen YOksanen ADing A(2022)Structural Hole Theory in Social Network Analysis: A ReviewIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.30703219:3(724-739)Online publication date: Jun-2022
https://doi.org/10.1109/TCSS.2021.3070321
Zhang JZhu JMeng TZhuang CChen Y(2022)Predicting Upvotes and Downvotes in Location-Based Social Networks Using Machine LearningBig Data and Social Computing10.1007/978-981-19-7532-5_12(180-196)Online publication date: 7-Dec-2022
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Gao MChen YGong QWang XHui P(2022)Understanding Scholar Social Networks: Taking SCHOLAT as an ExampleComputer Supported Cooperative Work and Social Computing10.1007/978-981-19-4549-6_25(326-339)Online publication date: 22-Jul-2022
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Li THsu WLee MChieu H(2020)Probabilistic Decision Modeling in Social Networks2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00079(466-473)Online publication date: Nov-2020
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Haq EBraud TKwon YHui PAlhajj R(2020)Enemy at the gateProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381296(212-216)Online publication date: 7-Dec-2020
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Haq EBraud TKwon YHui P(2020)A Survey on Computational PoliticsIEEE Access10.1109/ACCESS.2020.30349838(197379-197406)Online publication date: 2020
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