Predicting the Speed, Scale, and Range of Information Diffusion in Twitter

By Jiang Yang, Scott Counts. 2010

472 citations recorded by the source corpus.

We present results of network analyses of information diffusion on Twitter, via users’ ongoing social interactions as denoted by “@username” mentions. Incorporating survival analysis, we constructed a novel model to capture the three major properties of information diffusion: speed, scale, and range. On the whole, we find that some properties of the tweets themselves predict greater information propagation but that properties of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors. Implications for end users and system designers are discussed.

Subfields: Communication, Statistical and Nonlinear Physics

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Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “These approaches often combine all available data into aggregate analyses of the whole community over its entire history.”
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior (2013, background): “, how quickly emotional content might spread through social networks), which represents another important aspect of information diffusion [76].”
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake (2011, background): “Factors related to tweet content, user similarity, and user personality were explored [3,4,13,18,22,27].”
A Tale of Cities: Urban Biases in Volunteered Geographic Information (2014, background): “" @ " mentions have been identified as an important means of information diffusion on Twitter (Yang and Counts 2010) and our results suggest that rural areas participate less in this process.”
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior (2012, methodology): “Applying survival analysis, Yang and Counts [40] constructed a model to capture the three major properties of information diffusion: speed, scale, and range.”
Cyber-hate on social media in the aftermath of Woolwich (2015, methodology): “This is consistent with previous work on modelling information diffusion in social networks (Yang and Counts 2010) (see Table 1).”
Can cascades be predicted? (2014, methodology): “For example, research has specifically focused on content and users that create extremely large cascades, such as popular hashtags [15, 33] and very popular users [9, 14], which has led to criticism that cascades may only be predictable after they have already grown large [31].”
Evidence of Online Performance Deterioration in User Sessions on Reddit (2016, background): “…interconnected and people spend more time interacting through various online platforms, analyzing online performance is important for understanding how content is produced and consumed [11], how information spreads [12, 13, 14], and how people decide what and who to pay attention to [15, 16].”
RT to Win! Predicting Message Propagation in Twitter (2011, background): “Other studies of retweets concentrated on analyzing a small number of very popular tweets and their corresponding retweet networks (Nagarajan, Purohit, and Sheth 2010), or on predicting information diffusion by analyzing how tweets on the same topic spread (Yang and Counts 2010).”