Title :
Analyzing User Retweet Behavior on Twitter
Author :
Zhiheng Xu ; Qing Yang
Author_Institution :
Inst. of Autom., Beijing, China
Abstract :
This paper provides a deep analysis of user retweet behavior on Twitter. While previous works about analyzing retweet have mainly focused on predicting the retweetability of each tweet, they lacked interpretations at an individual level. In this paper, we perform a general analysis of retweet behavior from the perspective of individual users. Specifically, we train a prediction model to forecast whether a tweet will be retweeted by a given user, leveraging four different types of features: social-based, content-based, tweet-based and author-based features. By performing “leave-one-feature-out” comparisons, we identify factors that are strongly associated with user retweet behavior.
Keywords :
behavioural sciences computing; social networking (online); Twitter; author-based feature; content-based feature; prediction model; social-based feature; tweet-based feature; user retweet behavior analysis; Feature extraction; Logistics; Predictive models; Support vector machines; Training; Twitter; Twitter; retweet behavior; social media;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-2497-7
DOI :
10.1109/ASONAM.2012.18