论文标题
假新闻检测的混合合奏：尝试
Hybrid Ensemble for Fake News Detection: An attempt
论文作者
论文摘要
在机器学习领域，假新闻检测一直是一个具有挑战性的问题。研究人员已使用旧统计分类模型和现代深度学习通过多种技术对其进行了处理。如今，随着数据量的越来越多，NLP和ML领域的发展以及处置计算能力的增加，从不同的角度来看，有无限的排列和组合可以解决此问题。在本文中，我们尝试使用不同的方法来解决虚假新闻，并尝试建立构建，并提出混合合奏的可能性，将古典机器学习技术与现代深度学习方法结合在一起
Fake News Detection has been a challenging problem in the field of Machine Learning. Researchers have approached it via several techniques using old Statistical Classification models and modern Deep Learning. Today, with the growing amount of data, developments in the field of NLP and ML, and an increase in the computation power at disposal, there are infinite permutations and combinations to approach this problem from a different perspective. In this paper, we try different methods to tackle Fake News, and try to build, and propose the possibilities of a Hybrid Ensemble combining the classical Machine Learning techniques with the modern Deep Learning Approaches
