Visual projects

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By Miriam Redi

Wikimedia Foundation, the non-profit organization that runs Wikipedia, has launched a campaign called Wiki Unseen. The campaign gives visual representation to the biographies of Black, Indigenous and People of Color on Wikimedia projects. Recent analysis by the Wikimedia Foundation shows that almost half of English Wikipedia articles lack images. These figures vary according to the language editions. For most Wikipedias, between 40% and 60% of articles are illustrated. In all languages, shorter articles, where non-textual knowledge can be very useful to supplement the lack of content, are unfortunately the most affected by the lack of images.

But why is it so important to have images on Wikipedia? Research tells us that images can play a very important role in encyclopedic knowledge.

Visual representation can help reduce gender gaps and break down stereotypes: Exposure to biased media literacy can reinforce existing demographic gaps and biases. Conversely, interventions aimed at exposing subjects to counter-stereotypical role models can have a positive influence on reducing biased judgments. For example, studies have shown that exposure to biographies of women in leadership positions reduces gender bias. Wikipedia can play a key role here.

Biographies make up about 30% of Wikipedia articles, and this knowledge is accessible to billions of people. Wikimedia communities are aware of existing knowledge gaps in Wikimedia projects and are organized to rebalance the presence of textual content for different genders or ethnic groups. Unfortunately, there is still a lot to do, as more than 80% of biographies on Wikipedia are about men. Also, Wikipedia is missing a lot of pictures, and her biographies, especially biographies of women, too. Our recent study shows that in English Wikipedia, only about 40% of biographies are illustrated. Wiki Unseen and other Wikimedia Foundation projects such as WikProject Women in Red in English Wikipedia or Wikiproject Daughters of Hind in Hindi Wikipedia, strive to get more biographies of women in well-organized languages.

Images are attractive to our readers: According to a recent study by the Wikimedia Foundation on reader interaction with images, images are attractive to our readers. We found that, on English Wikipedia, users click on images 1 in 30 times to read an article. That might not seem like a huge number, until you put it into perspective. On the English Wikipedia, citations are only clicked on 1 in 350 times, while external links are only clicked on for 1 in 110 page views. Thus, Wikipedia users tend to interact much more frequently with images than with the other interactive parts of the page!

The study showed that images with a higher click-through rate are of three types. First, readers click more frequently on images of complex objects, such as visual arts, or vehicles. Readers also use images on Wikipedia to travel the world, interacting with photos of landmarks and maps of different countries. Finally, biographies: unlike users of other web platforms, such as social media or image search engines, for Wikipedia readers, images of popular people seem to be far less engaging than other topics. However, readers interact with portraits of less popular articles, that is, when the subject of the biography is not widely known.

Images can help satisfy our information needs: Cognitive scientists have observed that, in most cases, images are more memorable than words and that our visual cortex processes and recognizes images within fractions of a second. But what are the images for? In our research, we found that images on Wikipedia could have a cognitive function: that is, they can facilitate the understanding of textual content by providing additional or clearer information.

We observed that readers interact with images more often in shorter articles, likely to supplement the lack of knowledge in articles where traditional textual content is absent. This is especially important for Wiki Unseen because most biographies also don’t contain much information about the people who have been profiled. By obtaining a visual representation of these images, the project has done much to fill this knowledge gap. This research, learning through Wikipedia, is a first step towards understanding how readers use images on Wikipedia.

Images can be reused on different pages and projects: One of the reasons why Wiki Unseen biographies lacked visual representation was because there were no freely licensed images on the Internet of these people that could be used on Wikipedia. An image added to an article on Wikipedia is not an isolated contribution. It’s a gift to the greatest source of visual encyclopedic knowledge on the web. Images from the Wikimedia network are freely shared on the web and reused by anyone who needs them, free of charge. A single image may be used, on Wikipedia, as visual support for more than one concept, in more than one language, and beyond Wikipedia, in other Wikimedia projects. For example, a recent study found that images of paintings are widely used to illustrate not only articles about visual arts, but also very popular articles about places, historical events, and people, as alternatives to photographs.

But adding images to Wikipedia can also have a positive domino effect on visual gaps. Algorithms and tools designed to fill visual knowledge gaps also use the presence of images in articles as a signal to discover new matches between images and text. For example, our image recommendation algorithm, the new MediaSearch image search engine, and many community initiatives that gamify adding images to Wikidata, use existing image-to-article links to discover new image matches for natural language queries, non-image articles, and Wikidata items.

Are you ready to add images to Wikipedia yourself? You can join many initiatives! Take part in the Wiki Unseen campaign by drawing images for unillustrated articles.

Miriam Redi is Research Director at the Wikimedia Foundation and Visiting Scholar at King’s College London. Previously, she worked as a research scientist at Yahoo Labs in Barcelona and Nokia Bell Labs in Cambridge. She obtained her doctorate at EURECOM, Sophia Antipolis. She conducts research in social media computing, working on fair, interpretable and multimodal machine learning solutions to improve knowledge fairness.

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