2014/06/30 by Sanmay Das, Das, Sanmay, Allen Lavoie +1
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1406.7738
openalex publication_date 2014/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent research shows that humans are heavily influenced by online social\ninteractions: We are more likely to perform actions which, in the past, have\nled to positive social feedback. We introduce a quantitative model of behavior\nchanges in response to such feedback, drawing on inverse reinforcement learning\nand studies of human game playing. The model allows us to make predictions,\nparticularly in the context of social media, about which community a user will\nselect, and to quantify how future selections change based on the feedback a\nuser receives. We show that our model predicts real-world changes in behavior\non a dataset gathered from reddit. We also explore how this relatively simple\nmodel of individual behavior can lead to complex collective dynamics when there\nis a population of users, each individual learning in response to feedback and\nin turn providing feedback to others.\n