ArticlePloS one2017
Rumor Detection over Varying Time Windows.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis.JMIR infodemiology · 2025Article
- Rumor management in public health: a system dynamics analysis based on social trust.Frontiers in public health · 2025Article
- Survival analysis of the duration of rumors during the COVID-19 pandemic.BMC public health · 2024Article
- A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media.Information systems frontiers : a journal of research and innovation · 2023Article
- Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning.Information systems frontiers : a journal of research and innovation · 2022Article
- LOCO: The 88-million-word language of conspiracy corpus.Behavior research methods · 2022Article
- A model to measure the spread power of rumors.Journal of ambient intelligence and humanized computing · 2022Article
- Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses.Journal of medical Internet research · 2022Article
- Misinformation, believability, and vaccine acceptance over 40 countries: Takeaways from the initial phase of the COVID-19 infodemic.PloS one · 2022Article
- Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network.PloS one · 2022Article
- Novel approaches to fake news and fake account detection in OSNs: user social engagement and visual content centric model.Social network analysis and mining · 2022Article
- Location and Language Independent Fake Rumor Detection Through Epidemiological and Structural Graph Analysis of Social Connections.Frontiers in artificial intelligence · 2022Article
- Combating multimodal fake news on social media: methods, datasets, and future perspective.Multimedia systems · 2022Article
- Content characteristics predict the putative authenticity of COVID-19 rumors.Frontiers in public health · 2022Article
- Emotions explain differences in the diffusion of true vs. false social media rumors.Scientific reports · 2021Article
- Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection.Applied soft computing · 2021Article
- Spreading predictability in complex networks.Scientific reports · 2021Article
- Real-Time Tracking of COVID-19 Rumors Using Community-Based Methods in Côte d'Ivoire.Global health, science and practice · 2021Article
- Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.PloS one · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows-from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.