IP 3- Algorithm


“At a time when state funding for public goods such as universities, schools, libraries, archives, and other important memory institutions is in decline in the US, private corporations are providing products, services and financing on their behalf. With these trade-offs comes an exercising of greater control over the information, which is deeply consequential for those already systematically oppressed…” (Noble, 2018, p. 123 )

Explain in your own words what “content prioritization” (Noble, p. 156) means (give some examples) and how (in lay terms) content prioritization algorithms work. With control over the “largest digital repository in the world” (Noble, p. 157), how have Google’s content prioritization algorithms been “consequential for those already systematically oppressed”?

Content prioritization is a ranking algorithm that determines the order of content appearing in search engines such as Google. The prioritization is influenced by multiple factors, depending on the system adopted by the company. For example, Google states on their website that search results are determined by the meaning of the search query, the relevance of content, quality, usability, and context. (“Ranking Results – How Google Search Works,” n.d.) For instance, when I search for “Academic Integrity” using Google, web pages on academic integrity from major Canadian higher education institutions appear at the top. This happens because, through my IP address, Google can identify that I am located in Canada (“How Google Uses Location Information – Privacy & Terms – Google,” n.d.), leading the algorithm to conclude that resources from major universities would be most useful to me. Bing also follows a similar ranking criteria to Google, which can be accessed from their page.


However, as Noble has highlighted, the content ranking prioritization algorithm can have significant consequences for “those already systematically oppressed” (Noble, 2018, p. 121). Revisiting the earlier example, this algorithm tends to further benefit larger and more prominent higher education institutions, potentially disadvantage smaller educational institutions. Google defines one of the criteria for “Quality” as whether “prominent websites link or refer to the content” (“Ranking Results – How Google Search Works,” n.d.), indicating that resources linked by numerous websites are more likely to be ranked higher. Larger institutions, with more internal subunits and websites, usually link to their own resources, thereby gaining an advantage. These major institutions also often have connections with other institutions, leading to more referrals. This system essentially empowers institutions that are already powerful.

How do content algorithm prioritization impact your professional life? (give specific examples and briefly discuss)

At my workplace, I utilize Google Analytics to analyze the web traffic of the online resources we create for the department. When examining the analytics for the educational resource website I was involved in 2023, I found that 55% of the users discovered the resource through Google. Based on the result, my team members and I explored strategies to learn from web page with high traffic. We analyzed which types of web page achieve higher page rankings and how to enhance the page ranking for web page that had lower ranking. 


Analyzing the analytics not only made me think about the importance of PageRank but also prompted me to reflect on the broader implications of content prioritization, especially considering our position as a larger institution and how we can ethically share the resources we create at work. One strategy my team has adopted is licensing our work under a Creative Commons License, which allows other institutions to reuse and adapt the resources with attribution, without needing to ask for permission. By applying this license, other institutions can freely reuse and adapt the resources. This approach enables institutions with limited capacity and access to resources to adapt and create new materials. I believe this strategy can help combat the inherent biases in search engine page rankings, which often favor content from larger institutions.

What are some ways PageRank impacts your personal life? (specific examples and briefly discuss) (How) can you impact PageRank? Explain.

I find page ranks unconsciously impact a lot of my behavior in personal life unconsciously. For example,I use search engines to find where I can purchase a product (such as a bluetooth microphone). I typically only look at the first and 2nd result of the search result, and not the rest. This is proved in the study by Pan et al, which shows that users are likely to be strongly biased towards links in higher rank even if the abstract is not relevant  (Pan et al., 2007). 

As I reflect, I have noticed that major companies like Amazon and Walmart consistently appear as the top search results, while smaller retailers or local stores receive less visibility. This has raised concerns about whether algorithms may influence consumer behavior, potentially contributing to monopolistic practices. For instance, companies like Amazon benefit from referral or affiliate programs that offer monetary incentives to web developers or bloggers for referring products to their platform. This linking and referral process can further improve the “quality” of the web page, as defined by Google’s ranking criteria, thereby boosting the page rank.

As for how I can impact PageRank, I have found that contributing to online repositories or communities can have an impact on page rank. I write reviews for small businesses that I like on platforms such as Google Reviews and Yelp (a food review website). These reviews can make small businesses more visible and positively impact their PageRank, rather than allowing large businesses to continue exerting their monopoly power.


Another activity that can help improve PageRank is participating in edit-a-thons to enhance the visibility and address the information gaps of marginalized communities on Wikipedia.Initiatives such as the Art + Feminism Wikipedia Edit-a-thon and the Honoring Indigenous Writers Edit-a-thon are examples of such efforts. As  Noble has indicated, the keywords about minority or marginalized end up with search results that are not true or credible, which surfaces the culture of implicit bias that exists against minority groups (Noble,2018, p. 121). This observation resonated with my experiences participating in the Honoring Indigenous Writers Edit-a-thon in 2019 and 2020. During these events, I encountered difficulties in locating credible sources on search engines for Indigenous authors, a challenge not as pronounced for English-speaking Western authors. A study by Vincent & Hecht reveals that Wikipedia articles are displayed in 80-84% of search results for commonly used search keywords. (Vincent & Hecht, 2021, p. 8) Therefore, creating and enhancing Wikipedia articles related to marginalized communities can be an effective strategy to increase visibility and accuracy of information, which can help with reducing bias in search engine results.

References

About CC Licenses. (n.d.). Creative Commons. Retrieved February 4, 2024, from https://creativecommons.org/share-your-work/cclicenses/

Art + Feminism. (n.d.). Art + Feminism. Retrieved February 4, 2024, from https://artandfeminism.org/about/

How Bing delivers search results—Microsoft Support. (n.d.). Retrieved February 3, 2024, from https://support.microsoft.com/en-us/topic/how-bing-delivers-search-results-d18fc815-ac37-4723-bc67-9229ce3eb6a3

How Google uses location information – Privacy & terms – Google. (n.d.). Retrieved February 4, 2024, from https://policies.google.com/technologies/location-data?hl=en-US

Ohaegbu, C. (2019, March 6). Indigenous Writers Edit-a-Thon creates online change through Wikipedia edits. The Ubyssey. https://ubyssey.ca/culture/Indigenous-Writers-Edit-a-Thon-wikipedia/

Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In Google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12(3), 801–823. https://doi.org/10.1111/j.1083-6101.2007.00351.x

Rainie, K. P., Joanna Brenner and Lee. (2012, March 9). Search engine use 2012. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2012/03/09/search-engine-use-2012/

Ranking results – How Google search works. (n.d.). Google Search – Discover How Google Search Works. Retrieved February 1, 2024, from https://www.google.com/search/howsearchworks/how-search-works/ranking-results/

Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender systems: Techniques, applications, and challenges. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 1–35). Springer US. https://doi.org/10.1007/978-1-0716-2197-4_1

Safiya Umoja Noble. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=nlebk&AN=1497317&site=ehost-live&scope=site&custid=s5672194

Search Engine Market Share Canada. (n.d.). StatCounter global stats. Retrieved February 4, 2024, from https://gs.statcounter.com/search-engine-market-share/all/canada/2023

Vincent, N., & Hecht, B. (2021). A deeper investigation of the importance of Wikipedia links to search engine results. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 4:1-4:15. https://doi.org/10.1145/3449078

This work by Rie Namba and Duncan Hamilton is licensed under CC BY 4.0