MAPNEWS: A Framework for Aggregating and Organizing Online News Articles

[摘要] In recent years, digital news has become increasingly prevalent, with many people getting their news and information from online sources rather than traditional print or broadcast media. This shift has been driven, in part, by the convenience and accessibility of digital platforms, as well as the ability to personalize and customize news feeds. Digital news also allows for greater interactivity and engagement with readers and can reach a global audience almost instantly. News articles contain a plethora of hidden spatial information that, when shared with readers, increases comprehension of current events. Only a few news aggregation systems make this information available to users. Many stories, on the other hand, are not clearly geotagged with their spatial information. In this work, we propose the MapNews framework, a novel system that gathers, analyzes, and presents news articles on a map interface, allowing users to take advantage of their underlying spatial information. MapNews pulls content from several different internet news sources and, using a custom-built geotagger, it extracts geographic content from articles. A rapid online clustering method is used to organize articles into story clusters. Panning and zooming MapNews' map interface allows readers to receive news based on geographic location and category importance, and they will view distinct articles depending on their location. MapNews achieved an ARI score of 0.89 for clustering and an accuracy of 95% in usability testing.

[学科分类] 计算机科学（综合）

[关键词] News aggregation;information retrieval;clustering;data aggregation;web scrapping