Price trade-offs in social media advertising

Abstract

The prevalence of social media has sparked novel advertising models, vastly different from the traditional keyword based bidding model adopted by search engines. One such model is topic based advertising, popular with micro-blogging sites. Instead of bidding on keywords, the approach is based on bidding on topics, with the winning bid allowed to disseminate messages to users interested in the specific topic. Naturally topics have varying costs depending on multiple factors (e.g., how popular or prevalent they are). Similarly users in a micro-blogging site have diverse interests. Assuming one wishes to disseminate a message to a set V of users interested in a specific topic, a question arises whether it is possible to disseminate the same message by bidding on a set of topics that collectively reach the same users in V albeit at a cheaper cost. In this paper, we show how an alternative set of topics R with a lower cost can be identified to target (most) users in V. Two approximation algorithms are presented to address the problem with strong bounds. Theoretical analysis and extensive quantitative and qualitative experiments over real-world data sets at realistic scale containing millions of users and topics demonstrate the effectiveness of our approach.