Analyzing patterns of user content generation in online social networks

By Lei Guo, Enhua Tan, Songqing Chen, Xiaodong Zhang, Yihong Eric Zhao. 2009

225 citations recorded by the source corpus.

Subfields: Communication, Information Systems, Statistical and Nonlinear Physics

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In Communication

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “For example, sociological studies often use students who join a school in the same year to understand the effect of interventions [22, 30], and condition on the year in which people were born to understand people’s behavior, such as variations in financial decision-making [47] or opinions on issues…”
Combating Attacks and Abuse in Large Online Communities (2016, result): “Similar patterns have also been observed in other u ser generated content (UGC) networks, such as blogs and Q&A services [92].”
Characterizing user behavior in online social networks (2009, background): “The strong diurnal pattern in OSN workloads has also been observed in accessing messages and applications on Facebook [11] and in the content generation of blog posts, bookmarks, and answers in user generated content (UGC) websites [9, 13].”
FetchSGD: Communication-Efficient Federated Learning with Sketching (2020, unknown): “Client dataset sizes tend to follow a power law distribution (Guo et al., 2009), so most clients are likely to have datasets that are too small to benefit from many local steps.”
#mytweet via Instagram: Exploring user behaviour across multiple social networks (2015, background): “Other user activities, such as social connection [5], content generation [6], participation in conversation [7], have also been studied to gain better understanding of OSNs.”
A framework for quantitative analysis of cascades on networks (2010, unknown): “Rather, the entire data can be approximated well with a stretched-exponential (weibull), lognormal or double pareto lognormal distributions, similar to those observed in [5].”