The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Predicting the popularity of online content
Communications of the ACM
ePart'10 Proceedings of the 2nd IFIP WG 8.5 international conference on Electronic participation
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
Human Psychology of Common Appraisal: The Reddit Score
IEEE Transactions on Multimedia
Sentiment strength detection for the social web
Journal of the American Society for Information Science and Technology
PLEAD 2012: politics, elections and data
Proceedings of the 21st ACM international conference on Information and knowledge management
Social resilience in online communities: the autopsy of friendster
Proceedings of the first ACM conference on Online social networks
Who watches (and shares) what on youtube? and when?: using twitter to understand youtube viewership
Proceedings of the 7th ACM international conference on Web search and data mining
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We present our approach to online popularity and its applications to political science, aiming at the creation of agent-based models that reproduce patterns of popularity in participatory media. We illustrate our approach analyzing a dataset from Youtube, composed of the view statistics and comments for the videos of the U.S. presidential campaigns of 2008 and 2012. Using sentiment analysis, we quantify the collective emotions expressed by the viewers, finding that democrat campaigns elicited more positive collective emotions than republican campaigns. Techniques from computational social science allow us to measure virality of the videos of each campaign, to find that democrat videos are shared faster but republican ones are remembered longer inside the community. Last we present our work in progress in voting advice applications, and our results analyzing the data from choose4greece.com. We show how we assess the policy differences between parties and their voters, and how voting advice applications can be extended to test our agent-based models.