all AI news
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
April 16, 2024, 4:51 a.m. | Dror K. Markus, Effi Levi, Tamir Sheafer, Shaul R. Shenhav
cs.CL updates on arXiv.org arxiv.org
Abstract: Media Storms, dramatic outbursts of attention to a story, are central components of media dynamics and the attention landscape. Despite their significance, there has been little systematic and empirical research on this concept due to issues of measurement and operationalization. We introduce an iterative human-in-the-loop method to identify media storms in a large-scale corpus of news articles. The text is first transformed into signals of dispersion based on several textual characteristics. In each iteration, we …
abstract arxiv attention components concept cs.cl dynamics iterative landscape measurement media research scale significance story type wind
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
C003549 Data Analyst (NS) - MON 13 May
@ EMW, Inc. | Braine-l'Alleud, Wallonia, Belgium
Marketing Decision Scientist
@ Meta | Menlo Park, CA | New York City