Social Networks that matter: Twitter under the Microscope

By Bernardo A. Huberman, Daniel M. Romero, Fang Wu. First Monday, 2008

1,361 citations recorded by the source corpus.

Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4ec99ce62a72ee1050b65389782ff3a829e97217

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Citations in the corpus

Earthquake shakes Twitter users: real-time event detection by social sensors (2010, background): “They discovered that the relation between friends (deﬁned as a person to whom a user has directed posts using an ”@” symbol) is the key to understanding interaction in Twitter [11].”
Assessing the Bias in Communication Networks Sampled from Twitter (2012, background): “…the launch of Twitter in 2006, an increasing body of research has tried to identify the topological properties of this communication network (Huberman et al., 2009; Java et al., 2007; Kwak et al., 2010), the position and characteristics of influential users (Bakshy et al., 2011; Cha et al.,…”
Microblogging Inside and Outside the Workplace (2010, background): “A directed post may also signal some degree of familiarity between the two, since a microblogger is more likely to send a directed post to a friend rather than a stranger (Huberman et al. 2009).”
Procedia Social and Behavioral Sciences (neutral): “Huberman et al. (2009) analyzed the social interaction on Twitter, revealing that the driver of usage is a sparse hidden network among friends and followers, while most of the interaction links are meaningless.”
Geography of Twitter networks (2012, background): “See also Huberman et al. (2009) on the weakness of Twitter ties.”
Mirror or Megaphone?: How relationships between narcissism and social networking site use differ on Facebook and Twitter (2013, background): “Also, Twitter allows users greater anonymity than Facebook, which may privilege the content of one’s message over one’s projected identity (Huberman et al., 2009).”
Twitter adoption and use in mass convergence and emergency events (2009, background): “Past research on Twitter (Huberman et al., 2008; Java et al., 2007; Krishnamurthy et al., 2008) has yielded important insight into how Twitter is being used in these more general cases.”
Characterizing user behavior in online social networks (2009, background): “Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.”
Imagining Twitter as an Imagined Community (2011, background): “…as a global platform for raising awareness and organizing prodemocracy protests in countries such as Iran and Moldova; organizing donation drives and supporting victims of recent earthquakes in Haiti, Chile, and Taiwan; or helping with Hawaii’s tsunami evacuation (e.g., Hughes & Palen, 2009).”
Online and Social Media Data As an Imperfect Continuous Panel Survey (2016, background): “Others papers [27,28] outline further applications of social media monitoring and user classification, including for political advertising.”
Trends in Social Media: Persistence and Decay (2011, background): “[5] studied social interactions on Twitter to reveal that the driving process for usage is a sparse hidden network underlying the friends and followers, while most of the links represent meaningless interactions.”
Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice (2015, methodology): “This method uses the friendship deﬁnition of Huberman, Romero, and Wu (2009) which is that a user a is friends with b if a has mentioned b in at least two posts.”
Using APIs for Data Collection on Social Media (2014, neutral): “ Huberman, B. A., Romero, D. & Wu, F. 2009.”