What is Twitter, a social network or a news media?

@inproceedings{Kwak2010WhatIT,
  title={What is Twitter, a social network or a news media?},
  author={Haewoon Kwak and Changhyun Lee and Hosung Park and Sue B. Moon},
  booktitle={The Web Conference},
  year={2010}
}
Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. [] Key Result In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending…
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6,716 Citations
Highly Influential Citations
582
Background Citations
3,050
Methods Citations
652
Results Citations
156

6,716 Citations

Topics and Influential User Identification in Twitter using Twitter Lists

By analyzing Twitter List, this paper is able to detect topics and identify influential users in the corresponding topic more efficiently and, on an experimental evaluation, the influential users identified by the proposed method have the average influence score related to the topic made by interviewees.

A study on Twitter user-follower network a network based analysis

The investigations showed that the Twitter user-follower network follows power-law degree distribution and was found to be a connected network, which yields to friend recommendations, target based advertisements, etc.

Towards scalable X10 based link prediction for large scale social networks

This research implemented one of the link prediction algorithm named Link Propagation in X10, which is a parallel programming language, and evaluated its scalability and precision with Twitter user network data.

Identifying topical influencers on twitter based on user behavior and network topology

Quantifying Influence in Social Networks and News Media

  • Hong-Won Yun
  • Computer Science
    J. Inform. and Commun. Convergence Engineering
  • 2012
This work chose Twitter and the New York Times as representative media to analyze the influence and present an empirical analysis of these datasets, and measures the normalized influence score using the proposed expression based on the two correlation coefficients.

Estimating the Spreading of Viral Threads on Twitter

This model is based on the hypothesis that it is highly probable that a user decides to retweet a tweet if he/she is following either the tweet author, or another retweeter of the tweet, and chooses as main features of a tweet its number of retweets and the number of followers of retweeting users.

Information Propagation Speed and Patterns in Social Networks: A Case Study Analysis of German Tweets

The analysis has shown that information diffuses over time through the Twitter network in certain patterns, and it has shown those friend relationships significantly influence the information propagation speed on Twitter.

TRank: Ranking Twitter users according to specific topics

  • M. MontangeroM. Furini
  • Computer Science
    2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)
  • 2015
This paper proposes TRank, a novel method designed to address the problem of identifying the most influential Twitter users on specific topics identified with hashtags that combines different Twitter signals to provide three different indicators that are intended to capture different aspects of being influent.

Twitter as a Personalizable Information Service

This chapter proposes an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user.

Online social network evolution: Revisiting the Twitter graph

This study revisits the network observed by Kwak et al. in 2009, using most of the Twitter network at the time to examine the changes exhibited in both the graph and the behavior of the users in it, and concludes to a denser network.
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