Is Less Really More? Fake News Detection with Limited Information

2025-04-02

AI Summary

False news detection suffers from computational inefficiency, strong data dependency, and poor cross-domain robustness, with most existing methods relying on full-text analysis. This paper proposes SLIM—a novel information-theoretic framework for limited-information quantification and selection—marking the first approach to achieve high-accuracy detection using only minimal signals (e.g., headlines, lead sentences, and keywords). SLIM leverages entropy and mutual information to identify discriminative features, integrates lightweight encoders, fuses multi-source limited information, and employs few-shot robust training. On multiple benchmarks, SLIM attains over 98% of the accuracy of state-of-the-art full-text methods while reducing training data requirements by 76%, accelerating inference by 3.2×, and decreasing cross-domain generalization error by 41%. This work provides the first empirical evidence that highly compressed input representations can match full-text performance.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques.

Problem

• Detecting fake news using minimal information effectively
• Overcoming limitations of full-text based detection methods
• Achieving robust performance with reduced training data

Innovation

• Uses systematically selected limited information
• Introduces information-theoretic measures
• Combines various types of limited information