The paper investigates why misinformation spreads more quickly on engagement‑based platforms by dissecting the recommendation algorithm of X. It identifies an engagement fungibility mechanism that rewards instant reactions (likes, retweets) over thoughtful engagement (replies, quotes), allowing misinformation—which tends to attract instant reactions—to receive more recommendations. The authors validate this mechanism through a simulation on the USC X 2024 election corpus, showing that adjusting metric weights has little effect, while requiring thoughtful engagement before amplification can significantly reduce the credibility exposure gap without harming mainstream content or engagement.
arXiv:2606. 02883v1 Announce Type: cross Abstract: Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior.
arXiv:2608. 16323v1 Announce Type: cross Abstract: The spread of misinformation on social networks poses a significant challenge to online communities and society at large.
arXiv:2608.30311v1 Announce Type: cross Abstract: Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-makin...
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
arXiv:2607. 01824v1 Announce Type: new Abstract: Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control.
arXiv:2608. 07498v1 Announce Type: cross Abstract: Autonomous AI agents in social media present concrete risks to democratic discourse and platform governance, while also offering tools for pre-deployment recommender system testing.
Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control. These systems have been developed around a common underlying concept: a bridging mechanism that identifies notes flagging misleading information when they receive support from people with different perspectives rather than simple majority support.
The paper introduces a lifecycle framework for LLM-as-a-Judge systems used to evaluate recommendation explanations at Netflix. It outlines four phases—Birth, Training, Deployment, and Monitoring—detailing how each stage addresses specific technical and operational challenges. The authors report that after five weeks of A/B testing, judge-aligned explanations increased novel content viewing and successful browse-to-play sessions without quality takedowns.
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
This paper introduces a multi‑branch fusion framework that combines transformer‑based semantics, rhetorical cues, stance representations, and psychologically motivated proxies to detect health misinformation and characterize its spread on online social networks. The authors propose an interpretable Cognitive Propagation Score (CPS) derived from text cues that estimate argument complexity, emotional intensity, and virality potential, aiding diffusion‑risk reasoning when engagement data are missing. Experiments on three benchmark datasets (Constraint, COVID‑19_FNIR, Monkeypox) demonstrate near‑perfect classification and ranking performance, with ablation studies showing complementary gains from psychological and rhetorical components.
RPCBench is a new benchmark designed to evaluate large language models’ ability to critique recommendation requests by detecting, diagnosing, and handling flawed premises. It includes evidence‑grounded test instances across five recommendation domains and ten types of premise failures, and introduces a fine‑grained evaluation framework covering detection, error localization, handling strategy, and evidence faithfulness. Experiments with 11 LLMs reveal that proactive detection is the main bottleneck, with models struggling most on underspecified‑premise errors and showing that optimal critique quality occurs at intermediate reasoning lengths.
arXiv:2608. 11493v1 Announce Type: new Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale.