GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media
Year: 2019
Type: conference-paper
Abstract: Social media plays a crucial role as the main resource news for information seekers online.However, the unmoderated feature of social media platforms lead to the emergence and spread of untrustworthy contents which harm individuals or even societies.Most of the current automated approaches for automatically determining the veracity of a rumor are not generalizable for novel emerging topics.This paper describes our hybrid system comprising rules and a machine learning model which makes use of replied tweets to identify the veracity of the source tweet.The proposed system in this paper achieved 0.435 F-Macro in stance classification, and 0.262 F-macro and 0.801 RMSE in rumor verification tasks in Task7 of Se-mEval 2019.
DOI: 10.18653/v1/s19-2195
Authors: Sardar Hamidian, Mona Diab
Institution: George Washington University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2954416637