The Bursty Dynamics of the Twitter Information Network (1403.2732v1)

Published 11 Mar 2014 in cs.SI, physics.soc-ph, and stat.ML

Abstract: In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. These bursts transform users' networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.

Authors (2)

Seth A. Myers (4 papers)
Jure Leskovec (233 papers)

Citations (224)

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Summary

An Analysis of Bursty Dynamics in the Twitter Network

The research paper, "The Bursty Dynamics of the Twitter Information Network," authored by Seth Myers and Jure Leskovec, explores a comprehensive paper of the interplay between information dissemination and network evolution within the Twitter platform. This investigation bridges the gap in existing literature by analyzing how both the propagation of content and the dynamics of social connections on Twitter contribute to sudden shifts in network configuration, described as "bursts."

Key Findings

The paper focuses on a subset of 13.1 million English-speaking Twitter users, during which approximately 1.2 billion tweets were shared. Within this period, 112.3 million new connections were formed, and 39.2 million were dissolved. The analysis reveals that approximately 9% of all connections experience change monthly, characterized by a consistent "flux" of edge creation and deletion events. Crucially, these dynamics are disrupted by information diffusion events, resulting in burst-like behavior in network activity.

Two significant phenomena are identified: the "unfollow burst," where users collectively detach from the information source, and the "follow burst," characterized by a sudden influx of new connections to the source. These bursts enhance both the cohesiveness and homogeneity of a user's network, leading to higher textual similarity among followers. Additionally, new topics or external events, such as the "Occupy Wall Street" movement, can trigger significant changes in the network's structural dynamics, evidencing the impact of content on social ties.

Methodological Approach and Model

The authors developed a quantitative model to predict the occurrence of bursts based on information dissemination events. This model leverages the intuition that bursts occur when a user's content reaches new potential followers with a high level of interest similarity. The model assesses the likelihood of a follow burst by analyzing the diffusion paths and computing the normalized log-tweet similarity within the user's two-hop neighborhood, effectively quantifying compatibility in interests.

Implications and Future Directions

The research provides critical insights into the dual forces of network dynamics and information flow, emphasizing their combined impact on social media ecosystems. Practically, the findings suggest potential strategies for users aiming to optimize their visibility and engagement on platforms like Twitter by understanding and harnessing burst dynamics. Theoretically, the work contributes to the broader field of social network analysis, challenging static views of network growth and enriching models of temporal network evolution.

Future research could extend these insights by exploring content-driven variations more deeply, examining other social media platforms for comparative analysis, and integrating tweet content analysis into predictive models to further refine burst prediction accuracy. Additionally, the dynamics of unfollow bursts could offer intriguing perspectives on user preferences and content reception.

In summation, this paper underscores the complexity of Twitter's underlying social dynamics, where information diffusion acts as a catalyst for episodic change, subsequently transforming network configurations in subtle and profound ways.

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