Interpretable Propaganda Detection in News Articles

• URL: http://arxiv.org/abs/2108.12802v1
• Date: Sun, 29 Aug 2021 09:57:01 GMT
• Title: Interpretable Propaganda Detection in News Articles
• Authors: Seunghak Yu, Giovanni Da San Martino, Mitra Mohtarami, James Glass, Preslav Nakov
• Abstract summary: We propose to detect and to show the use of deception techniques as a way to offer interpretability. Our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.
• Score: 30.192497301608164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.

Related papers

• 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)
• Exposing and Explaining Fake News On-the-Fly [4.278181795494584]
  This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80 % accuracy and macro F-measure.
  arXiv  Detail & Related papers  (2024-05-03T14:49:04Z)
• ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs [13.608282497568108]
  ClaimVer is a human-centric framework tailored to meet users' informational and verification needs. It highlights each claim, verifies it against a trusted knowledge graph, and provides succinct, clear explanations for each claim prediction.
  arXiv  Detail & Related papers  (2024-03-12T17:07:53Z)
• Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection [50.07850264495737]
  "Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection. We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
  arXiv  Detail & Related papers  (2023-09-28T13:19:43Z)
• Adversarial Attacks on the Interpretation of Neuron Activation Maximization [70.5472799454224]
  Activation-maximization approaches are used to interpret and analyze trained deep-learning models. In this work, we consider the concept of an adversary manipulating a model for the purpose of deceiving the interpretation.
  arXiv  Detail & Related papers  (2023-06-12T19:54:33Z)
• Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation [74.38561925376996]
  Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph. For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
  arXiv  Detail & Related papers  (2023-03-15T12:01:43Z)
• 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)
• Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection [20.394457328537975]
  correlations between user-generated and user-shared content can be leveraged for detecting disinformation in online news articles. We develop a multimodal learning algorithm for disinformation detection.
  arXiv  Detail & Related papers  (2021-08-31T14:50:16Z)
• Technological Approaches to Detecting Online Disinformation and Manipulation [0.0]
  The move of propaganda and disinformation to the online environment is possible thanks to the fact that within the last decade, digital information channels radically increased in popularity as a news source. In this chapter, an overview of computer-supported approaches to detecting disinformation and manipulative techniques based on several criteria is presented.
  arXiv  Detail & Related papers  (2021-08-26T09:28:50Z)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)
• Machine Learning Explanations to Prevent Overtrust in Fake News Detection [64.46876057393703]
  This research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining.
  arXiv  Detail & Related papers  (2020-07-24T05:42:29Z)

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.