Says who? Automatic text-based content analysis of television news

Carlos Castillo, Gianmarco Morales, Marcelo Mendoza, Nasir Khan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We perform an automatic analysis of television news programs, based on the closed captions that accompany them. Specifically, we collect all the news broadcasted in over 140 television channels in the US during a period of six months. We start by segmenting, processing, and annotating the closed captions automatically. Next, we focus on the analysis of their linguistic style and on mentions of people using NLP methods. We present a series of key insights about news providers, people in the news, and we discuss the biases that can be uncovered by automatic means. These insights are contrasted by looking at the data from multiple points of view, including qualitative assessment.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages53-60
Number of pages8
DOIs
Publication statusPublished - 11 Dec 2013
Externally publishedYes
Event2013 ACM International Workshop on Mining Unstructured Big Data Using Natural Language Processing, UnstructureNLP 2013, Held at 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 28 Oct 201328 Oct 2013

Other

Other2013 ACM International Workshop on Mining Unstructured Big Data Using Natural Language Processing, UnstructureNLP 2013, Held at 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period28/10/1328/10/13

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Keywords

  • Algorithms
  • Measurement
  • Theory

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Castillo, C., Morales, G., Mendoza, M., & Khan, N. (2013). Says who? Automatic text-based content analysis of television news. In International Conference on Information and Knowledge Management, Proceedings (pp. 53-60) https://doi.org/10.1145/2513549.2513558