The structure of online diffusion networks

By Sharad Goel, Duncan J. Watts, Daniel G. Goldstein. ACM Conference on Economics and Computation, 2012

410 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b92cdc4b6a6d0c8a616e2f1e274cd6ce918cdeab

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Citations in the corpus

Dynamics of information diffusion and its applications on complex networks (2016, background): “Recently, although many theoretical model and numerical simulation methods [119] are proposed to interpret the spreading dynamics on the underlying network structure, the simulation results are always far away from the empirical observations [6].”
Searching for superspreaders of information in real-world social media (2014, background): “However, given that the underlying dynamical model of a predictor can heavily affect its performance, and the fact that in reality the information does not spread in a random walk fashion [51], then, PageRank does not perform as well as the k-core-based methods.”
Data ex Machina: Introduction to Big Data (2017, background): “The dissemination of news and rumors has been studied on Wikipedia (Keegan et al. 2013, Keegan & Brubaker 2015), Twitter (Bakshy et al. 2011, Romero et al. 2011, Yang & Counts 2010), and across platforms (Goel et al. 2012, Kim et al. 2014).”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “Empirical studies of information spread in social media have failed to observe outbreaks as large as those predicted by the independent cascade model [14, 19, 20].”
The Structural Virality of Online Diffusion (2015, background): “First, and consistent with previous work (Goel et al. 2012), the vast majority of diffusion events are small and accordingly lack much structure.”
Integrating explanation and prediction in computational social science (2021, background): “Some sit squarely in quadrant 1, as purely descriptive studies that measure the size and structure of large and representative sets of online information cascades 48,55 .”
Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter? (2013, background): “…and the nature of the Twitter social network, and on issues related to influence and information diffusion on this network (see for example Cha et al. 2010; Kwak et al. 2010; Weng, Lim and Jiang 2010; Bakshy et al. 2011; Romero et al. 2011; Wu et al. 2011; Goel, Watts, and Goldstein 2012).”
Can cascades be predicted? (2014, background): “These cautionary notes fall into two main genres: first, that large cascades are rare [11]; and second, that the eventual scope of a cascade may be an inherently unpredictable property [28, 31].”
Rumor Cascades (2014, background): “…studying the spread of news (Bhattacharya and Ram 2012), characterizing the structure of information cascades (Dow, Adamic, and Friggeri 2013; Goel, Watts, and Goldstein 2012; Liben-Nowell and Kleinberg 2008), or estimating in-ﬂuence on the diffusion process (Bakshy et al. 2011), the…”
The spread of true and false news online (2018, background): “[57] and is used to establish true retweet cascades in a broad range of academic studies of Twitter.”
Filter Bubbles, Echo Chambers, and Online News Consumption (2016, neutral): “6 Studies using actual browsing behavior tend to find that this number is quite a bit lower (Goel et al., 2012a; LaCour, 2013).”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, background): “While a large number of previous studies have focused on social contagions in on-line media (Du et al. 2013; Goel, Watts, and Goldstein 2012; Gomez-Rodriguez, Leskovec, and Krause 2010; Leskovec, Backstrom, and Kleinberg 2009), most ignore the impact of information overload on users’ forwarding…”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “Contrary to the popular narrative on influence, our results join other work in questioning the extent of influence or virality in these networks [27, 24], at least as conveyed through copying others’ observed behaviors in activity feeds.”
Co-Writing with Opinionated Language Models Affects Users’ Views (2023, background): “Research has shown that a variety of sources influences users’ attitudes and behaviors, including friends, family, experts, and internet celebrities [48, 78]; the latter group was labeled influencers due to their influence on a large group of ’followers’ [10].”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “For example, scholars have studied the propagation and correction of rumors(Friggeri et al. 2014), the role of in-ﬂuential users in spreading information (Bakshy et al. 2012), or whether information actually diffuses in a viral way at all (Goel, Watts, and Goldstein 2012).”
Latent Space Model for Multi-Modal Social Data (2015, neutral): “ACM 978-1-4503-4143-1/16/04. http://dx.doi.org/10.1145/2872427.2883031.”