Multi-stage News-Stance Classification Based on Lexical and Neural Features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • Simad University, Mogadishu, Somalia

Abstract

The amount of fake news present on the internet poses a great challenge for many online communities including manual fact-checkers who struggle to prevent the spread of misinformation and its negative impact. Detecting the stance of a news article involves classifying its perspective (e.g. agree, disagree, discuss, or unrelated) to a particular claim or headline which could support human fact-checkers to determine the veracity of the claims. Prior work on fake-news stance detection has proposed one-stage multi-class classification solutions which have limited success in detecting related pairs due to imbalanced class distributions in the data. This paper describes an improved approach to the stance detection of Fake News Challenge (FNC-1) based on multi-stage feature-assisted Deep Learning approaches. We break down the multi-class classification problem into two-stage and three-stage classifiers by combining the lexical-overlap features with Deep Learning techniques in an effort to mitigate the class imbalance problem. The experimental results demonstrate that the proposed models improve upon the state-of-the-art Accuracy and F1 score for stance detection. We also experimentally show that our models achieve solid results on minority classes i.e. agree and disagree without using fine-tuning approach or adding more training samples.

Details

Original languageEnglish
Title of host publication13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020)
EditorsÁlvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado
Publication statusPublished - 28 Aug 2020
Event13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020) - Burgos, Spain
Duration: 16 Sep 202018 Sep 2020

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume1267
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020)
CountrySpain
CityBurgos
Period16/09/2018/09/20

Keywords

  • Text categorization, Stance Detection, Fake News