Social network user influence sense-making and dynamics prediction

Li, Jingxuan; Peng, Wei; Li, Tao; Sun, Tanfeng; Li, Qianmu; Xu, Jian

doi:10.1016/j.eswa.2014.02.038

Cited by 73 publications

(45 citation statements)

References 17 publications

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“…Compared with prior studies in the literature that focused on network structure (e.g., Luo, Du, Liu, et al, 2015), social media user influence (e.g., Li, Peng, Li, et al, 2014), and friend recommendations (e.g., Chen, Zeng, & Yuan, 2013), this study emphasized: (1) the behavior and strategies involved in online petition diffusion, such as population targeting, petition sharing, and sending reminders; and (2) the aggregated perspective of the social network system. This study also incorporated statistical analysis to provide quantifiable evidence supporting the strategic recommendations, i.e., targeting the right population and increasing sharing rate through incentives.…”

Section: Resultsmentioning

confidence: 99%

Information diffusion through social networks: The case of an online petition

Jalali

Ashouri

Herrera-Restrepo

et al. 2016

Expert Systems with Applications

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People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers.Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have 'disproportionate' effects in increasing the total number of signatures. The model is fully documented for further development and replications.

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Section: Resultsmentioning

confidence: 99%

Information diffusion through social networks: The case of an online petition

Jalali

Ashouri

Herrera-Restrepo

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…They have validated the results from their model with a dataset from the retweeting that occurred directly after the Boston bombings and the Japanese earthquake. Lia et al [51] have developed a framework to measure the way that dynamic information propagates using retweets. They compare a large scale dataset from Twitter with their experimental results to evaluate their framework.…”

Section: Related Literaturementioning

confidence: 99%

Social Networking Privacy—Who’s Stalking You?

Gan

Jenkins

2015

Future Internet

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This research investigates the privacy issues that exist on social networking sites. It is reasonable to assume that many Twitter users are unaware of the dangers of uploading a tweet to their timeline which can be seen by anyone. Enabling geo-location tagging on tweets can result in personal information leakage, which the user did not intend to be public and which can seriously affect that user's privacy and anonymity online. This research demonstrates that key information can easily be retrieved using the starting point of a single tweet with geo-location turned on. A series of experiments have been undertaken to determine how much information can be obtained about a particular individual using only social networking sites and freely available mining tools. The information gathered enabled the target subjects to be identified on other social networking sites such as Foursquare, Instagram, LinkedIn, Facebook and Google+, where more personal information was leaked. The tools used are discussed, the results of the experiments are presented and the privacy implications are examined.

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“…By measuring the properties of an entity in a social network, it is possible to determine how much influence that entity has and how it impacts the social network; therefore, influence analysis is an important facet of social network analysis [8]. Most studies that evaluate individual influence are based on users' personal properties, such as their profile attributes or on their connected relationships and interaction records [9,10]. However, little attention has been paid to mining another significant entity in SNs, i.e., community influence.…”

Section: Introductionmentioning

confidence: 99%

A Community Related Influence Evaluation Model for Social Networks with a Dynamic Perspective

Xiang

Zhang

et al. 2018

Mathematical Problems in Engineering

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The nature of free information diffusion and sharing in social networks (SNs) has increasingly attracted users to gather in communities to facilitate communication, resulting in increased challenges to community influence analysis in SNs. In SNs, the ability to evaluate how a community affects other users and communities is essential for further applications. Although considerable effort has been expended on the influence evaluation of individuals, little attention has been paid to influence evaluation of the community as a whole—particularly when considering the inherent features of dynamic changes of influence over time. This paper proposes a community related influence evaluation model with a dynamic perspective (D-CIEM) that reflects two types of influence, entity influence and community-initiated influence, by considering dynamic effects over time. Our work includes the following main points: first, we address the framework of D-CIEM and its related formal definitions; then, we propose dynamic entity influence evaluation in D-CIEM, i.e., user influence and community influence evaluation with dynamic factors. We present community-initiated dynamic influence evaluation methods, including methods to calculate community influence for internal and external users and for other communities. Finally, we provide evaluation results to verify the performance of our model.

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Social network user influence sense-making and dynamics prediction

Cited by 73 publications

References 17 publications

Information diffusion through social networks: The case of an online petition

Information diffusion through social networks: The case of an online petition

Social Networking Privacy—Who’s Stalking You?

A Community Related Influence Evaluation Model for Social Networks with a Dynamic Perspective

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