Blog Post 2

"Flag of United States of America." PickPik, https://www.pickpik.com/american-flags-close-up-flags-united-states-of-america-flag-united-74927.

Introduction

The 2024 United States presidential election was an increasingly unusual election when compared to previous ones in American history. This is due to a number of key aspects of the election, such as the primary Democratic nominee, Joe Biden, dropping out of the race mere months before the election, the Republican nominee Donald Trump being tried and convicted of 34 counts of falsifying business records, as well as the fact that the Democratic nominee that took Biden's place on the ticket was Vice President Kamala Harris, who was not only a woman but a person of colour. While this election was in the early stages, it was incredibly intriguing to me whether or not Harris’ identity would play a part in the outcome of this highly unusual election, especially considering the significant amount of public discourse that was surrounding the topic. People on the left side of the aisle were claiming that Harris was being unfairly covered by the media due to her gender, whereas those who were on the right side of the aisle claimed that her media coverage was perfectly equal to that received by Trump. Looking at this discourse, this research aims to measure, in this unique election situation, whether Kamala Harris’ gender identity played a key role in influencing the media coverage that she received when compared to that of Donald Trump. In order to quantify what media bias is, three specific issue frames will be looked at, the personal frame, the issue frame and the strategic game frame. Looking into the personal frame is necessary because, according to a number of studies, women are unfairly evaluated in a personal context, whereas men are more frequently evaluated for merit based factors, therefore by looking at the personal frame of study it can be determined if female candidates are placed at a disadvantage by having their personal attributes analysed over their professional accomplishments. Similarly, by looking at the issue frame, it is clear to see if a woman is facing media bias, in which case the female candidate does not receive coverage on the same issues due to her gender, and therefore is placed at a disadvantage because voters do not have as full of a grasp on her policy for certain topics. Finally, the third issue frame that will be analysed in this research is the strategic game frame, which looks at coverage of polling and tactics, as this type of coverage, and whether it is positive or negative, can have detrimental impacts on the campaign of the individual. For instance, excessive coverage of polling for a female candidate can distract from other aspects of her campaign, or can spark doubt in potential voters, similarly, excessive coverage of tactics can make a candidate appear as though they are “calculated” or “conniving” in their pursuit of the presidency.Outside of these issue frames, I am also looking for numerical representation by determining the dominant candidate for each article in order to determine the presence of coverage of each candidate in the media. In this research, I will be analysing a number of articles from the sources the New York Times, the Wall Street Journal, and Fox News looking for the presence of each of these issue frames in each article in order to determine the presence of gender media bias in the political coverage of the 2024 election.

Progress So Far

Key Milestones

At this point in my research, I have read and analysed over 20 articles from the New York Times, the Wall Street Journal and Fox News, coding them for bias as outlined in my methods. This marks over ⅕ of my data collection being completed, as I aim to analyse 100 articles before the period for data collection has ended. Now that I have made some significant headway analysing the articles, I have been able to become more familiar with the codebook that I am using, and therefore I am able to analyse articles in a faster, more efficient manner. Moreover, by collecting this data I have been able to start seeing trends in the data that I am collecting, which continues to grow my understanding of media bias and how it could potentially present itself. I have similarly reached the milestone of overcoming my initial struggles within my database, and finding creative and new solutions that work and still operate within the parameters of my research. The limited publication selection in the database that I was initially using proved a significant setback that prevented me from starting data collection for most of January, but now that I have overcome this obstacle I am able to collect data from a range of sources and therefore collect all of the data necessary for my research.

What's Going Well?

So far, in my research process I have been able to find an abundance of articles that work within the parameters of my research, therefore, I have a number of different articles to analyse and will not be limited by the presence of available works to analyse. Moreover, the code book that I have been using has allowed me to continue to research without letting any personal opinions and bias get in the way of my research, as it holds me to strict definitions of each frame, and therefore prevents my own personal biases. Not only this, but my method of data collection so far has been working incredibly well for me, as by using a document and writing down the specific issues that are being commented on, rather than simply coding for presence or absence, I am able to see a more in depth analysis of what issues are being discussed in each articles, and am able to make note of certain key aspects of the issues.

