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.