DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection

• URL: http://arxiv.org/abs/2408.10883v1
• Date: Tue, 20 Aug 2024 14:13:54 GMT
• Title: DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection
• Authors: Xinqi Su, Yawen Cui, Ajian Liu, Xun Lin, Yuhao Wang, Haochen Liang, Wenhui Li, Zitong Yu,
• Abstract summary: We propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection. For knowledge-based methods, we introduce the Monte Carlo Tree Search (MCTS) algorithm. For semantic-based methods, we define four typical deceit patterns.
• Score: 23.17963985187272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection (MFND) methods can be classified into knowledge-based and semantic-based approaches. However, these methods are overly dependent on human expertise and feedback, lacking flexibility. To address this challenge, we propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection. For knowledge-based methods, we introduce the Monte Carlo Tree Search (MCTS) algorithm to leverage the self-reflective capabilities of large language models (LLMs) for prompt optimization, providing richer, domain-specific details and guidance to the LLMs, while enabling more flexible integration of LLM comment on news content. For semantic-based methods, we define four typical deceit patterns: emotional exaggeration, logical inconsistency, image manipulation, and semantic inconsistency, to reveal the mechanisms behind fake news creation. To detect these patterns, we carefully design four discriminators and expand them in depth and breadth, using the soft-routing mechanism to explore optimal detection models. Experimental results on three real-world datasets demonstrate the superiority of our approach. The code will be available at: https://github.com/SuXinqi/DAAD.

Related papers

• Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions [0.0]
  pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public. Recent works include powering the detection using large language model advances in multimodal frameworks. The review further identifies critical gaps in adaptability to dynamic social media trends, real-time, and cross-platform detection capabilities.
  arXiv  Detail & Related papers  (2025-02-01T06:56:17Z)
• GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection [18.157900272828602]
  Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. This paper develops a significantly novel approach, GAMED, for multimodal modelling. It focuses on generating distinctive and discriminative features through modal decoupling to enhance cross-modal synergies.
  arXiv  Detail & Related papers  (2024-12-11T19:12:22Z)
• Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts [68.48103545146127]
  This paper proposes a novel framework for unsupervised exploration of diffusion latent spaces. We directly leverage natural language prompts and image captions to map latent directions. Our method provides a more scalable and interpretable understanding of the semantic knowledge encoded within diffusion models.
  arXiv  Detail & Related papers  (2024-10-25T21:44:51Z)
• Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection [50.079690200471454]
  Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. We propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives.
  arXiv  Detail & Related papers  (2024-07-12T03:15:01Z)
• Fake News Detection and Manipulation Reasoning via Large Vision-Language Models [38.457805116130004]
  This paper introduces a benchmark for fake news detection and manipulation reasoning, referred to as Human-centric and Fact-related Fake News (HFFN) The benchmark highlights the centrality of human and the high factual relevance, with detailed manual annotations. A Multi-modal news Detection and Reasoning langUage Model (M-DRUM) is presented not only to judge on the authenticity of multi-modal news, but also raise analytical reasoning about potential manipulations.
  arXiv  Detail & Related papers  (2024-07-02T08:16:43Z)
• Text-Video Retrieval with Global-Local Semantic Consistent Learning [122.15339128463715]
  We propose a simple yet effective method, Global-Local Semantic Consistent Learning (GLSCL) GLSCL capitalizes on latent shared semantics across modalities for text-video retrieval. Our method achieves comparable performance with SOTA as well as being nearly 220 times faster in terms of computational cost.
  arXiv  Detail & Related papers  (2024-05-21T11:59:36Z)
• MSynFD: Multi-hop Syntax aware Fake News Detection [27.046529059563863]
  Social media platforms have fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news. We propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news.
  arXiv  Detail & Related papers  (2024-02-18T05:40:33Z)
• Detecting and Grounding Multi-Modal Media Manipulation and Beyond [93.08116982163804]
  We highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM4) DGM4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content. We propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities.
  arXiv  Detail & Related papers  (2023-09-25T15:05:46Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• A Multi-Policy Framework for Deep Learning-Based Fake News Detection [0.31498833540989407]
  This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection. MPSC uses deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious.
  arXiv  Detail & Related papers  (2022-06-01T21:25:21Z)
• FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment [79.23531577235887]
  This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data. Aditionally, the demo includes a new algorithm for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.
  arXiv  Detail & Related papers  (2020-09-12T17:45:09Z)

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.