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Understanding retweeting behaviors in social networks

Published: 26 October 2010 Publication History

Abstract

Retweeting is an important action (behavior) on Twitter, indicating the behavior that users re-post microblogs of their friends. While much work has been conducted for mining textual content that users generate or analyzing the social network structure, few publications systematically study the underlying mechanism of the retweeting behaviors. In this paper, we perform an interesting analysis for the problem on Twitter. We have found that almost 25.5% of the tweets posted by users are actually retweeted from friends' blog spaces. Our investigation unveils that for the retweet behaviors, some statistics still follows the power law distribution, while some others violate the state-of-the-art distribution for Web. Based on these important observations, we propose a factor graph model to predict users' retweeting behaviors. Experimental results on the Twitter data set show that our method can achieve a precision of 28.81% and recall of 37.33% for prediction of the retweet behaviors.

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cover image ACM Conferences
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management
October 2010
2036 pages
ISBN:9781450300995
DOI:10.1145/1871437
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 October 2010

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Author Tags

  1. factor graph
  2. retweet behavior
  3. social influence
  4. twitter

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CIKM '10

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  • (2024)Collaborative Graph Neural Networks for Attributed Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329800236:3(972-986)Online publication date: Mar-2024
  • (2024)Linking NFT Transaction Events to Identify Privacy RisksDistributed Ledger Technology10.1007/978-981-97-0006-6_6(82-97)Online publication date: 9-Feb-2024
  • (2023)Predicting Popularity of Viral Content in Social Media through a Temporal-Spatial Cascade Convolutional Learning FrameworkMathematics10.3390/math1114305911:14(3059)Online publication date: 11-Jul-2023
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  • (2022)Community Detection in Social Networks Considering Social BehaviorsIEEE Access10.1109/ACCESS.2022.320970410(109969-109982)Online publication date: 2022
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