The rising trend of information fusion on social media renders single-channel approaches to public opinion management increasingly inefficient in an age of multifaceted information dissemination. A multientity collaborative governance model offers a promising solution to this challenge, as it facilitates innovative cooperation and collective governance paradigms for public opinion management. Moreover, it can effectively address personalized information needs among users in a context where diverse types of information are readily accessible. We propose a model for the evolution and analysis of public opinion that takes into account the multientity collaboration. Based on the division of entity functions, a two-layer network with an interlayer promotion effect for multifaceted information dissemination is constructed. Control strategies, such as collaborative activation timing, isolation, and forced silence, are introduced to regulate the dissemination process. The results indicate that the multientity collaborative mechanism is significantly more effective than the single-entity approach in public opinion management. Additionally, the implementation of collaborative interventions during the rapid development stage of public opinion can effectively suppress its dissemination. Isolation and forced silence yield instantaneous and effective results for public opinion management. The publication of announcements enhances the dissemination of media content, facilitating targeted communication that resonates diverse user groups within a complex and dynamic information ecosystem.

This paper presents Meta's FBDetect system, which advances the state of the art in performance regression detection by catching regressions as small as 0.005% in noisy production environments. FBDetect monitors around 800,000 time series covering various types of metrics (e.g., throughput, latency, CPU and memory usage) to detect regressions caused by code or configuration changes in hundreds of services running on millions of servers. FBDetect introduces advanced techniques to capture stack traces fleet-wide, measure fine-grained subroutine-level performance differences, filter out deceptive false-positive regressions, deduplicate correlated regressions, and analyze root causes. Beyond these individual techniques, a key strength of FBDetect over prior work is its battle-tested robustness, proven by seven years of production use, and each year catching regressions that would have wasted millions of servers if left undetected.