DIGIT

Illustrative story

In the past years, TikTok has taken the world by storm. Primarily due to its addictive short-form video format and its algorithm. The algorithm analyses user behaviour, interactions, and preferences to tailor the content feed to each individual’s interest. This personalized approach ensures that users are consistently served with videos that align with their tastes, leading them deeper into a highly curated world of specific content.

For the purpose of this article, we conducted an experiment by creating a new TikTok account and deliberately engaging with 25 pro-life abortion videos. As a result, we observed a significant trend in the content recommendations. Out of the subsequent 25 videos that were suggested to us, 17 had a pro-life agenda and only 3 had a pro-choice agenda.

This experiment indicates that the algorithm has no incentive to propose viewpoints that are in opposition to the user preference

Why?

n today's digital age, content providers, especially those in social media, are motivated to keep users engaged on their platforms for longer periods to generate more ad revenue. To achieve this goal, recommendation algorithms are often utilized. These algorithms suggest content that a user is most likely to engage with, be it an article to read, a video to watch, or a podcast to listen to.​

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However, this approach has its drawbacks. Users are often presented with content that aligns with their existing interests, resulting in them being trapped in their own comfort zone. For instance, a manga fan will be bombarded with manga-related content, and a soccer fan will be fed soccer-related content. While this seems harmless, things can take a negative turn for someone who is a conspiracy theorist or a far-right political supporter. They will be bombarded with content that confirms their beliefs.​

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On social media platforms, individuals can post content without being subjected to an objectivity check. However, if someone relies solely on social media as their primary source of information, they may fall prey to confirmation bias. As they may only consume content that confirms their pre-existing beliefs. Such behaviour is leading to an increasingly polarized society, particularly since the audiences of state-owned national "objective" news programs have been on the decline.​

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If we lived in a world where customized messages were ubiquitous in all aspects of our society, then each individual would potentially live in their own subjective version of reality.​

How to mitigate?

1. Diversify X% of recommendations: Recommendation algorithms should aim to provide users with a diverse range of content, including viewpoints that challenge their existing beliefs. Additionally, this can increase viewership by identifying new content categories that interest the user.​

2. Upscale societal data literacy: Our public education system should teach the wide public to think critically about the content they are presented with and to question the validity of the sources.​

3. Provide context: Recommendation algorithms should give objective context about the source, objectivity, author, political agenda, etc. of a recommendation​

4. Use human oversight: Human oversight can ensure that recommendations are fair accurate, and diverse​

By diversifying recommendations, encouraging critical thinking, providing context, and using human oversight, recommendation algorithms can be used in a responsible and effective manner that avoids the polarization of society.​