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
Objectives and Key Themes
This research aims to analyze the linguistic bias in online news articles about Donald Trump from CNN and FOX News. It utilizes keyword analysis and a bias lexicon to compare the frequency of biased words used by each network. The study seeks to determine if there are statistically significant differences in the use of biased language between these two news sources, representing different sides of the political spectrum.
- Linguistic bias in news reporting
- Comparison of CNN and FOX News language use
- Application of keyword analysis techniques
- Analysis of biased language using a pre-existing lexicon
- Statistical significance of findings
Chapter Summaries
1 Introduction: This chapter introduces the study's focus on analyzing linguistic bias in news reporting from CNN and FOX News concerning Donald Trump. It highlights the evolution of news presentation across media and the growing importance of critical linguistic analysis in this context. The study's methodology, focusing on keyword analysis and a bias lexicon, is briefly outlined, emphasizing the selection of Donald Trump as a polarizing topic to reveal potential biases.
2 Theoretical Background: This section delves into existing research on linguistic bias, focusing on the "linguistic models for analyzing and detecting biased language" developed by Recasens et al. (2013). It explains the use of a bias lexicon derived from Wikipedia edits as the primary tool for identifying biased language in the study. The chapter also discusses Baker's (2004) work on keyword querying and its relevance to the current research, highlighting the methodology of comparing frequency lists and assessing statistical significance. The chapter lays the theoretical groundwork for the chosen methods of analyzing bias in news text.
3 Corpus Compilation: This chapter details the creation of the corpus used in the analysis. It describes the compilation of two subcorpora: one from FOX News and one from CNN, each containing 60 texts. The chapter meticulously explains the methods of gathering data from the respective websites, including search terms, filters used, and the process of manually downloading and cleaning the data. The significant size difference between the FOX News and CNN subcorpora is explained, due to the different formats of available content on each site. The description of the data collection method is detailed to ensure replicability.
4 Keyword Analysis: This chapter outlines the keyword analysis process. It explains the use of the Corpus of News on the Web (NOW) as a neutral reference corpus for comparison. The AntConc software and its use in generating keyword lists are described. The methodology involves comparing the network corpus' frequency list to the NOW corpus frequency list using a log-likelihood statistical test. The integration of Recasens et al.'s bias lexicon is explained, along with the identification of biased keywords. The chapter prepares the reader for the results described in the subsequent sections.
4.1 Relative Frequency of Biased Keywords: This section presents the first method of analyzing the relative frequency of biased keywords. It details the process of calculating the average relative frequency of biased keywords for both FOX News and CNN subcorpora through three different methods. These methods use various approaches for comparing the frequency of biased keywords to the total word count of each subcorpus and the entire corpora. The results, showing a negligible difference in biased keyword usage between the two networks, are presented, leading to a discussion on the need for further refinement of the analysis to account for statistical outliers.
4.2 Relative Frequency of Potent Biased Keywords: This section describes a refined analysis focusing on "potent" biased keywords, excluding less significant and statistically outlying results. The methodology focuses on identifying and including only those biased keywords with high keyness factors, to minimize the influence of irrelevant hits. It outlines strategies for excluding keywords that are only relevant to one network or show extreme frequency in a few texts and not others. The section highlights the limitations of the previous approach and introduces a more robust method for detecting significant bias in language use. The chapter sets the stage for further analysis and potentially more significant results.
Keywords
Linguistic bias, keyword analysis, news media, CNN, FOX News, Donald Trump, bias lexicon, corpus linguistics, statistical analysis, political discourse.
FAQ: Analysis of Linguistic Bias in CNN and FOX News Coverage of Donald Trump
What is the main topic of this research?
This research analyzes linguistic bias in online news articles about Donald Trump from CNN and FOX News. It aims to determine if there are statistically significant differences in the use of biased language between these two news sources.
What methodology is used in this study?
The study employs keyword analysis and a bias lexicon to compare the frequency of biased words used by CNN and FOX News. It utilizes a pre-existing bias lexicon derived from Wikipedia edits and compares frequency lists using statistical tests (log-likelihood).
What is the scope of the data used?
The study uses a corpus comprised of 60 articles from each of CNN and FOX News, focusing on articles about Donald Trump. The data collection process is described in detail to allow for replicability. A neutral reference corpus (Corpus of News on the Web – NOW) is used for comparison.
What are the key findings of the analysis of relative frequency of biased keywords?
The initial analysis of relative frequency of biased keywords showed a negligible difference in biased keyword usage between CNN and FOX News. This led to the need for a more refined approach.
How was the analysis refined to address the limitations of the initial approach?
A refined analysis focused on "potent" biased keywords, excluding less significant and statistically outlying results. This involved focusing on keywords with high keyness factors to minimize the influence of irrelevant hits. This addressed the limitations of the initial approach by excluding keywords that were only relevant to one network or showed extreme frequency in a few texts but not others.
What are the key theoretical frameworks used?
The study draws upon existing research on linguistic bias, specifically the "linguistic models for analyzing and detecting biased language" developed by Recasens et al. (2013), and Baker's (2004) work on keyword querying and statistical significance testing.
What software was used in the analysis?
The study utilized AntConc software for generating keyword lists and conducting frequency analysis.
What are the key themes explored in this research?
Key themes include linguistic bias in news reporting, comparison of CNN and FOX News language use, application of keyword analysis techniques, analysis of biased language using a pre-existing lexicon, and the statistical significance of findings.
What are the chapter summaries?
The report includes chapter summaries that detail the introduction, theoretical background, corpus compilation, keyword analysis (including relative frequency of biased keywords and potent biased keywords), and results. Each summary provides an overview of the chapter's content and methodology.
What are the keywords associated with this research?
Keywords include: Linguistic bias, keyword analysis, news media, CNN, FOX News, Donald Trump, bias lexicon, corpus linguistics, statistical analysis, political discourse.
- 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
