Content prioritization means that various Internet platforms use computer algorithms to provide users with content that may be of their interest. For example, after using Google Chrome to browse news on a certain topic, Google Chrome will continue to recommend news content of a similar topic to the user so that the user can preferentially browse news content of a similar theme. For another example, the Alibaba shopping website will obtain the user’s consumption preference information after the user has browsed a particular product and use computer algorithms to recommend many similar products to the user for them to choose. The use of content prioritization technology on the Internet limits users’ reception of information, making the information obtained by users more and more restricted to their existing preferences or cognition, thus limiting users’ broader awareness of the world.
Prioritization is a convention, as content with higher priority is processed first, and content with lower priority is processed after. Contents are prioritized according to specific requirements before being put into the algorithm. When the computer processes multiple contents, it will decide the order of processing according to the priority of each content. It even allows higher priority tasks to interrupt a lower priority task to ensure that tasks will be processed in priority order.
The operation of the content prioritization algorithm requires the input of user’s preferred content, which is the “digital footprint left by the user” when browsing various digital media platforms (Noble, p. 156, 2018). These digital footprints may include time, geographic location, past search results, and clicks tracked through websites and advertisements, including cookies stored on devices or other hardware (Noble, p. 187, 2018). Various Internet platforms or hardware analyze users’ preferences by tracking users’ digital footprint, thereby obtaining various data about user preferences. For example, as the world’s largest search engine company, Google has a massive users’ digital footprint and has formed the world’s largest digital repository (Google, 2019). By managing this digital repository, Google can keep track of each user’s preferences. With content prioritization algorithms, Google can precisely serve users’ content based on their historical preferences and keep them continuously browsing similar content, thus leading them to repeat past thinking patterns and behaviors. In this way, the user’s cognition is further limited to the past, continuously deepening minimal cognition development without knowing it.
I work as an analyst in a private equity fund company. My job is to conduct in-depth research on specific industries or companies, accurately define the market status and trends, and forecast the development prospects, as well as discover investment opportunities or business opportunities. Therefore, collecting raw data, news, and reports from the Internet’s public domain is part of my work routine. Unfortunately, during the collecting process, the content prioritization algorithm limits the spectrum of information recommended, destroying the possibility of obtaining broader information. For example, in the process of online industry research, it is difficult for me to find high-quality content that I could use for generating creditable analysis because of the imbalance of the algorithm.
PageRank is also an algorithm that impacts people’s daily life. Baidu, as the dominant search engine in China, triggered a nationwide protest against Baidu called “get away from Baidu” in 2016 and caused long-lasting trust issues (Business & Human Rights Resource Centre, 2016) because its PageRanking algorithm was too biased in favour of ad placements. The root of this campaign was the news of a college student named Zexi, who searched for his health problems through Baidu and finally chose a commercial hospital for treatment but died from the disease after improper surgery. The reporter of Zexi’s death found that this commercial hospital had many historical medical malpractices, but its scholarly articles about Zexi’s illness still appeared in the first few pages of Baidu’s search results. The news media thus realized that there were serious errors with Baidu’s PageRanks of information and began to suspect that it was because the hospital had spent a lot of money on advertising with Baidu. The public understands the importance of advertisers to search engines and acquiesces advertisers’ links will be recommended at the top when searching for products and services. However, this does not mean that search engines can increase the PageRank of advertisers’ scholarly articles and false advertising, especially for the health and medicine sector. For most people who use search engines every day, their algorithms are like black boxes which can not be seen or evaluated. But because we live in an era of information explosion, we have to heavily rely on search engines to get a sorted recommended reading list of information. Many people, even for such a serious and professional issue as disease, may only read the first 100 web pages among 1 million search results. Therefore, the correctness and fairness of the algorithm and the search engine companies’ corporate responsibility are crucial to us.
Reference:
Business & Human Rights Resource Centre. (2016, May 10). China: Student with cancer died after receiving unqualified treatment led by paid-ads on Baidu, criticisms push company to change culture. https://www.business-humanrights.org/en/latest-news/china-student-with-cancer-died-after-receiving-unqualified-treatment-led-by-paid-ads-on-baidu-criticisms-push-company-to-change-culture/
Google. (2019, October 24). How Google Search Works. https://www.youtube.com/watch?v=0eKVizvYSUQ
Noble, S. U. (2018). Algorithms of oppression. New York University Press.
Wikipedia. (2022). PageRank. https://en.wikipedia.org/wiki/PageRank