%0 Book Section %1 azman2012retweets %A Azman, Norhidayah %A Millard, DavidE. %A Weal, MarkJ. %B Social Informatics %D 2012 %E Aberer, Karl %E Flache, Andreas %E Jager, Wander %E Liu, Ling %E Tang, Jie %E Guéret, Christophe %I Springer Berlin Heidelberg %K analysis dark retweet twitter %P 489-502 %R 10.1007/978-3-642-35386-4_36 %T Dark Retweets: Investigating Non-conventional Retweeting Patterns %U http://dx.doi.org/10.1007/978-3-642-35386-4_36 %V 7710 %X Retweets are an important mechanism for recognising propagation of information on the Twitter social media platform. However, many retweets do not use the official retweet mechanism, or even community established conventions, and these “dark retweets” are not accounted for in many existing analysis. In this paper, a comprehensive matrix of tweet propagation is presented to show the different nuances of retweeting, based on seven characteristics: whether it is proprietary, the mechanism used, whether it is directed to followers or non-followers, whether it mentions other users, if it is explicitly propagating another tweet, if it links to an original tweet, and what is the audience it is pushed to. Based on this matrix and two assumptions of retweetability, the degrees of a retweet’s “darkness” can be determined. This matrix was evaluated over 2.3 million tweets and it was found that dark retweets amounted to 12.86% (for search results less than 1500 tweets per URL) and 24.7% (for search results including more than 1500 tweets per URL) respectively. By extrapolating these results with those found in existing studies, potentially thousands of retweets may be hidden from existing studies on retweets. %@ 978-3-642-35385-7

@incollection{azman2012retweets, abstract = {Retweets are an important mechanism for recognising propagation of information on the Twitter social media platform. However, many retweets do not use the official retweet mechanism, or even community established conventions, and these “dark retweets” are not accounted for in many existing analysis. In this paper, a comprehensive matrix of tweet propagation is presented to show the different nuances of retweeting, based on seven characteristics: whether it is proprietary, the mechanism used, whether it is directed to followers or non-followers, whether it mentions other users, if it is explicitly propagating another tweet, if it links to an original tweet, and what is the audience it is pushed to. Based on this matrix and two assumptions of retweetability, the degrees of a retweet’s “darkness” can be determined. This matrix was evaluated over 2.3 million tweets and it was found that dark retweets amounted to 12.86% (for search results less than 1500 tweets per URL) and 24.7% (for search results including more than 1500 tweets per URL) respectively. By extrapolating these results with those found in existing studies, potentially thousands of retweets may be hidden from existing studies on retweets.}, added-at = {2013-04-10T12:00:09.000+0200}, author = {Azman, Norhidayah and Millard, DavidE. and Weal, MarkJ.}, biburl = {https://www.bibsonomy.org/bibtex/21662462c811427ebd69bd2612ab60e9d/folke}, booktitle = {Social Informatics}, description = {Dark Retweets: Investigating Non-conventional Retweeting Patterns - Springer}, doi = {10.1007/978-3-642-35386-4_36}, editor = {Aberer, Karl and Flache, Andreas and Jager, Wander and Liu, Ling and Tang, Jie and Guéret, Christophe}, interhash = {b9299ee5ff5d3a4c06d8a9650d28c291}, intrahash = {1662462c811427ebd69bd2612ab60e9d}, isbn = {978-3-642-35385-7}, keywords = {analysis dark retweet twitter}, pages = {489-502}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2013-04-10T12:00:09.000+0200}, title = {Dark Retweets: Investigating Non-conventional Retweeting Patterns}, url = {http://dx.doi.org/10.1007/978-3-642-35386-4_36}, volume = 7710, year = 2012 }