LocalSavvy

The web has become an important medium for news delivery and consumption. Fresh content about a variety of topics, events, and places is constantly being created and published on the web by news agencies around the world. As intuitively understood by readers, and studied in journalism, news articles produced by different social groups present different attitudes towards and interpretations of the same news issues. In this paper, we propose a new paradigm for aggregating news articles according to the local news sources associated with the stakeholders of the news issues. This new paradigm provides users the capability to aggregate and browse various local points of view about the news issues in which they are interested. We implement this paradigm in a system called LocalSavvy. LocalSavvy analyzes the news articles provided by users, using knowledge about locations automatically acquired from the web. Based on the analysis of the news issue, the system finds and aggregates local news articles published by official and unofficial news sources associated with the stakeholders. Moreover, opinions from those local social groups are extracted from the retrieved results, presented in the summaries and highlighted in the news web pages. We evaluate LocalSavvy with a user study. The quantitative and qualitative analysis shows that news articles aggregated by LocalSavvy present relevant and distinct local opinions, which can be clearly perceived by the subjects.