On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Emergence of social conventions in complex networks
Artificial Intelligence
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Role model based mechanism for norm emergence in artificial agent societies
COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
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Democrats, republicans and starbucks afficionados: user classification in twitter
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The role of social networks in information diffusion
Proceedings of the 21st international conference on World Wide Web
No country for old members: user lifecycle and linguistic change in online communities
Proceedings of the 22nd international conference on World Wide Web
On popularity prediction of videos shared in online social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Tweeting under pressure: analyzing trending topics and evolving word choice on sina weibo
Proceedings of the first ACM conference on Online social networks
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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 attributing the content to its source. Despite being invented at different times and having different adoption rates, only two variations became widely adopted. In this paper we describe this process in detail, highlighting the factors that come into play in deciding which variation individuals will adopt. Our classification analysis demonstrates that the date of adoption and the number of exposures are particularly important in the adoption process, while personal features (such as the number of followers and join date) and the number of adopter friends have less discriminative power in predicting adoptions. We discuss implications of these findings in the design of future Web applications and services.