Measuring Message Propagation and Social Influence on Twitter.com

Ye, Shaozhi; Wu, S. Felix

doi:10.1007/978-3-642-16567-2_16

Cited by 161 publications

(122 citation statements)

References 14 publications

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“…Foreign research generally divided into three categories: First, by the measuring data. Such as Shaozhi Ye in [1]. He measured by means of the network to get the count of the user's followers, and the count of users who have forwarding or commenting the feeds to determine the user's authority level.…”

Section: Related Researchmentioning

confidence: 99%

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Algorithms and Analysis of Identifying Regional Key Users of Microblog

Zhang¹,

Liú²,

Shi³

2014

IJET

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Abstract-Today, social networking has become a popular web activity, with a large amount of information created by millions of people every day. In this paper, we focus on Sina Weibo, a rapidly growing microblogging platform, which provides a large amount, diversity and varying quality of content. Our research is from a regional perspective, and focus on the information propagation process. By measuring the real behavior of the user's blog sharing, we construct the network model of information follow and forward. We found that a small group of nodes can cover the most of the communication behavior of the information propagation. Then, we proposed a new method to rank the users, and proposed an evaluation based on real measurement coverage. Compared to similar algorithms to verify its effectiveness and accuracy.Index Terms-Information propagation, microblog, social network, user identify.

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Section: Related Researchmentioning

confidence: 99%

“…Reference [1] and [3] both used this algorithm method to evaluate the microblog user's influence, and proved the effectiveness of the algorithm. The core idea of PageRank is that the PR value of each node is based on the amount of backlinks.…”

Section: Rtc I J Rt I J Sc Jmentioning

confidence: 99%

Algorithms and Analysis of Identifying Regional Key Users of Microblog

Zhang¹,

Liú²,

Shi³

2014

IJET

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“…Lerman and Gosh conduct an empirical description of news spread process on Digg and Twitter [18]. Ye et al show how breaking news spread through Twitter and provide metrics for social influence of users [29].Goetz et al use "zero-crossing" approach to research the temporal dynamics of the blogosphere [8]. Gomez-Rodriguez et al develop an efficient approximation algorithm to infer the information diffusion network [9].…”

Section: B Online Information Diffusionmentioning

confidence: 99%

A genealogy of information spreading on microblogs: A Galton-Watson-based explicative model

Wang

Park

Xie

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-In this paper, we study the process of information diffusion in a microblog service developing Galton-Watson with Killing (GWK) model. Microblog services offer a unique approach to online information sharing allowing microblog users to forward messages to others. We describe an information propagation as a discrete GWK process based on Galton-Watson model which models the evolution of family names. Our model explains the interaction between the topology of the social graph and the intrinsic interest of the message. We validate our model on dataset collected from Sina Weibo and Twitter microblog. Sina Weibo is a Chinese microblog web service which reached over 100 million users as for January 2011. Our Sina Weibo dataset contains over 261 thousand tweets which have retweets and 2 million retweets from 500 thousand users. Twitter dataset contains over 1.1 million tweets which have retweets and 3.3 million retweets from 4.3 million users. The results of the validation show that our proposed GWK model fits the information diffusion of microblog service very well in terms of the number of message receivers. We show that our model can be used in generating tweets load and also analyze the relationships between parameters of our model and popularity of the diffused information. To the best of our knowledge, this paper is the first to give a systemic and comprehensive analysis for the information diffusion on microblog services, to be used in tweets-like load generators while still guaranteeing popularity distribution characteristics.

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“…To investigate if there is an increased information disclosure as the relative vulnerability of profiles increases, we used Spearman rank as used by Ye and Wu (2010) to correlate profiles that have a relative vulnerability greater than 0.8 against the amount of information disclosure for certain attributes displayed in the profiles' friends comments. …”

Section: Model Validationmentioning

confidence: 99%

A vulnerability evaluation framework for online social network profiles: axioms and propositions

Alim

Neagu

Ridley

2012

IJITST

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Abstract:Online social network (OSN) usage has led to personal details being presented on online profiles readily. This can cause profile owners to be vulnerable to social engineering attacks. Our approach to quantifying vulnerability consists of a model with three components: individual, relative and absolute vulnerabilities.The individual vulnerability is calculated by allocating weights to profile attribute values disclosed which may contribute towards the personal vulnerability of the profile owner. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall vulnerability for the profile which considers the individual and relative vulnerabilities.This paper extends research done on axioms based on the vulnerability model, by stating propositions to explore the effects of different operators on the profiles relative and absolute vulnerabilities. The case studies show that our approach offers a formal background for estimating how attributes and operator changes influence the individual, relative and absolute vulnerability of OSN profiles.Keywords: vulnerability; online social networks; OSN; axioms; propositions; information disclosure. Daniel Neagu is Professor of Computing at the University of Bradford. His research interests include knowledge discovery, information retrieval, data mining applications in multidisciplinary projects (with a focus in online social networks, healthcare and web profiling) by fusion of human experts knowledge and AI tools. He has published over 100 peer-reviewed papers and is the A vulnerability evaluation framework for online social network profiles 179 principal investigator in projects funded by EU FP7, EPSRC,

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Measuring Message Propagation and Social Influence on Twitter.com

Cited by 161 publications

References 14 publications

Algorithms and Analysis of Identifying Regional Key Users of Microblog

Algorithms and Analysis of Identifying Regional Key Users of Microblog

A genealogy of information spreading on microblogs: A Galton-Watson-based explicative model

A vulnerability evaluation framework for online social network profiles: axioms and propositions

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