Can cascades be predicted?

By Rediet Abebe. The Web Conference, 2014

869 citations recorded by the source corpus.

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3210f38625d086d56ba02704f95662520e7b9560

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In Computer Networks and Communications

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In Statistical and Nonlinear Physics

Citations in the corpus

Spread of Hate Speech in Online Social Media (2018, background): “However, there are several works that looks into diffusion of fake news [26, 37, 41], Linkedin [3], retweet cascade [8, 9, 18, 37], rumours [11, 17, 20, 22, 45] and Tumblr [1, 2, 7, 42].”
Popularity and Quality in Social News Aggregators: A Study of Reddit and Hacker News (2015, background): “Both (Cheng et al. 2014) and (Bakshy et al. 2011) ﬁnd that content features are weak features in comparison to structural features, such as the networks of original posters and the timing of early shares and retweets.”
DeepCas: An End-to-end Predictor of Information Cascades (2016, methodology): “Indeed, cascades of microblogs/Tweets [43, 39, 44, 20, 11, 17, 37], photos [10], videos [2] and academic papers [31] are proved to be predictable to some extent.”
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.”
False Information on Web and Social Media: A Survey (2018, background): “These accounts are synthetically created or compromised [85], controlled by a single underlying entity, and engineer the spread of false information through social networks [11, 19], with the aim of creating an illusion of public consensus towards the false pieces of information.”
Integrating explanation and prediction in computational social science (2021, background): “Conversely, in quadrant 3 is research that uses as much information as possible to passively forecast content popularity 48,59,60 .”
Science vs Conspiracy: Collective Narratives in the Age of Misinformation (2014, background): “A large body of literature addresses the study of social dynamics on socio-technical systems from social contagion up to social reinforcement [19–37].”
Examining a hate speech corpus for hate speech detection and popularity prediction (2018, methodology): “We use an large set of features inspired by re-lated work (Waseem and Hovy, 2016; Sutton et al., 2015; Suh et al., 2010; Zaman et al., 2013; Hong et al., 2011; Zhang et al., 2012; Cheng et al., 2014; Ma et al., 2013; Zhao et al., 2015).”
Measuring Emotional Contagion in Social Media (2015, background): “The study of socio-technical systems, and their effects on our increasingly interconnected society, is playing a signiﬁcant role in the emerging ﬁeld of computational social science [30, 47, 20, 26, 3, 42, 8].”
Topological Recurrent Neural Network for Diffusion Prediction (2017, background): “They often rely on explicitly engineering useful features to predict the activation probability of a node, such as network structure and temporal information [4], user nodes’ social roles [11], diffusion content [12] and user nodes’ interactions [13].”
DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades (2017, methodology): “For feature-based approaches, we use the features mentioned by Cheng et al. [4] and Shulman et al. [5] to investigate the predictbility of cascades.”