Clustering Online Poll Data: Towards a Voting Assistance System

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.