Social Incentive Optimization in Online Social Networks
Guangde Chen, Bee-Chung Chen, Deepak Kumar Agarwal · 2017
Most online social networks provide a mechanism for users to broadcast messages to their personalized network through actions like shares, likes and tweets. Receiving positive feedback from the network such as likes, comments and retweets in response to such actions can provide a strong incentive for users to broadcast more often in the future. We call such feedback by the network, that influences a user to perform certain desirable future actions, social incentives. For example, after a user shares an article to her social network, receiving positive feedback such as a ''like'' from a friend can potentially encourage her to continue sharing more regularly. Typically, for every user's visit to an online social network site, good messages need to be ranked and selected by a recommender system from a large set of candidate messages (broadcasted by the user's network). In this paper, we propose a novel recommendation problem: How should we recommend messages to users to incentivize neighbors in their personal network to perform desirable actions in the future with high likelihood, without significantly hurting overall engagement for the entire system? For instance, messages could be content shared by neighbors. The goal in this case would be to encourage more content shares in the future. We call this problem social incentive optimization and study an instance of it for LinkedIn's news feed. We observe that a user who receives positive social feedback from neighbors has a higher likelihood of broadcasting more frequently. Using this observation, we develop a novel recommendation framework that incentivize users to broadcast more often, without significantly hurting overall feed engagement. We demonstrate the effectiveness of our approach through causal analysis on retrospective data and online A/B experiments.