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.
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
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.