By Jem Shin ’23, Latonya Smith ’24, and Emma Tuhabonye ’23

Our goal was to use deep learning-based facial recognition algorithms to determine the appearances of political leaders, candidates, and opponents in political ad images. To do this, we ran a facial recognition algorithm in Python on Snapchat political campaign ad images. This algorithm uses the method, Histogram of Oriented Gradients (HOG) (Dalal & Triggs, 2005), for face detection, and a deep convolutional neural network (CNN) model for face encoding.   

We first wanted to compare the facial recognition results with setting different tolerances and comparing the results. Tolerance is equivalent to sensitivity, the lower the tolerance the more strict the algorithm is at facial comparisons. After completing the testing of tolerances 0.5, 0.6, and 0.7, we wanted to compare the accuracy of the faces found with the results from Amazon Web Services (AWS) that the Wesleyan Media Project (WMP) had previously analyzed. This allowed us to compare the open source facial recognition software and find that the tolerance .5 was the most accurate in identifying faces among the Snapchat images and videos. 

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By Ori Cantwell ‘22

This research aims to understand the role of station ownership in local news stations’ discussion of vaccinations during the ongoing COVID-19 pandemic. Past research tells us that a large proportion of the US population gets public health information from local news, with those who get COVID-19 vaccine information from local news expressing greater intent to get a COVID-19 vaccine than those who did not get their information from local TV, regardless of how much they trusted the vaccine information (Piltch-Loeb et al. 2021; Nagler et al. 2020; Hamel et al. 2021; Gollust, Fowler, and Niederdeppe 2019). Clearly, local news has power in sharing public health information. 

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By Trey Plante ‘24, Dale Ross ‘22

Our goal was to develop a dataset of Snapchat ads to enable the Wesleyan Media Project to research an area of ads that impacts a younger demographic, and hasn’t been explored as thoroughly as other platforms like Facebook. The Snapchat political ads library offers an interesting look into how political ads operate on primarily video based platform that has unique user base. By investigating the Snapchat political ad library, we hope to recognize differences between ads on different platforms, and discover underlying trends in political entities behavior across platforms. We extracted text from the speech in ad videos and text in ad images and generated facial recognition results from the Snapchat 2020 political ad dataset. Additionally, we developed a classifier that predicts the party lean of a Snapchat ad using the content data we gathered. As a result of our research, we have made it possible for the Wesleyan Media Project to analyze Snapchat ads, and have taken steps to predict the party of future Snapchat ads.

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By Brianna Mebane ‘22

This research centers around discussions of race and racial justice in Facebook campaign advertisements run during the 2020 election cycle. More specifically, this research analyzed Facebook campaign advertisements run by 2020 presidential and Georgia Senate special candidates in the state of Georgia. 2020 marked a watershed in the contemporary fight for racial justice in the United States following the highly publicized murders of innocent Black people like George Floyd, Breonna Taylor, and Ahmaud Arbery. The Georgia Senate special elections in particular were a major talking point due to national campaigns led by people like former Georgia State Rep. Stacey Abrams and activist LaTosha Brown to increase Black votership across the state. By reading this blog post, audiences will gain a better understanding of how politicians discussed race and racial justice during this major moment in contemporary American history.

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By Trey Plante ‘24 and Dale Ross ‘22

Our goal was to analyze the multiple classifiers that the Wesleyan Media Project has run on political advertisements and uncover the patterns that the classifier identified and utilized to make its predictions. The ABSA classifier works by analyzing the text of an ad for mentions of Joe Biden and Donald Trump and using sentiment analysis to predict which party the ad supports, while the Party All classifier works by running a machine learning method that uses hand-coded party training data to predict ad lean. By investigating how the classifiers actually work, we hope to enable the Wesleyan Media Project to improve the classification of advertisements and, perhaps more importantly, understand what the algorithms we utilize do. In other words, we want to turn our classifiers into something we understand and can explain, instead of a “black box.” The classifiers were run on the WMP’s set of ads from the 2020 election cycle. We analyze the trends and distributions in the set of classified ads to see the underlying patterns our classification algorithm is capturing. With these analyses, we hope to find sources of classification bias and error and seek to explain why the classifier does classify an ad to a specific party. As a result of our research, we were able to improve the classifier’s accuracy over the whole election cycle and uncover trends associated with regionally concentrated ads.

