| The advent of social media and social networks have reshaped how we produce and consume information, as well as how we communicate with others. In this dissertation, leveraging empirical data and methods from network theory and agent-based modeling, I study how the local and aggregated information from users affect the information diffusion, individual's choices and collective behaviors. On the modeling side, I demonstrated that some degree of modularity in the underlying social network could facilitate the spread of information in a scenario where social reinforcement is relevant. I have also studied the condition under which the availability of aggregated signals about the choices of users can help to promote the high-quality items. On the empirical side, I used data from the food-oriented platform Yelp to determine whether the choices of users that are connected via a social network are indeed similar. This motivated a further study aimed at predicting the rating of users from the previous choices of their friends. I finally used conversations among the users of the game streaming platform Twitch to study how the nature of conversation changes depending on the number of participants. The ideas developed in this thesis can help to address questions about human and social behavior. |
