In a world dominated by social media algorithms, Spread has emerged as a platform that prioritizes meaningful human engagement over maximizing time-on-site metrics. Unlike traditional social networks, Spread does not allow brands or bots on its platform, and it refrains from supporting ads. Instead, the founder, Rogers, focuses on indicators of “meaningful” engagement, such as when a user clicks on another user’s recommendation and takes subsequent actions like signing up for a newsletter or subscription. By aligning company content with user interactions, Spread aims to recapture the essence of what social media was originally meant to achieve.
Jonathan Stray, a senior scientist at the UC Berkeley Center for Human-Compatible AI, raises concerns about the effectiveness of purely chronological feeds. While some may argue that simplicity promotes recency bias, Stray suggests that chronological feeds can also enable spam and other forms of social harm. He acknowledges that the tech industry’s focus on maximizing engagement does not necessarily lead to socially desirable outcomes. In fact, Stray proposes that the solution to algorithm-driven social media may require the implementation of more sophisticated algorithms. He recently initiated the Prosocial Ranking Challenge, a competition aimed at developing feed-ranking algorithms that prioritize socially desirable outcomes based on user well-being and feed informativeness.
As users seek to escape engagement-driven algorithms, they are turning to alternative platforms and communication methods. Group messaging has become a popular supplement to traditional social media feeds, offering a more intimate space for sharing content and discussions. By following a chronological timeline, group chats provide a break from the chaos of algorithmic feeds. The growth of platforms like WhatsApp within the US suggests a shift towards algorithm-free communication channels. With increasing activity moving towards private messaging, users are creating ad-free and controlled social environments within their group chats.
While the flight to algorithm-free spaces may seem appealing, it is essential to acknowledge the inherent biases and pitfalls that come with group messaging. Just like complex algorithms, group chats can also create echo chambers and reinforce cognitive biases. Each member’s personalized content contributions can further complicate the information shared within the group. Despite the growing trend towards algorithm-free environments, the quest for a perfect information feed remains a challenge that is yet to be fully resolved.
The rise of algorithm-free social networks and platforms reflects a growing disillusionment with engagement-driven algorithms. By prioritizing meaningful human interactions and user well-being, these platforms are redefining success metrics in the social media landscape. However, it is crucial to recognize that even in algorithm-free spaces, biases and pitfalls can still exist. As users navigate the evolving social media landscape, the balance between personalized content curation and algorithmic transparency will continue to shape the future of digital communication.
Leave a Reply
You must be logged in to post a comment.