3HAN: A Deep Neural Network for Fake News Detection – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

3HAN: A Deep Neural Network for Fake News Detection

NLPIR SEMINAR Y2018#13

INTRO

       In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.

Arrangement

This week’s seminar is organized as follows:
1. The seminar time is 1.pm, Fri, at Zhongguancun Technology Park ,Building 5, 1306.
2. The lecturer is Ilham, the paper’s title is 3HAN: A Deep Neural Network for Fake News Detection.
3. The seminar will be hosted by Zhaoyou Liu.
4. Attachment is the paper of this seminar, please download in advance.

Anyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper

3HAN: A Deep Neural Network for Fake News Detection

Sneha Singhania        Nigel Fernandez       and Shrisha Rao

Abstract

       The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network (3HAN) for fast, accurate detection of fake news. 3HAN has three levels, one each for words, sentences, and the headline, and constructs a news vector: an effective representation of an input news article, by processing an article in an hierarchical bottom-up manner. The headline is known to be a distinguishing feature of fake news, and furthermore, relatively few words and sentences in an article are more important than the rest. 3HAN gives a differential importance to parts of an article, on account of its three layers of attention. By experiments on a large real-world data set, we observe the effectiveness of 3HAN with an accuracy of 96.77%. Unlike some other deep learning models, 3HAN provides an understandable output through the attention weights given to different parts of an article, which can be visualized through a heatmap to enable further manual fact checking.

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