Real-time News Story Detection and Tracking with Hashtags

Abstract

Topic Detection and Tracking (TDT) is an important research topic in data mining and information retrieval and has been explored for many years. Most of the studies have approached the problem from the event tracking point of view. We argue that the definition of stories as events is not reflecting the full picture. In this work we propose a story tracking method built on crowd-tagging in social media, where news articles are labeled with hashtags in real-time. The social tags act as rich meta-data for news articles, with the advantage that, if carefully employed, they can capture emerging concepts and address concept drift in a story. We present an approach for employing social tags for the purpose of story detection and tracking and show initial empirical results. We compare our method to classic keyword query retrieval and discuss an example of story tracking over time.

newsLens: building and visualizing long-ranging news stories

“…Emphasis is put on scalability with a goal of processing one article per second. Poghosyan and Ifrim (2016) leverage keywords in social media to generate storylines, and Vossen et al (2015) propose to use news timelines to build storylines, structured index of events in the timeline, and event relationships.…”

Modeling Influence with Semantics in Social Networks

“…Finally, a story-tracking framework based on hashtags in OSNs is proposed in [72]. The storyline extraction is modeled as a pattern mining and real-time retrieval problem.…”

“…The story-tracking framework of study [72] is modeled as a pattern mining and real-time retrieval problem. The most popular news stories, assigned with hashtags, are detected by mining frequent hashtag pattern sets.…”

Semantic social modeling of online user - generated content

“…Finally, a story-tracking framework based on hashtags in OSNs is proposed in (Poghosyan and Ifrim, 2016). The storyline extraction is modeled as a pattern mining and real-time retrieval problem.…”

“…The story-tracking framework of study (Poghosyan and Ifrim, 2016) is modeled as a pattern mining and real-time retrieval problem. The most popular news stories, assigned with hashtags, are detected by mining frequent hashtag pattern sets.…”