基于同义词增强的双向LSTM假新闻检测方法

2020 8th International Conference on Information and Communication Technology (ICoICT) Pub Date : 2020-06-01 DOI:10.1109/ICoICT49345.2020.9166230

Ghinadya, S. Suyanto

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引用次数: 14

摘要

假新闻是包含宣传内容的新闻，与实际新闻无关。今天，社交媒体上的新闻让互联网用户感到不安。因此，需要一个假新闻检测器来解决这个问题。本文研究了一种基于递归神经网络(RNN)的假新闻检测系统。该架构采用双向长短期记忆(Bi-LSTM)设计，并利用新闻标题和正文的姿态检测。对来自FNC-1的50 k篇新闻文章的评估表明，该方法在检测假新闻方面的f1得分为0.2423。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Synonyms-Based Augmentation to Improve Fake News Detection using Bidirectional LSTM

Fake news is the news which contains propaganda and not relevant to the actual news. Today, the news in social media are troubling internet user. Hence, a fake news detector is needed to solve the problem. In this research, a fake news detector system based on Recurrent Neural Network (RNN) is developed. The architecture is designed using Bidirectional Long Short-Term Memories (Bi-LSTM) with exploit stance detection for the headline and the body of the news. Evaluation on 50 k news articles from FNC-1 shows that the proposed method produces F1-score of 0.2423 in detecting the fake news.

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2020 8th International Conference on Information and Communication Technology (ICoICT)

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