365 Dots in 2019: Quantifying Attention of News Sources
Alexander C. Nwala, Michele C. Weigle, Michael L. Nelson

TL;DR
This paper introduces StoryGraph, a platform that quantifies and visualizes the attention given to news stories across various media sources in near-real time, enabling analysis of news focus and trends.
Contribution
We present a novel system, StoryGraph, that extracts, represents, and scores news stories based on entity overlap and similarity, providing real-time attention metrics across diverse news outlets.
Findings
Identified top news stories of 2018 and 2019 with attention scores.
Demonstrated the platform's ability to track news attention over time.
Showcased the potential for analyzing news media focus and trends.
Abstract
We investigate the overlap of topics of online news articles from a variety of sources. To do this, we provide a platform for studying the news by measuring this overlap and scoring news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention from news organizations and identifying slow news days versus major news days. Our application, StoryGraph, periodically (10-minute intervals) extracts the first five news articles from the RSS feeds of 17 US news media organizations across the partisanship spectrum (left, center, and right). From these articles, StoryGraph extracts named entities (PEOPLE, LOCATIONS, ORGANIZATIONS, etc.) and then represents each news article with its set of extracted named entities. Finally, StoryGraph generates a news similarity graph where the nodes represent…
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Taxonomy
TopicsTopic Modeling · Computational and Text Analysis Methods · Advanced Text Analysis Techniques
