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

Abstract

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.

Diffusion of Scientific Articles across Online Platforms

“…It has been shown before that posting of web links (URLs), news, photos, and videos on certain platforms is organized in bursts (Adar and Adamic 2005;Lerman and Ghosh 2010;Cheng et al 2014;. Here, we demonstrate that bursty behavior is typical for dissemination of scientific articles as well, across all considered platforms.…”

“…Despite the ubiquity and importance of cross-platform sharing and consumption of information, they remain understudied even in the context of information diffusion more broadly. Existing literature has looked at diffusion within individual platforms like blogs (Adar and Adamic 2005), webpages (Ratkiewicz et al 2010), Twitter (Lerman and Ghosh 2010;Vosoughi, Roy, and Aral 2018), Digg (Lerman and Ghosh 2010), Facebook (Cheng et al 2014;, and Wikipedia (Keegan, Gergle, and Contractor 2013). A few studies have looked into pairwise connections between the media, such as news outlets and blogs (Leskovec, Backstrom, and Kleinberg 2009), or news and Facebook (Tan, Friggeri, and Adamic 2016).…”

Predicting Successful Memes Using Network and Community Structure

“…Large T (h) does not necessarily implies large A(h), because a single user may generate many tweets. Meme popularity exhibits a broad and skewed distribution, as observed in many previous studies (Lerman and Ghosh 2010;Weng et al 2012). We partition all the memes into classes based on the order of magnitude of the total popularity ( log 10 |T | + 0.5 or log 10 |A| + 0.5 ).…”

What Stops Social Epidemics?

“…Many diverse phenomena can be modeled as contact processes, including adoption of new ideas (Rogers 2003;Bettencourt et al 2005), spread of infectious disease (Anderson and May 1991;Hethcote 2000) and behaviors (Christakis and Fowler 2007;, computer virus epidemics on the Internet (Castellano and Pastor-Satorras 2010), wordof-mouth recommendations (Goldenberg, Libai, and Muller 2001), viral marketing campaigns (Kempe, Kleinberg, and Tardos 2003;Iribarren and Moro 2009), and information cascades in online social networks (Lerman and Ghosh 2010). A contact process is simply a diffusion of activation on a graph, where each activated, or "infected," node can infect its neighbors with some probability given by the transmissibility.…”

“…The proliferation of online social networks on sites such as Facebook, Twitter, and Digg, where users explicitly declare social links and actively spread information, gives us a unique opportunity to quantitatively study dynamics of contact processes. 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). A user becomes inf ected by digging (i.e., voting for) a story and exposes her network neighbors to it.…”