RV-ML: An Effective Rumor Verification Scheme Based on Multi-Task Learning Model

Qian Lv, Yufeng Wang, Bo Zhang, Qun Jin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Social platforms are full of rumors (i.e., unverified contents). Naturally, it is imperative but challenging to effectively determine the veracity of these rumors on popular social platforms. Previously deep learning based rumor verification schemes usually treat the issue as an independent and single task. Considering the rumor verification and stance classification are relevant tasks, we propose an effective Rumor verification scheme based on Multi-task learning Model, RV-ML, in which the shared long-short term memory (LSTM) layer for both rumor verification and stance classification can effectively deal with the sequential information for the original input, and generate macro-level virtual features, and the convolution neural network (CNN) layer uniquely designed for rumor verification task is used to mine local features from shared LSTM layer. Comparisons between our RV-ML and several typical rumor verification schemes on the real RumourEval and PHEME datasets demonstrate that our proposed scheme gains better performance for the task of rumor verification.

Original languageEnglish
Article number9146786
Pages (from-to)2527-2531
Number of pages5
JournalIEEE Communications Letters
Volume24
Issue number11
DOIs
Publication statusPublished - 2020 Nov

Keywords

  • multi-task learning
  • Rumor verification
  • stance classification

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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