Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph

Abstract

Most previous analysis of Twitter user behavior has focused on individual information cascades and the social followers graph, in which the nodes for two users are connected if one follows the other. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, that the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and that the inter-tweet interval distribution is a power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users than the social graph. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems-both predicting real-word performance and detecting spam-are discussed.

Modeling viral diffusion using quantum computational network simulation

“…It was assumed that the false news story assessed in this study was especially influential, reaching the upper bound of a 5% share probability across the population, and that the cliques to be evaluated were composed of like‐minded individuals who generally shared false news at a greater frequency than the “average” person. Sharing behaviors were expected to follow a Pareto distribution, which naturally occurs in many human processes including communication in social networks, and as Gomez‐Rodriguez et al discuss, network edges were not assumed to maintain a static state over time. After all, in the case of false news, sharing is more likely to occur early in a story's lifespan, when it is more “newsworthy,” with this likelihood rapidly decaying over time; the same could be said of biological and technological viruses whose network penetration is stymied by quarantine and inoculation as the network reacts to the threat of infection …”

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“…Song et al [27], for instance, identify spammers in real time with a measure of distance and connectivity between users in the directed friendship graph (followers and followees). Bild et al [1] designed a similar method but based on the retweet graph instead. Also based on the retweet graph, the method of Ten et al [29] detects trends by noticing changes in the size and in the density of the largest connected component.…”

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“…which computes the maximal difference between cumulative distribution functions (CDF) of a real and predicted distributions (X(x) is the CDF of a predicted data and Y (x) the CDF of a real data). Recently, Bild et al [3] showed that using the aforementioned methods, the lifetime of a tweet does not follow the Pareto distribution but in fact it is the type-II discrete Weibull distribution.…”