Clustering online poll data: towards a voting assistance system

Abstract

Voting advice applications (VAA) are very recently developed in order to aid users in deciding what to vote in elections. Every user is presented with a set of important issues and she is asked to submit her opinion by selecting one of a predefined set of answers (e.g. agree/disagree). The VAA gathers the same information for all candidates that are about to compete in the elections. Hence, it can provide recommendation to users: the candidates that agree with the user on these selected issues. In this paper, we propose a collaborating filtering approach for providing such suggestions. Like-minded users are clustered together based on their profiles (views on the selected issues) and voting recommendation is provided to a user by the members of the nearest (to her profile) cluster. We observe that this method produces more effective recommendations by utilizing two different measures: accuracy and weighted mean rank. Furthermore, the proposed method provides with important insight and summarization information about the electorate's opinion. This research is based on new data gathered by the voting advice application Choose4Greece which was widely used for the most recent elections in Greece.