On the Benefit of Combining Neural, Statistical and External Features
for Fake News Identification
release_cmmm47fru5a35jzdlbfibbx3dm
by
Gaurav Bhatt, Aman Sharma, Shivam Sharma, Ankush Nagpal,
Balasubramanian Raman, Ankush Mittal
2017
Abstract
Identifying the veracity of a news article is an interesting problem while
automating this process can be a challenging task. Detection of a news article
as fake is still an open question as it is contingent on many factors which the
current state-of-the-art models fail to incorporate. In this paper, we explore
a subtask to fake news identification, and that is stance detection. Given a
news article, the task is to determine the relevance of the body and its claim.
We present a novel idea that combines the neural, statistical and external
features to provide an efficient solution to this problem. We compute the
neural embedding from the deep recurrent model, statistical features from the
weighted n-gram bag-of-words model and handcrafted external features with the
help of feature engineering heuristics. Finally, using deep neural layer all
the features are combined, thereby classifying the headline-body news pair as
agree, disagree, discuss, or unrelated. We compare our proposed technique with
the current state-of-the-art models on the fake news challenge dataset. Through
extensive experiments, we find that the proposed model outperforms all the
state-of-the-art techniques including the submissions to the fake news
challenge.
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