Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks

By Kristina Lerman, Rumi Ghosh. 2010

914 citations recorded by the source corpus.

Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.

Subfields: Communication, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “9 For instance, studies looking at similarities and diﬀerences among social platforms found diﬀerences with respect to user personalities (Hughes et al., 2012), news spreading (Lerman and Ghosh, 2010), or content sharing (Ottoni et al., 2014).”
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls (2014, background): “In sum, hashtag datasets should be seen as self-selected samples with data “missing not at random” and interpreted accordingly (Allison, 2001; Meiman and Freund, 2012; Outhwaite et al, 2007) All this is not to argue that hashtag datasets are not useful.”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “This makes it easier for users to respond to popular recommendations and changes the nature of contagion, though its gross properties remain similar to Digg [50].”
What Stops Social Epidemics? (2011, methodology): “We collected data from the social news aggregator Digg detailing how interest in more than 3,500 stories spreads through Digg’s social network (Lerman and Ghosh 2010).”
The role of social networks in information diffusion (2012, background): “Diﬀusion and inﬂuence have been modeled in blogs [2, 20, 25], email [31], and sites such as Twitter, Digg, and Flickr [8, 21, 29].”
Learning the graph of epidemic cascades (2012, background): “viral videos, news articles[8]) and spread of ideas and opinions (e.”
Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (2020, methodology): “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].”
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior (2012, methodology): “Regarding diffusion of information on Twitter, Lerman and Ghosh [24] conducted an empirical analysis of user activity on Digg and Twitter.”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “Since activity feeds are a primary interface element in most social networks that expose people to their friends’ updates [47, 33, 10, 7] and facilitate social learning [12, 20], we look specifically at how influence might flow through these feeds.”
Dynamics of information diffusion and its applications on complex networks (2016, background): “In addition, similar right-skewed and heavy-tailed size distribution of the diffusion networks are also observed on many other systems, such as Facebook [92], Digg [111], Sina Weibo Platform [124,125], Slashdot [126], Email Communications [127–130] and so on.”
Socializing in emergencies - A review of the use of social media in emergency situations (2015, background): “The SM can be utilized to mobilize and organize populations in order to achieve various objectives, and update them with the most up-to-date information, which might not be available through alternate official channels (Lerman & Ghosh, 2010).”
Information and Computation (2013, background): “These simple rules of the information dynamics on social networks are able to produce very interesting features of human computation (Lerman and Ghosh, 2010), which can be described as functions f .”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, background): “Researchers have also studied the dynamics of the diffusion of information throughout the Twitter user population (Lerman & Ghosh, 2010; Romero, Meeder, & Klein-berg, 2011).”
Deep Twitter diving: exploring topical groups in microblogs at scale (2014, background): “User roles in spreading information in Twitter: There have been several studies on how information and news related to speciﬁc topics spread in Twitter [17, 19], which have classi-ﬁed the different roles played by users in this diffusion.”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, methodology): “To measure the size of outbreaks on Twitter, researchers used URLs to external web content embedded in tweets as unique markers of information [21].”
#mytweet via Instagram: Exploring user behaviour across multiple social networks (2015, background): “conducted empirical analyses of user activities on Digg and Twitter to assess how it affected dissemination patterns [4].”
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake (2011, background): “Factors related to tweet content, user similarity, and user personality were explored [3,4, 13 ,18,22,27].”
Dynamical classes of collective attention in twitter (2011, background): “…including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23, 24, 25], the credibility of information [26, 27], and even its potential as an indicator of the state of mind of a population [28, 29, 30, 31, 32,…”