TELLER : A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection (2024)

Abstract: The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia. While existing deep-learning based methods have made progress in detecting fake news accurately, their reliability may be compromised caused by the non-transparent reasoning processes, poor generalization abilities and inherent risks of integration with large language models (LLMs). To address this challenge, we propose TELLER, a novel framework for trustworthy fake news detection that prioritizes explainability, generalizability and controllability of models. This is achieved via a dual-system framework that integrates cognition and decision systems, adhering to the principles above. The cognition system harnesses human expertise to generate logical predicates, which guide LLMs in generating human-readable logic atoms. Meanwhile, the decision system deduces generalizable logic rules to aggregate these atoms, enabling the identification of the truthfulness of the input news across diverse domains and enhancing transparency in the decision-making process. Finally, we present comprehensive evaluation results on four datasets, demonstrating the feasibility and trustworthiness of our proposed framework.

## introduction fake news has emerged as a prominent social problem due to the rampant dissemination facilitated by social media platforms @xcite . additionally, the swift progress of generative artificial intelligence has further amplified this issue @xcite . while human factchecking experts can accurately verify the authenticity of news, their efforts cannot scale with the overwhelming volume of online information. con-1 https://github.com/less-and-less-bugs/trust_ teller figure 1 : three crucial aspects of trustworthy fake news detection algorithms and the correlation between these principles and our dual-sytem framework teller. despite the improved predictive accuracy achieved by current deep learning-based detection approaches @xcite @xcite , these methods suffer from the lack of transparency because of the black-box nature of neural networks @xcite and a limited ability to generalize to unseen data of which the distribution is different from training data, given the inherent diversity of online information (e.g., topics, styles and media platforms) @xcite . moreover, the increasing integration with llms is prone to uncontrollable risks due to hallucinations and societal applications. thus, a growing awareness emphasizes trustworthiness foot_0 of these systems @xcite . unfortunately, the characteristics of a trustworthy fake news detector remain an open question. hence, based on recent surveys of trustworthy ai @xcite and fake news detection @xcite , we identify three crucial aspects that go beyond accuracy for fake news detection technologies: explainability, generalizability, and controllability. these aspects work collectively to enhance system security and trustworthiness. firstly, explainability refers to understanding how an ai model performs decision @xcite . this mechanism serves as a fundamental requirement for establishing end-user trust in these tools, as it enables the disclosure of complex reasoning processes and the identification of potential flaws in neural networks. secondly, generalizability represents the capability to acquire knowledge from limited training data to predict accurately in unseen situations @xcite . given the impracticality of exhaustively collecting and annotating vast amounts of data across various news domains, generalization ensures the affordable and sustainable deployment of data-driven fake news detection algorithms. lastly, controllability encompasses the capacity for human guidance and intervention in the behavior of models @xcite . this objective benefits models in understanding specific misinformation regulatory policies and rectifying deviations if necessary. while recent practices may satisfy the requirements of explainability @xcite or generalization @xcite , they often fail to adhere to all three principles simultaneously. to this end, we propose teller, a trustworthy framework for explainable, generalizable and con-trollabe detector, drawing inspiration from the dual-system theory foot_1 @xcite . this framework abstracts the existing pipeline of fake news detection into two components: the cognition and decision systems. as depicted in fig. 1 , the cognition system serves as the first step and is responsible for transforming meaningful human expertise from renowned journalism teams @xcite into a set of yes/no question templates that correspond to logic predicates. these decomposed questions are then answered using llms, which provide truth values for corresponding logic atoms. on the other hand, the decision system, empowered by a differentiable neural-symbolic model @xcite , can integrate the output of the cognition system to deduce the final authenticity of input news by leveraging domain invariant logic rules learned from data automatically. this visible logic-based ensemble can mitigate the negative effects caused by inaccurate predictions of llms and allow for the correction of unreasonable rules through adjusting the weights in the model manually to align with human expertise. our framework ensures explainability by incorporating human-readable question templates (predicates) and a transparent decision-making process based on logic rules. this interpretability further enables the flexibility to adjust rules and enhances the model's robustness against false llm predictions, thereby guaranteeing controllability. moreover, our model exhibits generalizability, attributed to the generalizable performance of llms combined with reliable human experience as guidance and the utilization of the neural-symbolic model, which can learn domain-generalizable rules. to summarize, the contributions of this work include: 1) we introduce a systematic framework comprising cognition and decision modules, aiming to uphold three crucial principles for establishing a trustworthy fake news detection system: explainability, generalizability, and controllability. 2) we validate the effectiveness of our framework by conducting comprehensive experiments using various llms on four benchmarks. the results demonstrate the feasibility and trustworthiness of teller across different scenarios. ## methodology formally, given a piece of news t , the objective of the fake news detection task is to predict its label of truthfulness @xmath0. for example, in binary classification setting, @xmath1, false}, and t is identified as real (fake) when y is true (false). as depicted in fig. 2 , teller involves two main components: cognition and decision systems. the cognition system decomposes human expertise into yes/no question templates corresponding to logic predicates. when presented with a new input t , the templates and predicates can be instantiated to form questions and logic atoms. by leveraging the parametric knowledge inside llms and gathering additional information from external tools (e.g., search engines), the cognition system can generate answers to these questions, represented as truth values of logic atoms. then, the decision system takes these truth values as input and generates interpretable logic clauses to debunk misinformation by a neural-symbolic model, which can learn generic logic rules from data in an end-to-end manner. ## experiments in this section, we present the experiment setup and demonstrate the feasibility, explainability, generalizability and controllability of teller through extensive experiments. ## conclusion in this work, we address the limitations of existing fake news detection methods, which struggle to establish reliability and end-user trust. to tackle this issue, we identify three crucial aspects for constructing trustworthy misinformation detection systems: explainability, generalizability, and controllability. by prioritizing these principles, we propose a dual-system framework teller that incorporates cognition and decision systems. to validate our framework's feasibility, explainability, generalizability, and controllability, we conduct extensive experiments on diverse datasets and llms. these results affirm the effectiveness and trustworthiness of our approach and highlight its significant potential through evolving both subsystems in the future. while we achieve trustworthiness from an algorith-mic perspective, we emphasize the importance of further research to improve the trustworthiness of the entire lifecycle of fake news detection systems.