The Structural Virality of Online Diffusion

By Sharad Goel, Ashton Anderson, Jake M. Hofman, Duncan J. Watts. Management Sciences, 2015

649 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/01d5e4482c4fa7c8c4dd9a40783efc79fa7640af

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In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation (2022, methodology): “that of simulations as testbeds and tools for understanding sociotechical systems such as in modeling recommender systems [17, 21, 45, 70, 89] and online information diffusion [44, 48, 119], rather than as a source for more data.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “19The study also adopts a measure called structural virality [Goel et al. 2015] and shows that false news has greater structural virality than true news.”
Spread of Hate Speech in Online Social Media (2018, background): “Not only does it imply that such posts diffuse deeper into the network but they are also more viral (Goel et al. 2015).”
Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News (2020, background): “Video and still images are more likely than news and online petitions to spread on Twitter (Goel et al., 2015, p. 186).”
Public Health and Online Misinformation: Challenges and Recommendations. (2019, background): “that although antivaccine content was uncommon, there was a clear separation between the 438 Swire-Thompson Lazer vaccine-hesitant and mainstream”
The effect of social networks structure on innovation performance: A review and directions for research (2019, background): “Goel, Anderson, Hofman, and Watts (2016) measured the lengths of cascades in the context of content sharing online.”
The spread of true and false news online (2018, background): “We measured and compared four static characteristics of false, true and mixed rumor cascades: depth, max-breadth, structural virality [23], and size (since on Twitter a person can only retweet a tweet once, the size of a cascade corresponds to the number of unique users involved in that cascade).”
A Survey of Fake News (2020, background): “Specifically, the cascade depth, maximum breadth (and mean breadth at every depth), size, and structural virality [Goel et al. 2015] (i.”
Integrating explanation and prediction in computational social science (2021, background): “These efforts have provided insights into how content spreads, some of which align with ideas put forth several decades ago 56 and others that challenge them 57 .”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Actually, the information diffusion among the social system would be generated by the combination of broadcast and viral spreading [120].”
An exploratory study of COVID-19 misinformation on Twitter (2020, positive): “To what extent information goes viral is often modelled using epidemiological models originally designed for biological viruses [39, 40].”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “, made in direct reply to the original post), average node depth, average branching factor, proportion of top-level comments replied to, Gini coefficient of replies to top-level comments (to measure how “clustered” the total discussion is), and Wiener Index of virality (which measures the average pa”