Pulling Back the Curtain on Personalization Algorithms

Algorithms are everywhere. They power social media sites like Facebook, search engines like Google and increasingly digital news outlets that want to show users more of what they like and less of what they don’t.

Algorithms rarely get attention, except for when they are in the news for allegedly having a left-leaning bias or other (usually negative) reasons. But they should be a regular topic of conversation in college classrooms. As an article I wrote for Digital Journalism argues:

Personalization algorithms, widely used by digital media sources, filter and prioritize news in ways that may be unapparent to users. Savvy media consumers should be aware of how this technology is used to tailor news to their tastes. This two-part study examines the extent to which U.S. college students are aware of news personalization, and the actions and criteria that affect news selection and prioritization. Interviews with one set of students (n=37) focus on the news sources they use most often to begin a news search. A subsequent survey given to a second set of students (n=147) focuses on Google and Facebook, two influential gatekeepers. Results show that students are largely unaware of whether and how news sources track user data and apply editorial judgments to deliver personalized results. These studies identify aspects of news personalization that warrant greater attention in college curricula.

I presented my research, boiled down big time in poster form below, at the 2016 AEJMC conference in Minneapolis.

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Here’s my elevator pitch (OK, it’s a long ride — maybe the Empire State Building?) for why this is such an important topic:

Endless streams of information have become so central to the ways in which news is consumed that it is tempting to assume that content is selected and ranked based upon some universal standard of editorial importance – or to ignore how these decisions get made altogether. To be savvy media consumers in the digital age, students need to understand the implications of news personalization and how the sites they most commonly rely upon for news and information use their data – and data from millions of other users – to filter the news they consume. While past research and news reports have focused on identifying some of the ways in which news is personalized on sites such as Facebook and Google, little attention has been paid to the extent to which users are aware of the concept of personalization and the specific types of user data that are tracked by personalization algorithms.

This exploratory, two-part study attempts to fill that void. Taken together, these studies shed light on what students know and do not know about news personalization and how educators can tailor lessons about this topic to areas about which students are least informed.

The headline from the study is that students know very little about news personalization. While this is troubling, it’s also an opening for journalism educators to find creative ways to cover this topic in class.

This was also the topic of discussion during a four-hour pre-conference workshop that I organized at the conference. Here’s the official program information:

1 p.m. to 5 p.m.
(w6) Teaching Algorithmic Transparency
$10 Fee
Teaching college students about the power of algorithms should be a central component to mass communication. This panel session will provide educators with an overview of algorithmic transparency and advice on how to teach these concepts. For additional information contact Jennifer Kowalewski, Georgia Southern University, (912) 478-0126 or at jkowalewski@georgiasouthern.edu (MCSD)