Improving Content Equity on Peer Production Platforms through Recommender Systems

Core Concepts

Recommender systems can be leveraged to guide editors towards underrepresented topics on peer production platforms without significantly reducing overall engagement.

Abstract

The article explores the use of recommender systems to address content gaps on peer production platforms like Wikipedia. It presents two empirical studies on the SuggestBot recommender system for Wikipedia. Study 1 (Observational): Analyzed SuggestBot recommendations in 2021 to establish a baseline understanding of how editing behavior is impacted by factors relevant to content gaps. Found that editors were more likely to edit biographies of women than biographies of men. Study 2 (Controlled Experiment): Conducted a 3-month controlled experiment on SuggestBot, where a subset of recommendations were replaced with the most relevant articles from underrepresented categories (gender, geography, important topics). The alternative recommendations did not suffer from any significant decreases in uptake, and providing a higher number of recommendations from underrepresented categories substantially increased the share of recommendation-prompted editing on those articles. The article discusses how recommender systems that rely solely on edit history can inadvertently magnify self-focus bias, as the article discovery process also plays a key role in determining what content editors are likely to engage with. It suggests that incorporating content equity considerations into recommender systems can help address content gaps on peer production platforms without significantly reducing overall engagement.

Stats

"Presenting editors with a greater share of female recommendations—41.7% female recommendations, instead of 22.3% and 23.0% in 2020 and 2021—also resulted in a greater share of editing being done on female biographies as compared with male biographies." "29.3% of edited recommendations pertained to the global south, compared with 22.0% in 2020 and 22.3% in 2021." "11.4% of edited recommendations in our study period pertained to important topics. This was, again, higher than the two previous years—7.2% in 2020 and 7.5% in 2021."

Quotes

"On one hand, I'm surprised it [Menstruation article] isn't here, but then as one of the x-deficient 90% of editors, I wouldn't have even thought to add it."

Key Insights Distilled From

Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms

by Mo Houtti,Is... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2307.08669.pdf

Deeper Inquiries

How might the findings of this study apply to other peer production platforms beyond Wikipedia?

The findings of this study can be applied to other peer production platforms by highlighting the potential of recommender systems to reduce content gaps. By modifying recommender systems to prioritize underrepresented topics, platforms can guide contributors towards addressing these gaps. The study's results suggest that presenting users with articles from underrepresented categories can increase the proportion of work done on those articles without significantly reducing overall recommendation uptake. This approach can be beneficial for platforms facing similar content disparities, such as gender, geographical, or topical biases. By incorporating diversity-focused recommendations, platforms can encourage contributors to engage with a more diverse range of content, ultimately improving the overall inclusivity and representation of the platform.

What are the potential drawbacks or unintended consequences of using recommender systems to address content gaps, and how can they be mitigated?

While using recommender systems to address content gaps can be beneficial, there are potential drawbacks and unintended consequences to consider. One drawback is the risk of narrowing recommendations based on users' past behaviors, which may reinforce existing biases or limit exposure to diverse content. This can lead to a "filter bubble" effect, where users are only exposed to a limited range of content that aligns with their preferences. To mitigate this, platforms can implement algorithms that balance personalization with diversity, ensuring that users are exposed to a variety of content while still receiving relevant recommendations. Another unintended consequence is the potential for decreased engagement if users perceive the recommendations as less relevant or if the system pushes them towards content they are not interested in. This could result in users editing less or disengaging from the platform altogether. To address this, platforms can provide users with control over their recommendations, allowing them to adjust settings or preferences to better align with their interests. Additionally, platforms can incorporate user feedback mechanisms to continuously improve the relevance and diversity of recommendations.

What other factors, beyond the article discovery process, might contribute to the persistence of content gaps on peer production platforms, and how can those be addressed?

Beyond the article discovery process, several other factors can contribute to the persistence of content gaps on peer production platforms. One factor is the composition of the editor community, as platforms may lack diversity in terms of demographics, expertise, or perspectives. This lack of diversity can influence the types of content that are created or prioritized, leading to gaps in representation. Platforms can address this by actively recruiting and supporting a more diverse community of contributors, providing training and resources to help them contribute to a wider range of topics. Another factor is the platform's governance and editorial policies, which may inadvertently perpetuate biases or hinder efforts to address content gaps. For example, editorial guidelines that prioritize certain types of content or topics over others can contribute to imbalances in representation. Platforms can review and revise their policies to ensure they are inclusive and supportive of diverse content creation. Additionally, platforms can implement measures to monitor and evaluate content gaps regularly, using data and analytics to identify areas for improvement and track progress towards greater inclusivity.