MAPX: An explainable model-agnostic framework for the detection of false information on social media networks

• URL: http://arxiv.org/abs/2409.08522v1
• Date: Fri, 13 Sep 2024 03:45:10 GMT
• Title: MAPX: An explainable model-agnostic framework for the detection of false information on social media networks
• Authors: Sarah Condran, Michael Bewong, Selasi Kwashie, Md Zahidul Islam, Irfan Altas, Joshua Condran,
• Abstract summary: We introduce a novel model-agnostic framework, called MAPX, which allows evidence based aggregation of predictions. We perform extensive experiments on benchmarked fake news datasets to demonstrate the effectiveness of MAPX. Our empirical results show that the proposed framework consistently outperforms all state-of-the-art models evaluated.
• Score: 1.5196326555431678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automated detection of false information has become a fundamental task in combating the spread of "fake news" on online social media networks (OSMN) as it reduces the need for manual discernment by individuals. In the literature, leveraging various content or context features of OSMN documents have been found useful. However, most of the existing detection models often utilise these features in isolation without regard to the temporal and dynamic changes oft-seen in reality, thus, limiting the robustness of the models. Furthermore, there has been little to no consideration of the impact of the quality of documents' features on the trustworthiness of the final prediction. In this paper, we introduce a novel model-agnostic framework, called MAPX, which allows evidence based aggregation of predictions from existing models in an explainable manner. Indeed, the developed aggregation method is adaptive, dynamic and considers the quality of OSMN document features. Further, we perform extensive experiments on benchmarked fake news datasets to demonstrate the effectiveness of MAPX using various real-world data quality scenarios. Our empirical results show that the proposed framework consistently outperforms all state-of-the-art models evaluated. For reproducibility, a demo of MAPX is available at \href{https://github.com/SCondran/MAPX_framework}{this link}

Related papers

• LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content [62.816876067499415]
  We propose LiveXiv: a scalable evolving live benchmark based on scientific ArXiv papers. LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs. We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities.
  arXiv  Detail & Related papers  (2024-10-14T17:51:23Z)
• Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models [19.015202590038996]
  We evaluate the factuality of different models tuned by various preference learning algorithms. We propose textbfAPEFT (textbfAtomic textbfPreference textbfEnhanced textbfFactuality textbfTuning) to enhance model's awareness of factuality.
  arXiv  Detail & Related papers  (2024-06-18T09:07:30Z)
• GVdoc: Graph-based Visual Document Classification [17.350393956461783]
  We propose GVdoc, a graph-based document classification model. Our approach generates a document graph based on its layout, and then trains a graph neural network to learn node and graph embeddings. We show that our model, even with fewer parameters, outperforms state-of-the-art models on out-of-distribution data.
  arXiv  Detail & Related papers  (2023-05-26T19:23:20Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [50.62245481416744]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Interpretable Fake News Detection with Topic and Deep Variational Models [2.15242029196761]
  We focus on fake news detection using interpretable features and methods. We have developed a deep probabilistic model that integrates a dense representation of textual news. Our model achieves comparable performance to state-of-the-art competing models.
  arXiv  Detail & Related papers  (2022-09-04T05:31:00Z)
• Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection [55.028065567756066]
  Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble neural networks (BE-SNNs) and overcome the feature collapse problem. We show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionM
  arXiv  Detail & Related papers  (2022-06-26T16:00:22Z)
• A Graph-Enhanced Click Model for Web Search [67.27218481132185]
  We propose a novel graph-enhanced click model (GraphCM) for web search. We exploit both intra-session and inter-session information for the sparsity and cold-start problems.
  arXiv  Detail & Related papers  (2022-06-17T08:32:43Z)
• MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection [3.210653757360955]
  We propose MiDAS, a multi-domain adaptative approach for fake news detection. MiDAS ranks relevancy of existing models to new samples. We evaluate MiDAS on generalization to drifted data with 9 fake news datasets.
  arXiv  Detail & Related papers  (2022-05-19T19:36:08Z)

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.