构建多层AI框架，系统分析信息环境中的偏见与操纵模式。
A Multi-Layer AI Framework for Information Landscape Analysis
- 从多维度解析媒体内容，包括信源可信度、叙事框架与情绪激活。
- 揭示信息传播动态，支持对事件全貌的结构化呈现。
- 适用于研究信息失序问题，适合政策与舆情分析人员使用。
本文提出一种多层AI框架，用于信息失序背景下的信息景观分析。该框架不将虚假信息检测视为简单的真假判断任务，而是从多个维度分析政治与媒体内容：信源可靠性、事实结构、表述框架、偏见、情绪激活、操纵模式及传播动态。目标是超越孤立的事实核查，建立对事件、实体或叙事所处信息环境的结构性描述。论文主张，媒体分析类AI系统应支持认知制图——即透明、多维度地呈现事实、解读、主体与叙事随时间的互动关系。本文阐述了该框架的概念架构、分析层次与方法论依据，旨在为信息失序研究提供更细致、可解释且具批判价值的工具支持。
原文摘要 · Abstract (English)
This paper proposes a multi-layer AI framework for information landscape analysis in the context of information disorder. Rather than treating misinformation detection as a binary fact-checking task, the framework analyzes political and media content across multiple dimensions, including source reliability, factual structure, framing, bias, emotional activation, manipulation patterns, and propagation dynamics. The goal is to move beyond isolated claim verification toward a structured representation of the informational environment surrounding an event, entity, or narrative. We argue that AI systems for media analysis should support epistemic mapping: a transparent, multi-dimensional account of how facts, interpretations, actors, and narratives interact over time. The paper presents the conceptual architecture, analytical layers, and methodological rationale of the framework, with the aim of supporting more nuanced, explainable, and critically useful tools for information disorder research.
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