Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
The role of community and groupware in geocache creation and maintenance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 16th ACM international conference on Supporting group work
Hip and trendy: Characterizing emerging trends on Twitter
Journal of the American Society for Information Science and Technology
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Finding trendsetters in information networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Media marketers and researchers have shown great interest in what becomes a trend within social media sites. Their interests have focused on analyzing the items that become trends, and done so in the context of Youtube, Twitter, and Foursquare. Here we move away from these three platforms and consider a new mobile social-networking application with which users share pictures of "cool" things they find in the real-world. Besides, we shift focus from items to people. Specifically, we focus on those who generate trends (trend makers) and those who spread them (trend spotters). We analyze the complete dataset of user interactions, and characterize trend makers (spotters) by activity, geographical, and demographic features. We find that there are key characteristics that distinguish them from typical users. Also, we provide statistical models that accurately identify who is a trend maker (spotter). These contributions not only expand current studies on trends in social media but also promise to inform the design of recommender systems, and new products.