Dynamical classes of collective attention in twitter

By Janette Lehmann, Bruno Gonçalves, José J. Ramasco, Ciro Cattuto. The Web Conference, 2011

401 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/6eda0fc5b19bb21b6d2b41061da483d615f6aea0

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Citations in the corpus

Assessing the bias in samples of large online networks (2014, background): “An increasing number of studies use Twitter data to investiate a wide range of phenomena, including information diffusion nd credibility, user mobility patterns, spikes of collective attenion, and trends in public sentiment (Bakshy et al., 2011; Bollen t al., 2011; Castillo et al., 2011; Cha et al.,”
Dynamics of information diffusion and its applications on complex networks (2016, background): “[87] found that the peer-to-peer spreading process plays a minor role in hashtag popularity, while the exogenous factors such as communication in mass media would be more important to drive the hashtag propagation.”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “Previous work on attention has focused on collective attention, the attention a meme received from the entire community [43], [44], [27], [45], [46].”
Comparative Studies of Detecting Abusive Language on Twitter (2018, methodology): “Since hashtags tend to have a high correlation with the content of the tweet (Lehmann et al., 2012), we use a segmentation library2 (Segaran and Hammerbacher, 2009) for hashtags to extract more information.”
Competition among memes in a world with limited attention (2012, background): “To compound these difficulties, social networks that host information diffusion processes are not closed systems; exogenous factors like exposure to traditional media and their reports of world events play important roles in the popularity and lifetime of specific topic”
Assessing the Bias in Communication Networks Sampled from Twitter (2012, background): “…mobility patterns, spikes of collective attention, or trends in public sentiment (Bakshy et al., 2011; Bollen et al., 2011; Castillo et al., 2011; Cha et al., 2012; Dodds et al., 2011; Lehmann et al., 2012; Paltoglou and Thelwall, 2012; Quercia et al., 2012; Romero et al., 2011; Wu et al., 2011).”
Understanding the Factors That Impact the Popularity of GitHub Repositories (2016, background): “Finally, Section VIII concludes the paper and lists future work.”
Virality Prediction and Community Structure in Social Networks (2013, background): “Existing research has attempted to characterize viral memes in terms of message content [22], temporal variation [16, 24], inﬂuential users [19, 28], ﬁnite user attention [18, 21], and local neighborhood structure [10].”
Sensing Trending Topics in Twitter (2013, background): “The idea behind those methods is that breaking news, unlike other discussion topics, happen to reach a fast peak of attention from social media users as soon as they are publicly announced [26], [27].”
What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform (2018, background): “Lehmann et al. [21] analyze popularity peaks of hashtags.”
Investigating the Relations between Twitter Sentiment and Stock Prices (2015, methodology): “Within this window we evaluate the baseline volume activity TWb as the median of the window [35].”
Comparing Events Coverage in Online News and Social Media: The Case of Climate Change (2015, methodology): “The attention patterns of Twitter keywords have been described as belonging to three classes (Lehmann et al. 2012): (i) continuous, i.”