Retweeting is the key mechanism for information diffusion in Twitter. It emerged as a simple yet powerful way of disseminating information in the Twitter social network. Even though a lot of information is shared in Twitter, little is known yet about how and why certain information spreads more widely than others. In this paper, we examine a number of features that might affect retweetability of tweets. We gathered content and contextual features from 74M tweets and used this data set to identify factors that are significantly associated with retweet rate. We also built a predictive retweet model. We found that, amongst content features, URLs and hashtags have strong relationships with retweetability. Amongst contextual features, the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, the number of past tweets does not predict retweetability of a users tweet. We believe that this research would inform the design of sensemaking and analytics tools for social media streams.
Suh, B.; Hong, L.; Pirolli, P. L.; Chi, E. H. Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. Second IEEE International Conference on Social Computing (SocialCom); 2010 August 20-22; Minneapolis, MN. Los Alamitos CA: IEEE Computer Society; 2010; 177-184.