A Multi-Layer AI Framework for Information Landscape Analysis

• URL: http://arxiv.org/abs/2606.26115v1
• Date: Fri, 22 May 2026 16:02:45 GMT
• Title: A Multi-Layer AI Framework for Information Landscape Analysis
• Abstract summary: This paper proposes a multi-layer AI framework for information landscape analysis in the context of information disorder.<n>Rather than treating misinformation detection as a binary fact-checking task, the framework analyzes political and media content across multiple dimensions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
  We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
  arXiv  Detail & Related papers  (2026-01-21T16:47:05Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• Structured Attention Matters to Multimodal LLMs in Document Understanding [52.37530640460363]
  We investigate how input format influences document comprehension performance.<n>We discover that raw OCR text often impairs rather than improves MLLMs' performance.<n>We propose a novel structure-preserving approach that encodes document elements using the LaTex paradigm.
  arXiv  Detail & Related papers  (2025-06-19T07:16:18Z)
• VisTopics: A Visual Semantic Unsupervised Approach to Topic Modeling of Video and Image Data [0.0]
  This study introduces VisTopics, a computational framework designed to analyze large-scale visual datasets.<n>Applying VisTopics to a dataset of 452 NBC News videos resulted in reducing 11,070 frames to 6,928 deduplicated frames, which were then semantically analyzed to uncover 35 topics.
  arXiv  Detail & Related papers  (2025-05-20T19:59:41Z)
• Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
  We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
  arXiv  Detail & Related papers  (2025-04-10T02:52:34Z)
• A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok [0.0]
  This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect suspected disinformation in TikTok videos.<n>The methodology is comprised of two core components: a multimodal feature analyser that extracts and evaluates data from text, audio, and video; and a multimodal disinformation detector based on fuzzy logic.
  arXiv  Detail & Related papers  (2025-02-09T12:37:48Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Onologies are widely used for representing domain knowledge and meta data.<n> logical reasoning that can directly support are quite limited in learning, approximation and prediction.<n>One straightforward solution is to integrate statistical analysis and machine learning.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Detecting misinformation through Framing Theory: the Frame Element-based Model [6.4618518529384765]
  We focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community. We propose an innovative approach leveraging the power of pre-trained Large Language Models and deep neural networks to detect misinformation.
  arXiv  Detail & Related papers  (2024-02-19T21:50:42Z)
• Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis [3.231170156689185]
  Document AI aims to automatically analyze documents by leveraging natural language processing and computer vision techniques. One of the major tasks of Document AI is document layout analysis, which structures document pages by interpreting the content and spatial relationships of layout, image, and text.
  arXiv  Detail & Related papers  (2023-08-29T16:58:03Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Survey on Visual Sentiment Analysis [87.20223213370004]
  This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
  arXiv  Detail & Related papers  (2020-04-24T10:15:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.