Challenges and Problem Solving

So far in my research, I have found myself facing and overcoming numerous challenges, especially when it came to my intended methodology for my research. Initially, I had planned to look at 5 different publication sources, one for each political alignment (left, left center, center, right center, right), however, looking at the database I was using, I quickly realised that this was not possible. On the US Newsstream ProQuest database, I quickly realised that there were only a limited number of publication options that I could chose to see articles from, and almost all of the options for publications that were available fell on the left side of the political spectrum according to the All Sides and Ad Fontes media bias charts. If the publications on the ProQuest database were not considered left leaning, I found that they were considered centrally aligned, and not a single publication I found was considered to be right leaning. This left me with a clear bias in my research, as looking into only left leaning and center publications would not allow me to get a good idea of the media that American citizens were consuming as a whole, and therefore would leave me with inadequate results.Once I discovered this, I went to the Rock Canyon High School librarian Jason Parker to see if he could find a way around the lack of right leaning publications in the database that I was using. Upon further investigation , he found that not only is there a lack of right leaning publications in the ProQuest database, but other databases such as that published by EBSCO similarly do not contain these publications. While I continued to search academic databases, it was looking more and more bleak for having multiple political perspectives in my research, so, I looked back to Eliana DuBosar’s research. This is because Dubosar performed a similar research study to mine looking at the 2016 election, and her research is where the majority of my methodology had been drawn from. Therefore, by looking at DuBosar’s research, I decided to study only at the two publications she studied in her paper, the New York Times and the Wall Street Journal. Based on this information, I was able to start my data collection. However, once I had started doing data collection I realised that the New York Times was much more left-skewed, and the Wall Street Journal much more central than DuBosar’s paper had originally stated, creating an imbalance in my research. With this realisation in mind, I continued to research and found that, according to a study by the Pew Research Center, while Democrats were highly trusting of the New York times, Republicans expressed a high level of distrust in the New York Times as a news source. Moreover, I discovered that both political alignments trusted the Wall Street Journal, it being one of the most trusted news publications. This information further proved that my research was unbalanced, so I had to find a news source that was distrusted by Democrats but largely trusted by Republicans. The news source that best fit this metric was Fox News, so I decided that in order to prevent bias in my data collection, I must include Fox News articles in my article samples. In order to create a search that aligns with the original search parameters of my research, I used ChatGPT to return a list of Fox articles that matched my parameters exactly. I quickly discovered that this was not a viable method of data collection, as ChatGPT did not return a list of existing articles when prompted, rather it provided a list of hypothetical publications that did not fit within the dates specified in my search parameters. Therefore, I decided that instead of using ChatGPT to provide a list of articles that fit my search, I could instead use the AI to generate a search that would allow me to find the Fox articles that fit into my research on Google search. Once I overcame these problems, I was able to find the correct type of Fox news articles and therefore I was able to embark on my research in a way that was finally as unbiased as possible.

An image of an example search looking for news articles that fit the parameters of my research. The highlighted sections show the specific limits put on the search, such as the specific search terms, the date range and the publication I am drawing from.

This shows an example of a targeted search looking for Fox News articles. The highlighted portions show how this search fits my specific research parameters, showing the specific limiting search terms and the date range selected.

Collected Data Highlights

My data so far has been particularly interesting, as it varies greatly from my initial hypotheses. For instance, there has been no mention about the role of either candidate in their family in any of the articles I have examined, and furthermore there has been only one mention of physical appearance, discussing the facial expressions made by Harris during the presidential debates. I had originally anticipated that this part of the personal frame would be present as it has been in previous research, and has furthermore been linked to gender bias in other instances, however, it is clear that media outlets have moved away from such coverage, possibly to appear less biased or more professional. This is interesting because it shows how political coverage has varied over the years since the 2016 election, and how it varies between countries as this frame has been seen in the coverage of a number of foreign elections. Not only this, but I have discovered that so far, the majority of the articles that I have examined do not have a dominant candidate, however, those that do are more likely to have Trump as the dominant candidate than Harris. This is significant because it works to support my hypothesis that Harris will be underrepresented numerically when compared to Trump, therefore demonstrating that Harris has less media coverage in total than Trump does. Furthermore, I have learned that Harris is more likely to be referred to in the context of Biden’s campaign or presidency when covered by Fox News when compared to the Wall Street Journal or the New York Times. This is especially interesting because by referring to Harris as Biden's vice president, or as an extension of Biden's campaign, it shows how the republican news sources seek to de-legitimise her campaign by not showing as her own candidate.

This image shows the code book that I have been using, as well as the colour coordination for the classification of each issue in the issue frame. Blue highlighted issues are classified as masculine, pink highlighted issues are classifies as feminine, and orange highlighted issues are classified as neither. By having these issues colour coded, I am able to more easily categorise them and therefore my datat collection process is streamlined.

The above image shows my method of data collection and recording, as I have a spreadsheet where I record the presence of each category, what specific aspect of the category makes it present, and the nuances of each piece of data collected.

Reflection

By working in this research so diligently for the past few months, I have discovered many interesting things about the research process, my topic and myself as a researcher. During the research process I learned that research is complicated, and what worked for one research may not work for another and their study, a lesson thatI learned when looking at DuBosar’s research and attempting to imitate her study in the publications she chose. Moreover, this process has forced me to learn more about myself as a researcher, specifically how I handle and deal with adversity. Throughout this project, I have faced a number of setbacks pertaining to how I collect my data, as my initial plan and my backup plans all went out the window once I started data collection. By struggling and fighting to overcome these challenges, I learned that as a researcher I have to be creative and open to many solutions in order to make my research work as well as I possibly can with my given resources. Similarly, I learned that in order to complete research, especially after facing as many challenges as I did at the beginning of the data collection period, I have to be committed and willing to put in extra work outside of class time in order to collect enough data by the end of the collection period.

Not only did I learn about myself as a researcher, but I also learned a significant amount about the coverage of political events in the American media. For instance, American media outlets can be incredibly selective of what they cover and how they portray specific candidates based on the outlet's political affiliation. This can be seen especially when comparing Fox News to the New York Times. The New York Times typically portrays people's attitudes on Trump as negative, but not overly extreme, however, Fox News often describes Democratic attitudes relating to Donald Trump as increasingly extreme, highlighting instances when Trump was described as “dangerous to democracy” or “fascist”. This is incredibly telling about the nature of media bias in the United States, and how media partisanship can subtly worsen the political polarisation in the US.

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