Newsenhancer

Abstract

This thesis describes a web browser plugin called NewsEnhancer, which has the goal to make it more efficient to read the news. The plugin receives data from a server application, which continuously aggregates meta information from articles on various news sites. This meta information is then presented to the user through the plugin. This can help the user to decide whether or not to click a headline link, which then can make the reading process more efficient. The plugin is also community based, and enables users to report headlines, as clickbait or fake news, and submit summaries of articles. The reported headlines will be flagged for other users, as to signal that this has been reported as a clickbait or fake news. The submitted summary will appear alongside the other meta information.