Prioritizing Wikipedia’s Knowledge Gaps

Within the ecosystem of Wikipedia, knowledge is constantly being built, refined, and made accessible to a global audience. This blog series began in the post, An Overview of Wikipedia’s Structural Framework where we explored the fundamental characteristics that define Wikipedia’s content: its dynamic, factual, verifiable, collectively significant, and neutral, all presented in a digital format that is both meaningful and accessible to humans and machines alike. 

In the second post of the series, Navigating Wikipedia’s Knowledge Discrepancies, we examined the structure of Wikipedia’s knowledge. We unpacked the anatomy of a Wikipedia article and highlighted how wikitext, multimedia, structured data, categories, internal links, and templates create a rich, interconnected web of information. We also introduced the concept of knowledge discrepancies: the inherent gaps and imperfections that inevitably arise within this vast, community-driven project. We classify these discrepancies into broad categories such as missing content, poorly structured content, surplus content, and cataloging mismatches.

Now that we’ve established what constitutes Wikipedia’s knowledge and the types of discrepancies it contains, we arrive wondering: Which of these knowledge gaps/discrepancies should we tackle first, and how do we prioritize them? In the remainder of this article, we use the term “knowledge gaps” synonymously with “knowledge discrepancies.” 

Maximizing Impact: Urgency & Relevance

Prioritizing knowledge gaps effectively ensures that the efforts of editors and community initiatives are directed towards the most impactful tasks. To guide us in prioritization, we introduce two fundamental concepts: Knowledge Urgency and Knowledge Relevance. These concepts provide a framework for evaluating the critical importance of a given knowledge gap.

Knowledge Urgency refers to content that demands immediate attention and rapid updates, typically in response to breaking news or other time-sensitive current events. These are discrepancies where the timeliness of information directly impacts its utility and accuracy for readers. For example, an article about the 2024 United States Presidential Election requires rapid and continuous updates as events unfold. Similarly, information related to unfolding natural disasters or public health crises need to be integrated into relevant articles as quickly as possible to keep Wikipedia current and accurate. Existing community mechanisms such as article watchlists (where editors track changes to specific pages) and dedicated task forces (where editors collaborate to improve articles related to a specific theme of current importance) often help monitor these types of urgent gaps, but a more systematic prioritization is often needed.

In contrast to urgency, Knowledge Relevance focuses on the broader significance of certain topics. These are gaps that, even if not immediately time-sensitive, hold long-term importance due to their profound and lasting impact. Consider topics like climate change, which has pervasive global implications for the environment, public health, and economies. Knowledge gaps within such foundational articles remain critical regardless of whether there’s breaking news directly related to them. Measuring the relevance of a knowledge gap often requires nuanced, subjective judgment, often informed by comparisons with external reliable sources and data analysis.

A crucial distinction must be made: the overall importance of an article does not always directly correspond to the importance of a specific knowledge gap within that article. For instance, the Wikipedia article on stroke might be deemed of higher overall importance than the one on scleroderma due to its broader prevalence and societal impact. However, within the article about scleroderma, highlighting the clear female predominance in the disease’s occurrence might be a far more critical knowledge gap to fill, as it could impact the public understanding of the condition. Conversely, a historical anecdote about Martin Luther’s stroke, while interesting, might be a less critical gap to fill in the stroke article, even if the article itself is highly important.

Therefore, what truly matters is the relevance of the knowledge gap itself in the context of the reader’s needs and the topic’s overall integrity. The focus should be on filling the most critical gaps for readers, rather than merely prioritizing the content based on the overall “importance” of the article in which the gap resides.

Current State of Prioritization Efforts

At the time of writing, the Wikipedia community has made significant strides in prioritizing articles. This ongoing research into article importance, community efforts to build vital article lists (articles considered essential for a complete encyclopedia), the application of machine learning tools like ORES (Objective Revision Evaluation Service) for giving a completeness score to existing articles, and widespread community efforts for manual assessment of article quality based on editorial validation. There is certainly no shortage of topics on lists of missing encyclopedic topics and most wanted articles; indeed, an entire WikiProject is dedicated to identifying missing encyclopedic articles.

However, there is a notable absence of systematic, large-scale efforts specifically designed for prioritizing the knowledge gaps within articles, outside the broader context of overall article prioritization. This means while we know what articles are missing or which articles are “good” or “bad” overall, a granular system for prioritizing specific knowledge gaps within existing articles is still an evolving area.

Addressing Knowledge Gap Prioritization

To address knowledge gaps more effectively and systematically, Wikipedia could benefit from implementing an integrated system that helps editors identify and prioritize tasks based on a comprehensive understanding of both urgency and relevance. Such a system could incorporate several key factors:

• Dynamic Task Prioritization Based on Urgency and Relevance: We conceptualize a sophisticated system that would analyze both internal Wikipedia metrics (e.g., page views, edit frequency) and external sources (e.g., recent news highlights, trending search queries, global event calendars) to dynamically flag urgent content gaps. For example, articles related to upcoming elections, unfolding health crises, or significant geopolitical events would automatically receive immediate attention. Quicksilver, a tool that identifies missing notable biographies of scientists based on notability and news recency, offers a glimpse into the potential of such a system. An ideal iteration would be language-agnostic, capable of delivering prioritized results in any of Wikipedia’s over 300 languages.

• Topic Buckets and Personalized Recommendation: To match editors with tasks that align to their individual interests and expertise, knowledge gaps could be categorized into granular “topic buckets” and further subdivided into specific task lists (e.g., “Climate Change” bucket with “copyediting,” “missing content,” or “surplus content” task lists). This would allow editors to easily discover tasks that resonate with their passion. Tools like PetScan already facilitate the creation of custom lists based on various criteria (example), such as articles with missing sections under a specific WikiProject’s scope. Furthermore, the system could leverage an editor’s past contributions or self-declared interests to recommend tasks, acting as a personalized “to-do list.” For example, if an editor frequently corrects grammatical errors in environmental topics, the system could suggest high-priority copyediting tasks related to climate change. This involves building a sophisticated, multi-tiered recommendation system that accurately personalizes tasks based on an editor’s demonstrated expertise level. While some academic research on personalized article recommendations exists, to our knowledge, a fully implemented system has yet to be deployed on Wikipedia.
  
• Real-Time Collaboration Alerts: Fostering collaboration is vital for a volunteer-driven project like Wikipedia. A system could notify editors when large-scale collaborative efforts are underway (such as an online edit-a-thon focused on a specific topic like climate change) or when a particular topic is receiving increased attention (as indicated by increased article traffic or external media coverage). This would help editors stay engaged with projects that align with their expertise and allow for more coordinated efforts to tackle large gaps. However, a significant hurdle is the current decentralized reporting of collaborative events. While initiatives like Celebrate Women are encouraging event organizers to report all gender diversity-focused events on a single page, without a centralized system for indexing and reporting collaborative events, we remain far from the future where we can provide real-time notifications about new events.

Wrapping Up

Effectively prioritizing knowledge gaps on Wikipedia is an endeavor that requires a nuanced understanding of both the urgency and relevance of topics. A robust system combining dynamic task prioritization, personalized recommendations, and real-time collaboration alerts could transform how editors engage with critical knowledge gaps. By aligning tasks with editors’ interests and expertise and providing timely updates based on current events and ongoing projects, Wikipedia can make the task of knowledge building more enjoyable, efficient, and impactful.

In our upcoming blog post, we will build directly on this understanding by exploring the next step: the diverse methods, tools, and community workflows that the Wikipedia ecosystem employs to systematically find and flag these knowledge discrepancies, making them visible and ready for editors to work on. Stay tuned!