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The Importance of Readability in the Discussions of Monkeypox Misinformation on Twitter

https://doi.org/10.2139/ssrn.4636666
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-34111-3_36openalex-extracted
Mining the Discussion of Monkeypox Misinformation on Twitter Using RoBERTa
inferredcites →https://doi.org/10.1007/978-3-319-14977-6_47openalex-extracted
Using KNN and SVM Based One-Class Classifier for Detecting Online Radicalization on Twitter
inferredcites →https://doi.org/10.1007/s11192-022-04468-6openalex-extracted
User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis
inferredcites →https://doi.org/10.1007/s11999-014-4071-2openalex-extracted
Most American Academy of Orthopaedic Surgeons’ Online Patient Education Material Exceeds Average Patient Reading Level
inferredcites →https://doi.org/10.1007/s41060-021-00291-zopenalex-extracted
Characterizing and predicting fake news spreaders in social networks
inferredcites →https://doi.org/10.1007/s41060-021-00298-6openalex-extracted
Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic
inferredcites →https://doi.org/10.1016/j.chb.2017.03.005openalex-extracted
Exploring the effect of user engagement in online brand communities: Evidence from Twitter
inferredcites →https://doi.org/10.1016/j.gloenvcha.2015.03.006openalex-extracted
Network analysis reveals open forums and echo chambers in social media discussions of climate change
inferredcites →https://doi.org/10.1016/j.ijinfomgt.2021.102390openalex-extracted
Diffusion of real versus misinformation during a crisis event: A big data-driven approach
inferredcites →https://doi.org/10.1016/j.ipm.2021.102554openalex-extracted
Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19
inferredcites →https://doi.org/10.1016/j.jbusres.2019.01.071openalex-extracted
Say what? How the interplay of tweet readability and brand hedonism affects consumer engagement
inferredcites →https://doi.org/10.1016/j.jjimei.2022.100067openalex-extracted
How do text characteristics impact user engagement in social media posts: Modeling content readability, length, and hashtags number in Facebook
inferredcites →https://doi.org/10.1016/j.socscimed.2019.112552openalex-extracted
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media
inferredcites →https://doi.org/10.1017/cbo9781139013567openalex-extracted
Regression Analysis of Count Data
inferredcites →https://doi.org/10.1038/s41598-023-40399-9openalex-extracted
Dynamics and characteristics of misinformation related to earthquake predictions on Twitter
inferredcites →https://doi.org/10.1080/1369118x.2020.1759669openalex-extracted
Fake news practices in Indonesian newsrooms during and after the Palu earthquake: a hierarchy-of-influences approach
inferredcites →https://doi.org/10.1109/icaiic51459.2021.9415192openalex-extracted
An Empirical Comparison of BERT, RoBERTa, and Electra for Fact Verification
inferredcites →https://doi.org/10.1109/iccwamtip51612.2020.9317379openalex-extracted
Comparative Analyses of Bert, Roberta, Distilbert, and Xlnet for Text-Based Emotion Recognition
inferredcites →https://doi.org/10.1109/smc42975.2020.9283438openalex-extracted
Comparisons Between Text-Only and Multimedia Tweets on User Engagement
inferredcites →https://doi.org/10.1109/socialcom-passat.2012.128openalex-extracted
Credibility in Context: An Analysis of Feature Distributions in Twitter
inferredcites →https://doi.org/10.1109/wi-iat.2015.102openalex-extracted
Fake and Spam Messages: Detecting Misinformation During Natural Disasters on Social Media
inferredcites →https://doi.org/10.1126/science.aao2998openalex-extracted
The science of fake news
inferredcites →https://doi.org/10.1142/s0219691320500277openalex-extracted
A gradient boosted decision tree-based sentiment classification of twitter data
inferredcites →https://doi.org/10.1145/3485447.3512126openalex-extracted
The Impact of Twitter Labels on Misinformation Spread and User Engagement: Lessons from Trump’s Election Tweets
inferredcites →https://doi.org/10.1177/20563051221126051openalex-extracted
COVID-19 Conspiracy Theories Discussion on Twitter
inferredcites →https://doi.org/10.1177/20563051231183430openalex-extracted
Sharing Feelings and User Engagement on Twitter: It’s All About Me and You
inferredcites →https://doi.org/10.1785/0220200125openalex-extracted
Earthquakes and Coronavirus: How to Survive an Infodemic
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-main.55openalex-extracted
Reformulating Unsupervised Style Transfer as Paraphrase Generation
inferredcites →https://doi.org/10.18653/v1/w18-5446openalex-extracted
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
inferredcites →https://doi.org/10.2196/26527openalex-extracted
“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study
inferredcites →https://doi.org/10.2196/publichealth.6327openalex-extracted
“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets
inferredcites →https://doi.org/10.2328/jnds.34.3openalex-extracted
Utilization of Social Media in the East Japan Earthquake and Tsunami and its Effectiveness
inferredcites →https://doi.org/10.29007/d931openalex-extracted
Fake News Detection System: An implementation of BERT and Boosting Algorithm
inferredcites →https://doi.org/10.3390/ijerph19095126openalex-extracted
Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-extracted
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-extracted
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.48550/arxiv.1708.07104openalex-extracted
Automatic Detection of Fake News
inferredcites →https://doi.org/10.48550/arxiv.2005.07503openalex-extracted
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
inferredcites →https://doi.org/10.48550/arxiv.2005.13012openalex-extracted
Comparing BERT Against Traditional Machine Learning Models in Text Classification
inferredcites →https://doi.org/10.48550/arxiv.2107.07229openalex-extracted
Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task
inferredcites →https://doi.org/10.7717/peerj-cs.467openalex-extracted
Stance detection with BERT embeddings for credibility analysis of information on social media
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