Combining Neural, Statistical and External Features for Fake News Stance Identification

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. 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.

[1]  Isabelle Augenstein,et al.  A simple but tough-to-beat baseline for the Fake News Challenge stance detection task , 2017, ArXiv.

[2]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[3]  Andreas Vlachos,et al.  Emergent: a novel data-set for stance classification , 2016, NAACL.

[4]  Jun Zhang,et al.  Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection , 2017, ArXiv.

[5]  Lei Yu,et al.  Deep Learning for Answer Sentence Selection , 2014, ArXiv.

[6]  Barbara Poblete,et al.  Predicting information credibility in time-sensitive social media , 2013, Internet Res..

[7]  Maarten Versteegh,et al.  Learning Text Similarity with Siamese Recurrent Networks , 2016, Rep4NLP@ACL.

[8]  Stephen Pfohl.  Stance Detection for the Fake News Challenge with Attention and Conditional Encoding , 2017 .

[9]  Preslav Nakov,et al.  SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings , 2016, *SEMEVAL.

[10]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[11]  Jimmy J. Lin,et al.  Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks , 2015, EMNLP.

[12]  Arkaitz Zubiaga,et al.  SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours , 2017, *SEMEVAL.

[13]  Hung-Yu Kao,et al.  IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification , 2017, *SEMEVAL.

[14]  Bowen Zhou,et al.  Applying deep learning to answer selection: A study and an open task , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[15]  Richard Davis.  Fake News , Real Consequences : Recruiting Neural Networks for the Fight Against Fake News , 2017 .

[16]  Kalina Bontcheva,et al.  USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders , 2016, *SEMEVAL.

[17]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[18]  Bowen Zhou,et al.  LSTM-based Deep Learning Models for non-factoid answer selection , 2015, ArXiv.

[19]  Yi Yang,et al.  WikiQA: A Challenge Dataset for Open-Domain Question Answering , 2015, EMNLP.

[20]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[21]  Kalina Bontcheva,et al.  Stance Detection with Bidirectional Conditional Encoding , 2016, EMNLP.

[22]  W. Bruce Croft,et al.  Beyond Factoid QA: Effective Methods for Non-factoid Answer Sentence Retrieval , 2016, ECIR.

[23]  Arkaitz Zubiaga,et al.  Discourse-aware rumour stance classification in social media using sequential classifiers , 2017, Inf. Process. Manag..