Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information

• URL: http://arxiv.org/abs/2302.10195v1
• Date: Sun, 19 Feb 2023 00:54:33 GMT
• Title: Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
• Authors: Zhen Guo, Qi Zhang, Xinwei An, Qisheng Zhang, Audun J{\o}sang, Lance M. Kaplan, Feng Chen, Dong H. Jeong, Jin-Hee Cho
• Abstract summary: Distinguishing the types of fake news spreaders based on their intent is critical. We propose an intent classification framework that can best identify the correct intent of fake news.
• Score: 17.25399815431264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy.

Related papers

• A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention [0.0]
  We propose a semi-supervised self-learning method in which a sentiment analysis is acquired by some state-of-the-art pretrained models. Our learning model is trained in a semi-supervised fashion and incorporates LSTM with self-attention layers. We benchmark our model on a dataset with 20,000 news content along with their feedback, which shows better performance in precision, recall, and measures compared to competitive methods in fake news detection.
  arXiv  Detail & Related papers  (2024-07-27T20:00:10Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification. XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• 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)
• Verifying the Robustness of Automatic Credibility Assessment [79.08422736721764]
  Text classification methods have been widely investigated as a way to detect content of low credibility. In some cases insignificant changes in input text can mislead the models. We introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• 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)
• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [105.20743048379387]
  We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
  arXiv  Detail & Related papers  (2022-03-10T14:24:19Z)
• Explainable Tsetlin Machine framework for fake news detection with credibility score assessment [16.457778420360537]
  We propose a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) We use the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least $5%$ in terms of accuracy.
  arXiv  Detail & Related papers  (2021-05-19T13:18:02Z)
• Supporting verification of news articles with automated search for semantically similar articles [0.0]
  We propose an evidence retrieval approach to handle fake news. The learning task is formulated as an unsupervised machine learning problem. We find that our approach is agnostic to concept drifts, i.e. the machine learning task is independent of the hypotheses in a text.
  arXiv  Detail & Related papers  (2021-03-29T12:56:59Z)
• Evaluating Deep Learning Approaches for Covid19 Fake News Detection [0.0]
  We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. We report the best accuracy of 98.41% on the Covid-19 Fake news detection dataset.
  arXiv  Detail & Related papers  (2021-01-11T16:39:03Z)
• Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference [150.07326223077405]
  Few-shot learning is attracting much attention to mitigate data scarcity. We present a discriminative nearest neighbor classification with deep self-attention. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model.
  arXiv  Detail & Related papers  (2020-10-25T00:39:32Z)
• 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.