Why We Twitter: An Analysis of a Microblogging Community

Java, Akshay; Xue, Song; Finin, Tim; Tseng, Belle L.

doi:10.1007/978-3-642-00528-2_7

Cited by 184 publications

(164 citation statements)

References 20 publications

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“…1b. The in-degree distribution (inset) shows a power-law decay p i ∼ i −2.06 over a large range of in-degrees (the powerlaw exponents are estimated by the method in [7]); however, the out-degree distribution clearly shows a departure from the power-law nature that was observed by measurements on Twitter before the restriction was imposed [10,12]. Now, the power-law p j ∼ j −1.92 for the out-degrees below the point of restriction is followed by a sharp spike at around out-degree j = 2000, and a rapid decay in the distribution beyond this point.…”

Section: Empirical Measurements On the Twitter Social Networkmentioning

confidence: 91%

“…joining of new users, creation of new social links) by the preferential attachment model [4] which has been experimentally shown to occur in several OSNs [13,15]. Also, it produces power-law degree distributions similar to the empirical distributions in Twitter before the restriction was imposed [10,12]. Our proposed model is a customized version of the network-growth model proposed by Krapivsky et.…”

Section: Modeling Restricted Growth Dynamics Of Osnsmentioning

confidence: 96%

“…The exponents were actually found [10] to be equal for the Twitter OSN before the restriction was imposed. Since we are studying restrictions only on out-degree (as in Twitter), we shall henceforth consider only the out-degree distribution.…”

Section: Extending the Model To Find Degree Distributions Analyticallymentioning

confidence: 99%

“…The Twitter OSN has been of interest to researchers since 2007 and there have been several attempts [8,10,12,14] to crawl the Twitter network 1 . Recently a large crawl of the entire Twitter social network in July 2009, containing about 41.7 million nodes and 1.47 billion follow-edges, has been made publicly available [14]; we use this data for empirical measurements in this paper.…”

Section: Empirical Measurements On the Twitter Social Networkmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Ghosh

Srivastava

Ganguly

2011

Lecture Notes in Computer Science

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Abstract. Most popular OSNs currently restrict the number of social links that a user can have, in order to deal with the problems of increasing spam and scalability in the face of a rapid rise in the number of users in recent years. However such restrictions are often being criticized by socially active and popular users, hence the OSN authorities are facing serious design-choices while imposing restrictions; this is evident from the innovative 'soft' cut-off recently imposed in Twitter instead of the traditional 'hard' cut-offs in other OSNs. Our goal in this paper is to develop an analytical framework taking the restriction in Twitter as a case-study, that can be used to make proper design-choices considering the conflicting objectives of reducing system-load and minimizing userdissatisfaction. We consequently define a simple utility function considering the above two objectives, and find that Twitter's policy well balances both. From a network science perspective, this is the first analysis of 'soft' cut-offs in any sort of network, to the best of our knowledge.

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Section: Empirical Measurements On the Twitter Social Networkmentioning

confidence: 91%

Section: Modeling Restricted Growth Dynamics Of Osnsmentioning

confidence: 96%

Section: Extending the Model To Find Degree Distributions Analyticallymentioning

confidence: 99%

Section: Empirical Measurements On the Twitter Social Networkmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Ghosh

Srivastava

Ganguly

2011

Lecture Notes in Computer Science

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“…Mislove et al measure the structure of Flickr, YouTube, LiveJournal, and Orkut [22], and observe the growth of the Flickr social network [21]. Java et al study the topological and geographical properties of Twitter [13]. Huang et al measure user prestige and visible interaction preference in Renren [12].…”

Section: Related Workmentioning

confidence: 99%

Understanding latent interactions in online social networks

Jiang

Wilson

Wang

et al. 2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

152

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Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior, and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, passive actions such as profile browsing that cannot be observed by traditional measurement techniques.In this paper, we seek a deeper understanding of both visible and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 150 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed visitor logs for each user profile, and counters for each photo and diary/blog entry. We capture detailed histories of profile visits over a period of 90 days for more than 61,000 users in the Peking University Renren network, and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than visible events, non-reciprocal in nature, and that profile popularity are uncorrelated with the frequency of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior, and compare their structural properties against those of both visible interaction graphs and social graphs.

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Bibliography

2010

An Introduction to Search Engines and Web Navigation

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Why We Twitter: An Analysis of a Microblogging Community

Cited by 184 publications

References 20 publications

Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Assessing the Effects of a Soft Cut-Off in the Twitter Social Network

Understanding latent interactions in online social networks

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