Do Social Media Algorithms Promote Mean and Politically Inflammatory Content?

Are social media algorithms designed to promote mean and politically inflammatory content? This is a topic of significant interest and concern, particularly as more people question the role of social media platforms in shaping public discourse. While the exact workings of these algorithms are often shrouded in mystery, there's no denying that these systems play a crucial role in what content users see.

The Role of Algorithms in Social Media: Profit Over Safety?

It's widely believed that the algorithms used by major social media platforms like YouTube and Facebook prioritize engagement over user safety. These algorithms are designed to keep users engaged by promoting content that keeps them scrolling or watching. This often results in the promotion of 'sticky' material, which can be defined as content that is emotionally charged, shocking, or controversial.

YouTube’s Experience with Negative Content

YouTube, in particular, has been at the forefront of this controversy. The platform's autoplay function has been accused of steering users towards extreme and politically inflammatory content. One notable case involved an engineer blowing the whistle, revealing that the algorithm was promoting extremist material and conspiracy theories. While YouTube has since taken steps to ban conspiracy theory content, the extent of the problem remains significant.

In a report from Naked Security, it was highlighted that YouTube's previous algorithms were not just promoting conspiracy theories but also inciting division. They were highly profitable but at the cost of societal well-being. This raises important questions about the ethical responsibility of these platform owners.

Impact of Algorithmic Filtering

The way algorithms filter content can lead to a self-reinforcing cycle where inflammatory content gets more visibility. Even if the algorithm doesn't have an inherent bias towards promoting mean or inflammatory content, it can learn from user behavior. For example, a post about Flat Earth theories could become more popular due to its controversy and divisive nature. This content then gets pushed to more users, reinforcing the cycle.

Case Study: The Quora Example

To understand how algorithms work in promoting content, consider an example from Quora. If a user asks a question that is racially charged, like Why do black children never know their fathers?, the algorithm might promote this question because it generates a lot of engagement. Even if the question is not factually correct or not inherently harmful, the algorithm sees a spike in views and comments and thus, it gets more visibility. This can lead to a flood of responses, some of which might be highly inflammatory or supportive of negative views.

On the other hand, if someone asks a positive question like, Who is working in social projects in a given city?, this content might not get much traction because it lacks the emotional or controversial pull that algorithms typically favor.

The Broader Implications

The promotion of mean and politically inflammatory content has far-reaching implications. It not only contributes to a polarized society but also can lead to the erosion of trust in facts and institutions. Social media platforms have a responsibility to ensure that the content they promote is not only engaging but also ethical and inclusive.

Michael Zuckerman, a prominent figure in the technology world, is often cited as an example of someone who has learned from the past. However, the question remains: is the platform unrepentantly using this strategy on other feeds today? The answer is likely yes, as it serves to keep people engaged and drive ad revenue.

In conclusion, while social media algorithms aren't specifically designed to promote mean or politically inflammatory content, they are programmed to promote what they believe will engage their users. This often results in the circulation of inflammatory and divisive content. Addressing this issue requires a multifaceted approach, including better algorithm design, user education, and platform ethical standards.