The Emergence of Conventions in Online Social Networks

By Farshad Kooti, Haeryun Yang, Meeyoung Cha, Krishna P. Gummadi, Winter A. Mason. 2012

100 citations recorded by the source corpus.

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.

Subfields: Sociology and Political Science, 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 analyses uncover insights about the lifecycle of a user in a community: users’ preferences and behavior change with their age in a community [49], while their early experiences and activity shape future outcomes predictably [43,48,66,70].”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, methodology): “In particular, we study the propagation of hashtags (Romero, Meeder, and Kleinberg 2011), retweet conventions (Kooti et al. 2012), and URL shortening service usage (Antoniades et al. 2011) through the Twitter network.”
Predicting emerging social conventions in online social networks (2012, background): “An in-depth examination revealed that in-degree distributions of the first 500 adopters were not particularly different from one variation to another [29].”
No country for old members: user lifecycle and linguistic change in online communities (2013, unknown): “By applying this framework to two large online communities we show that users follow a determined two-stage lifecycle with respect to their susceptibility to linguistic change: a linguistically innovative learning phase in which users adopt the language of the community followed by a conservative…”
Computational Sociolinguistics: A Survey (2016, background): “Within communities, norms emerge over time through interaction between members, such as the use of slang words and domain-specific jargon (Danescu-NiculescuMizil et al. 2013b; Nguyen and Rosé 2011), or conventions for indicating retweets in Twitter (Kooti et al. 2012).”