Engagement algorithms cause misinformation to spread faster online
Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X
Social and Information NetworksMachine Learning
Summary
Misinformation spreads faster on social media because of how recommendation algorithms count user reactions. The researchers found that algorithms boost content if many people give quick reactions like likes and retweets, but not necessarily thoughtful responses like comments. Because misinformation often gets more quick reactions, it ends up being promoted more. They showed that changing how these algorithms work to require thoughtful engagement first can reduce the spread of false information without lowering overall user engagement or visibility of trustworthy content.
What this means in practice
- •For social media platform engineers: Design recommendation systems that prioritize posts predicted to receive thoughtful responses to reduce misinformation amplification.
- •For content moderation teams: Improve credibility exposure management by implementing thresholds that prevent promotion of low-credibility content based on engagement type.
Authors
Pan Li, Shuang Gao
Abstract
Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X's recommendation algorithms, we conduct what is, to our knowledge, the first component-level study of the recommendation algorithm deployed by a social media platform, which examines how each of its components affects misinformation propagation. Specifically, we identify the engagement fungibility mechanism in the algorithm, where the final recommendation score is constructed as a weighted sum of all predicted user activities. As a result, a tweet can be repeatedly recommended simply because it is predicted to draw many instant reactions (e.g., likes and retweets), even when it is not expected to draw thoughtful responses (e.g., replies and quotes). Since misinformation typically draws a larger share of its engagement from instant reactions, this mechanism enables it to receive more recommendation exposure and to propagate faster. To empirically validate this mechanism, we re-implement X's recommendation algorithm on the USC X 2024 election corpus, and build a calibrated simulation study to analyze the impact of different scoring rules. We find that re-tuning the metric weights has little or even a negative impact on reducing the credibility exposure gap, while those scoring rules that set a precondition of thoughtful engagement for amplification would be able to alleviate the gap significantly, across 46 robustness checks. Our diagnosis, therefore, yields a simple and deployable fix, a reflective-threshold gate that withholds amplification until a tweet is predicted to draw thoughtful engagement, which we find to reallocate exposure away from low-credibility content at no cost to mainstream exposure and with no loss of engagement.