In most discussions about misinformation, we tend to focus on what is actively false. We look for fabricated claims, manipulated narratives, or content designed to mislead. The assumption underlying this approach is that the problem is always too much wrong information circulating too quickly. But there is another dynamic that is far less visible, and arguably just as important.

Sometimes misinformation does not rise to the surface because it is louder or more persuasive. It rises because, in that moment, there is very little else present to compete with it. This is where the concept of a data void becomes useful. It shifts our attention away from the idea that the internet is always overflowing with information, and instead asks a more uncomfortable question: what happens when there is not enough?

The term “data void” is used by researchers Golebiewski and Boyd to describe situations in which search queries return limited, low-quality, or easily manipulated results due to insufficient authoritative content on a topic (Golebiewski and Boyd, 2018). In simple terms, it refers to gaps in the informational landscape, moments when the internet is expected to have answers but offers only fragments. It is important to pause here and clarify what this does not mean. A data void does not imply that a topic is unimportant or that knowledge does not exist. Rather, it highlights something more structural: that knowledge may not yet exist in a form that is visible, searchable, or indexed in ways that digital systems can easily surface.

This distinction matters because search engines do not operate as arbiters of truth. They do not verify claims in the way a researcher or editor might. Instead, they organise what already exists, prioritising relevance, popularity signals, and available indexed content. In that sense, search engines are less like librarians carefully curating verified knowledge, and more like mirrors reflecting the shape of what has been documented so far. When that documentation is thin, uneven, or absent, the reflection becomes distorted, not because the system is malfunctioning, but because it is working with limited material.

Data voids often emerge in moments where something is new, emerging, or not widely recorded. Some examples may include a developing political event, a niche social question, or a locally rooted issue that has not been extensively documented online. We can also see it in topics that exist primarily within oral, community-based, or multilingual knowledge systems that do not translate neatly into dominant search infrastructures.

In these moments, the internet behaves less like a complete archive of human knowledge and more like an abandoned library, with entire shelves missing books. The structure still gives the impression of order and completeness, but in certain sections, there is simply nothing to draw from. And when someone walks in searching for answers, those empty spaces do not remain empty for long. They get filled, but not always with accuracy.

This is where the risk emerges. When little authoritative content is available, whatever content exists gains disproportionate visibility. If the first available explanations are speculative, misleading, or intentionally manipulative, they can become highly visible simply by being the most accessible or the most optimised. In other words, visibility is not always a reflection of accuracy; it can also reflect timing and availability. And once something becomes visible first, it often becomes difficult to dislodge, because search systems are designed to reinforce patterns of engagement and relevance over time. The result is a subtle but important shift in how misinformation operates. It is no longer only about the spread of false content in a crowded information space. It is also about false content becoming the default answer in a space where alternatives are missing. This is why data voids matter so deeply in contemporary digital environments. They reveal that misinformation is not only a problem of excess but also of absence. And absence is harder to notice, because it does not announce itself. It shows up quietly, in the form of incomplete search results, thin explanations, or narrow perspectives that appear to be the whole story simply because nothing else is visible.

In African digital contexts, this becomes even more significant. Much of the knowledge that exists across communities, languages, and lived experiences is not always fully captured in structured digital formats. It may exist in conversation, local practice, vernacular expression, or offline archives that are not easily indexed. At the same time, search engines tend to privilege content that is widely distributed, frequently linked, and produced within dominant linguistic and institutional ecosystems.

This creates uneven terrain within the digital information landscape. Knowledge exists across communities, languages, lived experiences, and local histories, yet not all of it is equally visible within the systems people increasingly rely on to find answers. Some topics are extensively documented and easy to discover, while others appear only in fragments, scattered across sources that are difficult to find, connect, or surface in search results. As these visibility differences grow, gaps begin to emerge in the searchable record, creating conditions for data voids to form. Understanding data voids requires looking beyond individual pieces of misleading content and paying attention to the broader information environment. The challenge is not only what people encounter online but also what remains out of view. The shape of an information ecosystem is determined as much by what is visible as by what is missing, overlooked, or insufficiently documented.

Seen through this lens, digital literacy becomes more than the ability to separate true information from false information. It also involves recognising when a picture may be incomplete, asking whose perspectives are represented, and considering what knowledge exists beyond the boundaries of what is easily searchable. In a world where visibility often influences credibility, understanding absence becomes just as important as evaluating presence. After all, influence does not always come from the loudest voice. Sometimes it emerges from a lack of alternatives. When one explanation stands largely alone, visibility can be mistaken for authority, and familiarity can begin to feel like evidence. In those moments, absence is no longer simply a gap in the record. It becomes part of the process through which understanding is shaped.

References

Golebiewski, M. and boyd, d. (2018) Data Voids: Where Missing Data Can Easily Be Exploited. Data & Society Research Institute.

Wardle, C. and Derakhshan, H. (2017) Information Disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe.