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

TLDR

Most previous analysis of Twitter user behavior focuses on individual information cascades and the social followers graph. The authors aim to analyze aggregate user behavior and the retweet graph quantitatively, and to discuss applications for decentralized microblogging systems such as performance prediction and spam detection. The study analyzes aggregate user behavior and the retweet graph quantitatively. The authors find that users’ lifetime tweet counts follow a type‑II discrete Weibull distribution, tweet rates are asymptotically power‑law with a lognormal cutoff, inter‑tweet intervals follow a power‑law with exponential cutoff, and the retweet graph is small‑world, scale‑free, less disassortative, and highly clustered, reflecting stronger social ties.

Abstract

Most previous analysis of Twitter user behavior is focused on individual information cascades and the social followers graph. 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, the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and the inter-tweet interval distribution is power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but is 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. 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.