I want to analyze the linguistic features the networks use to present their news by scrutinizing linguistic bias of two networks that cover different sides of the political spectrum - CNN and FOX News. I will perform a keyword analysis on a corpus that consists of texts from the mentioned networks' websites with the topic Donald Trump. The analysis will display the different rate of use of biased words by both networks by comparing the keyword lists to a bias lexicon.
Throughout the last century, the presentation of news has changed considerably. Media like radio and television opened it to a new field of technological progress and therefore a greater accessibility for the population. The increasing importance of news and its ubiquitous presence induced a field of linguistic research that occupies itself with the critical analysis of language in news. In recent years the internet contributed to the many variations of news presentation, as it catalyzed the digital revolution. Newspapers and networks can now further publish their news in the world wide web.
Table of Contents
1 Introduction
2 Theoretical Background
3 Corpus Compilation
4 Keyword Analysis
4.1 Relative Frequency of Biased Keywords
4.2 Relative Frequency of Potent Biased Keywords
5 Results
6 Conclusion
Research Objectives and Core Themes
The primary objective of this research is to analyze and compare the linguistic bias present in online news coverage regarding Donald Trump by examining two major networks, CNN and FOX News, through a systematic keyword analysis.
- Application of the Recasens et al. (2013) bias lexicon to identify biased linguistic features.
- Compilation of a specialized network corpus consisting of online news articles from FOX News and CNN.
- Statistical comparison of keyword frequencies against a neutral reference corpus (NOW).
- Methodological distinction between general biased keywords and "potent" biased keywords to ensure statistical significance.
- Evaluation of polarising language usage within the context of digital news presentation.
Excerpt from the Publication
4 Keyword Analysis
The analysis will compare the corpus of network language of FOX News and CNN on the topic Donald to a neutral reference corpus. The reference corpus will be a random 1.7-million-word sample of the Corpus of News on the Web (NOW) (Davies, 2013), which originally comprises 4.9 billion words and grows about 4 to 5 million words each day by updating itself every night with URLs from Google News. The NOW corpus as a representation of internet news language is therefore suitable as a reference corpus to scrutinize the language CNN and FOX News explicitly use in online articles about Donald Trump.
The tool used for keyword analysis is the freeware corpus analysis toolkit AntCONC, programed by Laurence Anthony.
To create a keyword list, the NOW corpus sample was set as the reference corpus in the tool preferences and its frequency list was collated to the network corpus’ frequency list, using the log likelihood statistical test. AntCONC generated three keyword lists: one for the network corpus and one for both of the CNN and the FOX News subcorpus. The program compared the resulting keyword lists to the bias lexicon (Recasens et al., 2013). The bias lexicon is a wordlist that “contains 654 bias-inducing lemmas” generated from Wikipedia NPOV edits (Recasens et al., 2013).
Summary of Chapters
1 Introduction: This chapter introduces the evolution of news presentation and establishes the research goal of examining linguistic bias in CNN and FOX News coverage of Donald Trump.
2 Theoretical Background: This section discusses existing linguistic models for detecting biased language and outlines the methodology for keyword analysis as established by previous scholars.
3 Corpus Compilation: This chapter details the construction of the "Network corpus," explaining how subcorpora were gathered from the respective websites of CNN and FOX News.
4 Keyword Analysis: This section explains the statistical methodology, including the use of AntCONC and the classification of keywords into "biased" and "potent biased" categories to ensure data relevance.
5 Results: This chapter presents the comparative findings, noting that FOX News exhibits a higher frequency of potent biased keywords compared to CNN.
6 Conclusion: This chapter summarizes that FOX News utilizes more biased expressions than CNN and suggests directions for future research into media language.
Keywords
Linguistic bias, Keyword analysis, CNN, FOX News, Donald Trump, Corpus linguistics, AntCONC, Bias lexicon, Potent biased keywords, Statistical significance, Online news, Media language, Log likelihood, Subcorpora, News presentation.
Frequently Asked Questions
What is the fundamental focus of this research?
The research focuses on the critical linguistic analysis of news media, specifically investigating how two different news networks, CNN and FOX News, utilize biased language when reporting on a polarized political topic like Donald Trump.
What are the core thematic fields addressed in this study?
The core themes include text linguistics, digital news distribution, the construction of linguistic corpora, and the quantitative analysis of media bias through statistical word frequency comparisons.
What is the primary research question?
The primary objective is to detect whether there are significant differences in the frequency and nature of biased words used by CNN and FOX News in their online articles regarding Donald Trump.
Which scientific method is employed to achieve the results?
The study utilizes a corpus-based approach using the AntCONC toolkit to perform a keyword analysis. It compares frequency lists of the news networks against a neutral reference corpus (NOW) and applies a bias lexicon (Recasens et al., 2013) to isolate specific linguistic features.
What topics are covered in the main section of the document?
The main sections cover the theoretical framing of bias, the technical process of building a corpus from web-scraped data, the statistical methodology for identifying "potent" biased keywords, and the presentation of empirical findings.
Which keywords are essential to characterize the work?
Essential keywords include linguistic bias, keyword analysis, CNN, FOX News, Donald Trump, corpus linguistics, and potent biased keywords.
How does the author define a "potent" biased keyword?
A "potent" biased keyword is defined as a biased lemma that scores a keyness factor of over 100, which serves to exclude statistically irrelevant outliers that might appear only infrequently or in a skewed manner across the corpus.
Why was the topic of "Donald Trump" selected for this analysis?
Donald Trump was chosen because the topic is highly polarizing; such topics are specifically needed to provoke or elicit biased language, making them ideal for linguistic studies of this nature.
What is the significance of the "NOW" corpus in this study?
The Corpus of News on the Web (NOW) acts as a neutral reference corpus, representing general internet news language, which allows the author to determine which words are statistically over-represented in the specific CNN and FOX News subcorpora.
What is the main finding regarding CNN and FOX News?
The study finds that while both networks use biased expressions, FOX News employs a higher number of "potent" biased keywords with higher keyness factors than CNN, suggesting a greater prevalence of biased language in their coverage of the topic.
- Quote paper
- Sophie-Luise Müller (Author), 2017, Keyword Analysis of Biased Words Used by CNN and FoxNews, Munich, GRIN Verlag, https://www.grin.com/document/465019
