Applications of Voting Theory to Information Mashups

Blogs, discussion forums and social networking sites are an excellent source for people’s opinions on a wide range of topics. We examine the application of voting theory to “Information Mashups” – the combining and summarizing of data from the multitude of often-conflicting sources. This paper presents an information mashup in the music domain: a Top 10 artist chart based on user comments and listening behavior from several Web communities. We consider different voting systems as algorithms to combine opinions from multiple sources and evaluate their effectiveness using social welfare functions. Different voting schemes are found to work better in some applications than others. We observe a tradeoff between broad popularity of established artists versus emerging superstars that may only be popular in one community. Overall, we find that voting theory provides a solid foundation for information mashups in this domain.

• Type: Conference
• Year: 2008
• Where: SEMCO
• Authors: Alfredo Alba, Varun Bhagwan, Julia Grace, Daniel Gruhl, Kevin Haas, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Nachiketa Sahoo
• Keywords: Information Mashups, Semantic Computing, SEMCO 2008, Social Networking Sites, Voting Theory