Content prioritization is the ranking of items (content), such as search results, based on their perceived importance by a computer program, which will then display recommended results to the user. Depending on the type of content and the software, content is prioritized differently and can use a combination of algorithms and recommender systems. For example, when using Google Search, the content that Google Search determines to be the most relevant to the user will be displayed first and has priority over others. The actual relevance to the user depends on the algorithm used and can vary dramatically based on numerous factors such as user data, the term(s) being searched, the location of the user, and external influences such as paid advertising. Another example of content prioritization would be online shopping platforms such as Amazon, which shows users certain products over others based on its algorithm, data collected from the user, and the type of advertising the selling has decided to buy. On Amazon, content prioritization can occur multiple times when purchasing an item and begins at the display of search results, suggestions for similar items when viewing an item, then suggestions for items frequently bought together during check-out. Each of these stages allow Amazon to collect more data about the user and allows it to prioritize the content that allows Amazon (and its advertisers) to profit the most.

One example of a content prioritization algorithm is PageRank used by Google Search. It works by ranking content based on the number of times a webpage gets hyperlinked which supposedly determines the relative importance of a webpage. If a page is linked from another page with a high PageRank, then the linked page will also receive more votes for its PageRank. In order words, the more clicks and more websites refer to a certain webpage, the higher its ranking. Also, if a webpage linked by many websites deemed reputable by PageRank, then the webpage also becomes reputable.

From the PageRank example, the prioritization of already popular websites is problematic as it creates a positive feedback loop and can create biases against minorities in the search engine’s userbase. The positive feedback loop allows larger entities with more marketing budget to advertise directly with Google Search or indirectly on other webpages to include a link and redirect users. A corporation with more money can also hire people to manipulate the algorithm or work it in their favour to help generate more traffic and boost a webpage’s PageRank. Compounding to the problem, if Google has the “largest digital repository in the world”, then users will believe (or already believe) the results returned by Google Search are the most relevant and factual, when really, they are only the most popular based on the algorithm criteria. If the algorithm has been trained or programmed by members of the majority and their opinions on the systematically oppressed, then the displayed results will portray the views of the oppressors, which reinforces their biased views. Algorithms can turn the internet into an echo chamber since the webpages of minorities will be suppressed for voicing unpopular opinions or for not having enough financial means to pay advertisers. The most popular articles are also often clickbait, or images or headlines that generate clicks for their shock value and are often misleading or false. One single private corporation with so much control over the dissemination of digital information is extremely dangerous as it has the power to shape the economic, political, and social environment of communities, cities, and even nations.

The impact of content prioritization on my professional life is unclear as the effects or methods are not always tangible, direct, or visible. As an ethnic minority with a Chinese last name, I might be at a disadvantage in job applications where an algorithm might filter out or give my resume lower priority for not being Caucasian. Algorithms and data collection also contribute to policies such as affirmative action that could have influenced my chances of being admitted to a post-secondary institute. Algorithms and content prioritization also affect me as an educator because school curriculums are adapting to the new ways students find information by including more critical thinking and new media studies.

When PageRank shows the mainstream media headlines as the top search results, my personal life can be affected since western media tend to have negative views on China. As a Chinese-Canadian, I might be associated with the Chinese Communist Party just because of my ethnicity, even though I share none of its views. The media uses sensationalized headlines and fear to grab the attention of readers which will result in more views or clicks, which increases a webpage’s PageRank, generating more revenue for the media and Google. Most of the times, readers will only read the headlines, especially given how most newspaper websites are behind paywalls now. Some news articles will not fact-check, due to the algorithm rewarding those who publish the fastest.

I believe I can impact PageRank by becoming vocal and actively speaking out against misrepresentation in ways such as emailing PageRank programmers, writing a blog, making a video, or publishing research. As an educator, I can teach students how PageRank works and to be cognizant of algorithmic biases. My role as a teacher is shifting from the source of all content knowledge to a facilitator of classroom discussions who can teach students how to properly find relevant information by recognizing and avoiding biases.