Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

By Daniel M. Romero, Brendan Meeder, Jon M. Kleinberg. 2011

1,185 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics, Social Sciences & Humanities, Network Science & Complex Systems

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Citations in the corpus

Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, background): “For example, it may appear that repeated exposures to information or hashtags on social media make an individual less likely to share it with his or her followers [18].”
Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “1 of [20]) or share the links with followers [21] (Fig.”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “This function is similar in form to that observed in previous works [29], [26].”
Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (2020, background): “esource in studying a wide range of topics. Twitter has long been used by the research community as a means to understand dynamics observable in online social networks: from information dissemination [11, 12], to the prevalence and influence of bots and misinformation [13,14]. More importantly durin”
Predicting emerging social conventions in online social networks (2012, result): “With respect to the effect of repeated exposures in adoptions, Romero and colleagues found a similar positive effect in the spread of political hashtags in Twitter [38].”
Recommender Systems (2012, background): “Social influences may also vary across topics and items: [227] showed that different tags and topics spread on Twitter differently and [228] found that strength and direction of social influence are topic-dependent.”
Dynamical classes of collective attention in twitter (2011, background): “[10, 16, 12], and the effect of semantic differences on the persistence of a hashtag have also been considered [33].”
Partisan asymmetries in online political activity (2012, background): “option of these behaviors. Work by Romero, Meeder and Kleinberg focused speciﬁcally on online social networks indicates that this effect is particularly pronounced for political discourse on Twitter (Romero et al., 2011). With fewer users in high-order k-cores, individuals in the left-leaning commun”
Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Further studies found that the relationship could vary across topics [45], across number of friends due to the information overload of highly connected users [46, 47], and across community structures [48].”
Social media news communities: gatekeeping, coverage, and statement bias (2013, background): “Second, saturation eﬀects have been observed in Twitter; [20] demonstrated that the probability that a user posts something initially increases with the number of exposures to it, but then drops.”
News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks (2015, neutral): “, 2011), and news and content preferences (e.g., Hermida, Fletcher, Korell, & Logan, 2012; Himelboim, Hansen, & Bowser, 2013; Lehmann, Castillo, Lalmas, & Zuckerman, 2013; Romero et al., 2011; Rosengard, Tucker-McLaughlin, & Brown, 2014; Sun, Rosenn, Marlow, & Lento, 2009; Weeks & Holbert, 2013).”
Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data (2019, background): “investigated recommender systems on Twitter that consider social network features or the popularity of items in the Twitter network [28].”