%0 Conference Paper %1 lehmann2012dynamical %A Lehmann, Janette %A Gonçalves, Bruno %A Ramasco, José J. %A Cattuto, Ciro %B Proceedings of the 21st International Conference on World Wide Web %C New York, NY, USA %D 2012 %I ACM %K attention burst detection event evolution hashtag micropost popularity temporal twitter %P 251--260 %R 10.1145/2187836.2187871 %T Dynamical Classes of Collective Attention in Twitter %U http://doi.acm.org/10.1145/2187836.2187871 %X Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors. %@ 978-1-4503-1229-5

@inproceedings{lehmann2012dynamical, abstract = {Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors.}, acmid = {2187871}, added-at = {2014-05-14T11:29:13.000+0200}, address = {New York, NY, USA}, author = {Lehmann, Janette and Gonçalves, Bruno and Ramasco, José J. and Cattuto, Ciro}, biburl = {https://www.bibsonomy.org/bibtex/2a3ea87e38d4dfb2dc642f3c971a1b55c/jaeschke}, booktitle = {Proceedings of the 21st International Conference on World Wide Web}, doi = {10.1145/2187836.2187871}, interhash = {bf231578674c8994ec95436ab5ddbffa}, intrahash = {a3ea87e38d4dfb2dc642f3c971a1b55c}, isbn = {978-1-4503-1229-5}, keywords = {attention burst detection event evolution hashtag micropost popularity temporal twitter}, location = {Lyon, France}, numpages = {10}, pages = {251--260}, publisher = {ACM}, series = {WWW '12}, timestamp = {2016-07-15T14:41:42.000+0200}, title = {Dynamical Classes of Collective Attention in Twitter}, url = {http://doi.acm.org/10.1145/2187836.2187871}, year = 2012 }