Scheduling with Time-Evolving Uncertainty for Content Review Prioritization in Social Media

The talk presents a rigorous and well-motivated research contribution. The problem is clearly defined and practically relevant, given the scale of content moderation and the significant investment by platforms. The speaker effectively motivates the need for a new queueing model that accounts for time-evolving uncertainty in post views, which is a realistic and important aspect often ignored in classical queueing theory. The theoretical contribution, an asymptotically optimal algorithm, is a strong result, though the talk does not delve into the technical details of the proof. The use of simulations based on real data adds credibility and demonstrates practical applicability. The speaker is transparent about assumptions, such as perfect human reviewers, and acknowledges limitations. The presentation is well-structured, with clear explanations and helpful analogies. The sources cited include the preprint on arXiv and the Simons Institute talk page, which are appropriate. The main weakness is the lack of peer-reviewed publication at the time of the talk, and the presentation necessarily simplifies some aspects of the real system. Overall, the talk provides valuable insights into a complex operational problem and offers a promising algorithmic solution. The adéquation titre/contenu is excellent, as the title accurately reflects the focus on scheduling with time-evolving uncertainty. The audience questions are addressed thoughtfully, and the speaker engages with the audience effectively. The talk is suitable for a technical audience familiar with queueing theory and optimization, but the core ideas are accessible to a broader scientific audience.

The title accurately reflects the content: the talk focuses on scheduling algorithms for content review under time-evolving uncertainty, with a specific application to social media prioritization.

8/10

The talk presents original research with a formal theoretical model, asymptotic optimality guarantees, and simulations based on real data. The speaker is a recognized researcher (MIT), and the work is part of a Simons Institute seminar. The preprint is available on arXiv. However, the talk is a presentation, not a peer-reviewed publication, and some details are simplified.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 00:00Introduction to content moderation on social media, scale of the problem, and investment by platforms.
• 05:00Description of the AI-human pipeline: AI models make initial decisions, human reviewers correct errors and provide labels.
• 10:00Simplified setting: focus on scheduling, exogenous admission, posts kept on platform, perfect reviewers.
• 15:00Definition of the goal: minimize negative impact of violating posts, cost proportional to views over time.
• 20:00Introduction of the new queueing model with time-evolving uncertainty in post views.
• 25:00Theoretical results: asymptotically optimal algorithm.
• 30:00Simulations based on real data showing outperformance over status quo heuristics.
• 35:00Discussion of practical implications and future work.
• 40:00Q&A session: handling appeals, ambiguity, and reviewer reliability.

• Preprint on arXiv — The paper corresponding to the talk, providing full details of the model and algorithms.
• Simons Institute talk page — Official page of the talk, part of the program 'Bridging Prediction and Intervention Problems in Social Systems'.

• Simons Institute program page — The program under which the talk was given, indicating relevance to the broader research agenda.