Using Social Networks for More Accurate Voting and Marketing

Vincent Conitzer, Computer Science and Economics - Duke
Social network graphs are increasingly directly accessible to us and our algorithms. For example, Facebook has direct access to the graph on its users. To what ends can we use such direct access? I will discuss three topics. First, in voting (or rating), our opinions are often affected by those of our neighbors. If we know the social network, should this affect how we count the votes in the election? (I prove a result indicating that it shouldn't.) Second, when an entity such as Facebook holds a vote among its users (as it has in the past), we may worry about the use of fake accounts to cast extra votes. We show how the incentive to do so can be removed by computing which parts of the graph are "suspect." Third (time permitting), I will discuss a marketing application, where a seller can sell to nodes in a network one at a time and the price a node is willing to pay depends on which of its neighbors have previously adopted the product. The three topics correspond to the following papers. Vincent Conitzer. Should Social Network Structure Be Taken into Account in Elections? Mathematical Social Sciences, Special Issue on Computational Foundations of Social Choice, Volume 64, Issue 1, 2012, pp. 100-102. Vincent Conitzer, Nicole Immorlica, Joshua Letchford, Kamesh Munagala, and Liad Wagman. False-Name-Proofness in Social Networks. In Proceedings of the Sixth Workshop on Internet and Network Economics (WINE-10), pp. 209-221, Stanford, CA, 2010. Sayan Bhattacharya, Dmytro Korzhyk, and Vincent Conitzer. Computing a Profit-Maximizing Sequence of Offers to Agents in a Social Network. In Proceedings of the Eighth Workshop on Internet and Network Economics (WINE-12), pp. 482-488, Liverpool, UK, 2012.
September, 3 2013 | 12:30 - 2:00 | 230E Gross Hall

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