Image and Video Facial Recognition and Transcription and Building a Classifier with Snapchat Political Advertisements Data

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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BLM in the Battleground: An Analysis of Racial Justice-Related Ads in Georgia

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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Inside the Black Box: Examining Possible Sources of Classification Bias in Facebook Political Advertisements

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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Reproductive, Radical, Religious: Partisan Differences in Abortion-Related Facebook Political Ads

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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Automated Issue Classification of Political Advertisements on Facebook

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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Spanish language advertising by prominent groups in the 2020 presidential election

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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Assessing Discrepancies in Reported Advertiser Spend by Google

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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Trump’s Lead in Spending on Spanish Facebook Ads was Surpassed by Biden’s Last Minute Push

By Angela Loyola

According to the NYT and several other news outlets, as the election neared, polling in battleground states like Texas and Florida indicated that Biden was not leading Trump with the Latino vote in every case. And even in the cases when he did hold a lead, it was by a much smaller margin than Clinton did against Trump in 2016. 

This was significant because as the Latino electorate has grown in the US, especially in key battleground states, both parties have been eager to target Latino voters in their ads to garner their support. I wanted to conduct research this semester that would allow me to dig into these ads for the presidential race. While this could include a variety of different methods, I chose to investigate how each candidate might be targeting Latino voters through Facebook advertising in Spanish.

Data used in this analysis came directly from the Wesleyan Media Project, which tracks advertising through Facebook’s Ad Library API tool and Ad Library Report.

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Presidential advertising by candidates reveals microtargeting tactics on Snapchat

By Spencer Dean ’21

Introduction

The booming market for political advertisements on digital platforms remains dominated by two major players, Facebook and Google. Together, Donald Trump and Joe Biden have spent nearly $300 million on ads run on these platforms since mid-April. However, a new player may be emerging in social media app Snapchat, which boasts an audience of 249 million daily active users and has shown significant levels of political advertisement activity. 

Snap Inc., the parent company behind Snapchat, recently made waves in the stock market after posting record earnings in the third quarter. Analysts attributed this growth to both a surge in average ad prices, up 20 percent in the past year, and an increased demand from advertisers. 

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