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By Ori Cantwell ‘22, Dale Ross ‘22, Emma Tuhabonye ‘23

Abortion has emerged as a key polarizing issue for voters over the last few decades. Attitudes toward abortion predict voters’ decisions across levels of government––presidential, congressional, gubernatorial, lower offices––making abortion a matter of issue ownership for political parties (Jelen & Wilcox, 2003). Since the pro-life movement gained political traction in the 1980s, media attention on pro-choice vs. anti-abortion interest groups has consistently (a) linked the groups to distinct parties and (b) amplified party-specific positions in the mind of the American electorate (Carmines & Wagner, 2010). As such, pro-choice has become synonymous with the Democratic Party and anti-abortion with the Republican Party. In addition, long-term exposure to Facebook political advertisements about abortion and women’s healthcare may impact voter turnout in competitive congressional districts, particularly among women voters (Haenschen, 2022). The national conversation on abortion has become increasingly heated in the past election cycle, and abortion will only become a bigger issue when the Supreme Court rules on modifications to 1973’s landmark Roe v. Wade case during the upcoming 2022 midterm election cycle (Hulse, 2021). 

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By Natalie Appel ‘23, Noah Cohen ‘22, Spencer Dean ‘21, Sam Feuer ‘23, Magda Kisielinska ‘22, and Brianna Mebane ‘22

Since 2010, the Wesleyan Media Project has hand coded American political advertisements for an extensive list of variables relating to content and tone. The information collected through this process is insightful, however it is a time intensive task. Thus, the initial question we sought to answer was how much, if any, of this process could be automated in order to keep up with the scale of digital advertising. Our goal was to do so by training machine learning models on the text of the existing hand coded ads in order to predict the characteristics of new ads. We tested a number of methods and ultimately found that our Random Forest worked best for binary issue variables while a distilBERT Neural Network worked best for multi class variables, especially ad tone.

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By Angela Loyola ’21 and Sam Feuer ’23

A prior Delta Lab research project on the amount of spending by presidential candidates Joseph Biden and Donald Trump on Spanish language Facebook ads found that while Trump was leading in spending until a few weeks before the election, Biden’s spending skyrocketed as the election neared, eventually surpassing Trump’s lead. As Hispanic voters were recognized to be a crucial voting bloc in the 2020 presidential election, especially in swing states, we wanted to continue researching the sorts of appeals that advertisers were using to target this community. How did Spanish language spending by the two candidates compare to other sponsors? What type of ads were run, and where were Spanish ads targeted?

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Many of our Delta Lab students presented their research in May 2021 at the Wesleyan Media Project’s Political Advertising Workshop. Others conducted research over the summer and presented posters. Summaries of these projects are shared below.

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By Natalie Appel

Although Facebook requires sponsors of ads about politics, elections, and social issues to disclose who paid for the advertisement, how much was spent, and who was targeted, there have been a number of instances in which Facebook has failed to recognize and label these advertisements. A study by digital experts at NYU found that Facebook failed to label and identify 9.7% of ads relating to elections and other social issues between May 2018 and June 2019, representing a total spend of 37 million dollars (Silverman). Another analysis of Facebook’s Ad Library found that in many instances Facebook has grossly overreported or underreported spending, resulting in unexplained spikes in cumulative advertiser spend or the number of total ads. Since there has been a lot of discussion surrounding both the accuracy of Facebook’s data and its failure to disclose who paid for all of the political advertisements on the platform, I was curious to see if Google, another major platform for political advertisements, was having similar problems reporting all the ads related to politics and the election.

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