用多通道CNN-LSTM模型识别孟加拉语假新闻，准确率达75.05%。
Bangla Fake News Detection Based On Multichannel Combined CNN-LSTM
- 设计多通道CNN-LSTM架构，结合特征提取与序列建模。
- 在约5万条孟加拉语新闻上实现75.05%的检测准确率。
- 针对低资源语言假新闻检测，适合中文读者关注跨语言应用。
近年来，虚假信息在虚假网络和新闻门户中迅速传播，对社会造成严重危害。例如，2019年曾有谣言称孟加拉国帕德玛大桥建设需10万人头祭祀，导致无辜者死亡。尽管英语领域研究较多，但孟加拉语相关研究较少。本文旨在从非可信新闻源中识别假新闻，为读者提供真实信息。提出一种多通道组合的CNN-LSTM模型，其中CNN用于深度特征提取，LSTM用于基于提取特征的检测。研究通过收集网站数据构建了包含约5万条新闻的数据集，并采用深度学习方法进行部署。所提模型在孟加拉语假新闻检测任务中达到75.05%的准确率，表明其在该领域具有积极前景。
原文摘要 · Abstract (English)
There have recently been many cases of unverified or misleading information circulating quickly over bogus web networks and news portals. This false news creates big damage to society and misleads people. For Example, in 2019, there was a rumor that the Padma Bridge of Bangladesh needed 100,000 human heads for sacrifice. This rumor turns into a deadly position and this misleading information takes the lives of innocent people. There is a lot of work in English but a few works in Bangla. In this study, we are going to identify the fake news from the unconsidered news source to provide the newsreader with natural news or real news. The paper is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM), where CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. The first thing we did to deploy this piece of work was data collection. We compiled a data set from websites and attempted to deploy it using the methodology of deep learning which contains about 50k of news. With the proposed model of Multichannel combined CNN-LSTM architecture, our model gained an accuracy of 75.05%, which is a good sign for detecting fake news in Bangla.
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