Ins and outs of news: Twitter as a real-time news analysis service

Abstract

Both time and user popularity play a crucial role within the domain of news search - the fundamental problem lies in integrating these two onto a single platform for news retrieval systems. In this paper we propose techniques for enhancement of these systems by identifying the news topics that are popular over a certain period of time. The notion of "popularity over time" is studied with the help of the well-known real-time microblogging service "Twitter" - our method performs linguistic analysis of the news data published daily on news sites for extraction and detection of news topics that are in high demand using Twitter. We also present a prototype of our framework which detects popular news in real-time. The results obtained suggest the need of taking into account users' interest for effective news services and strongly imply that harnessing of micro-blogging data for this purpose can lead to surprising outcomes.