A recent experiment by Business Insider reporter Katie Notopoulos highlights that asking questions on Meta’s Threads platform may drive more reach and engagement. In her experiment, Notopoulos posted controversial questions, which sparked emotional reactions and generated significant user responses. The results suggest that Threads’ algorithm prioritises posts with high comment activity over Likes and re-shares, promoting more discussion-based content.
This focus on conversation aligns with Meta’s goal of making Threads a more positive alternative to Twitter, emphasizing meaningful engagement and potentially aiding in the development of Meta’s AI models. By prioritising discussions, Meta can collect valuable human-generated conversations to train its AI, similar to how Reddit is valued as a data source for large language models due to its question-and-answer format.
Rage Baiting and Its Implications
Notopoulos’ experiment was designed to test whether “rage baiting”—posting content that sparks outrage—functions similarly on Threads as it does on other platforms like Twitter. Her findings indicate that controversial questions generate heightened emotional responses, driving higher engagement levels. This raises concerns about whether Threads might fall into the same trap as other platforms where inflammatory content is rewarded with more reach.
However, Meta seems intent on preventing this, as the company has stressed that Threads should be a more positive and less toxic environment. Encouraging conversations could keep the platform more engaging while avoiding the pitfalls of platforms where conflict and outrage dominate.
Why Focus on Discussion?
There’s another layer to why Meta is prioritising discussion on Threads: its ongoing development of generative AI models. Meta, like many tech companies, is actively working on advancing AI and chatbots, which require large amounts of human-generated content to train properly. Threads’ emphasis on discussions, particularly those that pose questions and generate real human responses, may provide exactly the type of data that AI models need to improve.
Much like Reddit, which is a key source of data for training large language models (LLMs) due to its Q&A structure, Threads’ focus on human conversation could serve as a valuable resource for AI development. Posts that encourage replies and interactions offer rich material for training AI systems to better understand human dialogue.
The Future of Threads: Balancing Engagement and Positivity
Meta’s experiment with Threads presents both an opportunity and a challenge. On one hand, encouraging more genuine conversations could create a more positive social platform. On the other, there’s a risk that tactics like rage baiting might hijack these conversations, leading to a more hostile environment, much like what’s happened on other platforms.
Ultimately, how Meta balances engagement with its goal of fostering positivity will determine whether Threads can become the alternative to Twitter that Meta envisions. In the meantime, experiments like Notopoulos’ highlight how important conversation is to the platform’s algorithm, and why Threads may play a key role in Meta’s broader AI ambitions.
This focus on discussions, paired with the right moderation, could distinguish Threads as a platform that encourages meaningful interaction while fueling AI development. Whether Threads can maintain this balance remains to be seen, but its current trajectory suggests a clear intent to promote constructive dialogue and innovation in the world of social media.
