BroadcastSTAND: Clustering Multimedia Sources of News

Jason Zhang, Ai-Te Kuo, Nicole R. Schneider, Jacob Peters, Hanan Samet

Abstract: News reaches a variety of audiences through any number of mediums, from traditional newspaper publications to social media posts to radio and TV broadcasts. These varied mediums present valuable research opportunities to gain insights into emerging trends. We present BroadcastStand, an extension framework for the NewsStand architecture, which traditionally focuses on online news articles and Twitter posts. Our objective is to seamlessly integrate a new genre of news data: radio and TV broadcasts. We show how these transcripts fit into the previous clustering landscape of traditional news data and highlight key insights that will drive future research in aggregating various news sources to increase the dimensions across which analysis can be done. In particular, we highlight the value in clustering these various sources of news data and highlight certain key pitfalls that must be addressed to obtain good clustering results.