Social networks now connect billions of people around the world, where individuals occupying different positions often represent different social roles and show different characteristics in their behaviors. The structural hole (SH) theory demonstrates that users occupying the bridging positions between different communities have advantages since they control the key information diffusion paths. Users of this type, known as SH spanners, are important when it comes to assimilating social network structures and user behaviors. In this article, we review the use of SHs theory in social network analysis, where SH spanners take advantage of both information and control benefits. We investigate the existing algorithms of SH spanner detection and classify them into information flow-based algorithms and network centrality-based algorithms. For practitioners, we further illustrate the applications of SH theory in various practical scenarios, including enterprise settings, information diffusion in social networks, software development, mobile applications, and machine learning (ML)-based social prediction. Our review provides a comprehensive discussion on the foundation, detection, and practical applications of SHs. The insights can facilitate researchers and service providers to better apply the theory and derive value-added tools with advanced ML techniques. To inspire follow-up research, we identify potential research trends in this area, especially on the dynamics of networks.

Structural Hole Theory in Social Network Analysis: A Review

Computational Politics is the study of computational methods to analyze and moderate users’ behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users’ participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

/pdf/a-survey-on-computational-politics-2tof2h43ob.pdf

A Survey on Computational Politics

Social networks are effective platforms to study the real-life behavior of users In this paper, we study users' political polarization during the times of crisis and its relation to nationalism To this purpose, we focus on the reaction of Indian and Pakistani Twitter users during February 2019 crisis and the ensuing Indian General Elections in 2019 We show that a national crisis affects the polarization and discourse in both countries Also, we show that user activities increase during a national crisis, and political discourse strengthens while polarization decreases on critical days Finally, we highlight the links between this crisis and the Indian elections and show how the political parties discussed the crisis in their campaigns

Enemy at the gate: evolution of Twitter user's polarization during national crisis

Social networks now connect billions of people around the world, where individuals occupying different positions often represent different social roles and show different characteristics in their behaviors. The structural hole (SH) theory demonstrates that users occupying the bridging positions between different communities have advantages since they control the key information diffusion paths. Users of this type, known as SH spanners, are important when it comes to assimilating social network structures and user behaviors. In this article, we review the use of SHs theory in social network analysis, where SH spanners take advantage of both information and control benefits. We investigate the existing algorithms of SH spanner detection and classify them into information flow-based algorithms and network centrality-based algorithms. For practitioners, we further illustrate the applications of SH theory in various practical scenarios, including enterprise settings, information diffusion in social networks, software development, mobile applications, and machine learning (ML)-based social prediction. Our review provides a comprehensive discussion on the foundation, detection, and practical applications of SHs. The insights can facilitate researchers and service providers to better apply the theory and derive value-added tools with advanced ML techniques. To inspire follow-up research, we identify potential research trends in this area, especially on the dynamics of networks. 

Online social networks play an important role in our daily activities. As an important concept in social network analytics, the structural hole theory shows that the positions in social networks that can bridge different user groups will get benefits. Existing solutions for identifying structural hole spanners normally require the knowledge of the entire social graph. In this paper, we propose a novel solution to uncover structural hole spanners according to the users' profiles and user-generated contents (UGCs), instead of referring to the entire social graph. We propose a machine learning-based model to implement the identification. We further leverage the ego networks and the cross-site linking function to enhance the identification. A real-world dataset collected from Foursquare and Twitter is used to evaluate the identification performance of our model. The results show that our model can achieve a high F1-score of 0.857.

Identifying Structural Hole Spanners in Online Social Networks Using Machine Learning

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.

/pdf/effects-of-ego-networks-and-communities-on-self-disclosure-1d4o7jmck2.pdf

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

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