利用深度学习检测假新闻的词典增强型长短期记忆(LSTM)

A. Mohammed Yasar, Dr. C. Meenakshi
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引用次数: 0

摘要

在线社交媒体(OSM)网络因其日益普及、成本低廉和易于访问的特性,已发展成为人们获取、消费和分享新闻的强大平台。传播假新闻与传播病毒一样危险。由于假新闻对社会的负面影响,假新闻检测引起了许多研究人员的关注。在过去的几年里,人们提出了许多假新闻检测方法,现有的大多数方法都依赖于新闻内容或社交媒体平台上新闻传播过程的社会背景。在这项工作中,我们提出了一种词典增强型 LSTM 自动模型,该模型能够同时考虑新闻内容和社会背景来识别假新闻。该模型首先使用情感词典作为额外信息预训练单词情感分类器,然后获取单词的情感嵌入,包括词典中没有的单词。我们使用五个性能指标来评估所提出的框架:准确率、曲线下面积、精确度、召回率和 f1-score。此外,与其他方法相比,该模型平均提高了 18.76%,这表明它在与基于假分类器的模型相比是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lexicon-Enhanced Long Short-Term Memory (LSTM) for Detecting of Fake News using Deep Learning
Due to its increasing popularity, low cost, and easy-to-access nature, online social media (OSM) networks have evolved as a powerful platform for people to access, consume, and share news.However, this has led to the large-scale distribution of fake news, i.e., deliberate, false, or misleading information. Spreading fake news is roughly as dangerous as spreading the virus. Fake news detection attracts many researchers' attention due to the negative impacts on the society Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news content or the social context of the news dissemination process on social media platforms. In this work, we propose a lexicon-enhanced LSTM an automated model that is able to take into account both the news content and the social context for the identification of fake news. The model first uses sentiment lexicon as an extra information pre-training a word sentiment classifier and then get the sentiment embeddings of words including the words not in the lexicon. Combining the sentiment embedding and its word embedding can make word representation more accurate and to detect fake news and better predict fake user accounts and posts.We used five performance metrics to evaluate the proposed framework: accuracy, the area under the curve, precision, recall, and f1-score.The model achieves an accuracy of 99.55% compared to 93.62% against discourse structure analysis. Also, it shows an average improvement of 18.76% against other approaches, which indicates its viability against fake-classifier-based models
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