Predicting and analyzing the popularity of false rumors in Weibo

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 2024 United Kingdom English Publisher:Elsevier BVJournal:Expert Systems with Applications, volume 243, page 122,791 (issn: 0957-4174,

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Authors: Mu, Y.; Niu, P.; Bontcheva, K.; Aletras, N.;

doi: 10.1016/j.eswa.2023.122791 , 10.5281/zenodo.8374169 , 10.5281/zenodo.8374168

Predicting and analyzing the popularity of false rumors in Weibo

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Abstract

Anonymised Raw 19,256 Weibo Rumors. Paper: Predicting and Analyzing the Popularity of False Rumors in Weibo Authors: Yida Mu, Pu Niu, Kalina Bontcheva and Nikolaos Aletras. Pre-print will be available soon. import pandas as pd data=pd.read_csv('Weibo_Rumor_Popularity.tsv', sep='\t', lineterminator='\n') Yida Mu: (i) y.mu@sheffield.ac.uk (ii) yidamu.nlp@gmail.com

Country

United Kingdom

Related Organizations

Henan University
China (People's Republic of)
White Rose Consortium: University of Leeds; University of Sheffield; University of York
United Kingdom
University of Sheffield
United Kingdom

Keywords

Social media, Misinformation analysis, Rumor popularity, Weibo, Rumor Detection, Social Media

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%