Information diffusion and external influence in networks

By Seth A. Myers, Chenguang Zhu, Jure Leskovec. Knowledge Discovery and Data Mining, 2012

548 citations recorded by the source corpus.

Social networks play a fundamental role in the diffusion of information. However, there are two different ways of how information reaches a person in a network. Information reaches us through connections in our social networks, as well as through the influence external out-of-network sources, like the mainstream media. While most present models of information adoption in networks assume information only passes from a node to node via the edges of the underlying network, the recent availability of massive online social media data allows us to study this process in more detail. We present a model in which information can reach a node via the links of the social network or through the influence of external sources. We then develop an efficient model parameter fitting technique and apply the model to the emergence of URL mentions in the Twitter network. Using a complete one month trace of Twitter we study how information reaches the nodes of the network. We quantify the external influences over time and describe how these influences affect the information adoption. We discover that the information tends to "jump" across the network, which can only be explained as an effect of an unobservable external influence on the network. We find that only about 71% of the information volume in Twitter can be attributed to network diffusion, and the remaining 29% is due to external events and factors outside the network.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ea164fe038094a6ad44f70821c72cc3c3c782e9e

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In Statistical and Nonlinear Physics

Citations in the corpus

Modeling Information Propagation with Survival Theory (2013, background): “In the additive model, external inﬂuences that are endogenous to the network (Myers et al., 2012) could be considered by including an extra additive term α i 0 ( t ).”
The Anatomy of a Scientific Rumor (2013, background): “Internet mathematics 1, 226–251 (2004).”
Can cascades be predicted? (2014, background): “As we can trace information flow on Facebook exactly, we need not worry about independent entry points influencing a cascade [6, 24]; external influence instead allows us to investigate multiple independent cascades arising from the same content (see Section 5.”
News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks (2015, background): “Some authors already have addressed this issue and investigated the interconnectedness of different (social) media in the Internet and the diffusion patterns that emerge from these connections (Jain, Rodrigues, Magno, Kumaraguru, & Almeida, 2013; Kim et al., 2014; Myers et al., 2012).”
Dynamics of information diffusion and its applications on complex networks (2016, background): “[4] presented that about 71% of information by volume could be attributed to peer-to-peer interactions, and the rest is due to the external influence.”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “The analogy between the spread of a disease and information is the basis of computational methods that attempt to amplify the spread of information in networks by identifying influential “superspreaders” [1–5], and those that make inferences about the network from observations of how information spr”
Identifying a set of influential spreaders in complex networks (2016, background): “Many activities such as advertising over media and word-of-mouth on social networks can be described by information spreading on complex networks[1, 2, 3, 4, 5].”
DeepInf: Social Influence Prediction with Deep Learning (2018, background): “Such global patterns includes various respects of a cascade and their correlation with the final cascade size, e.g., the rise-and-fall patterns [32], external influence sources [33], and conformity phenomenon [43].”