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The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO.
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning
Jaejun Lee, Raphael Tang, and Jimmy Lin. 2019 · 1911
Earlier work this paper cites.
Towards Lingua Franca Named Entity Recognition with BERT
Taesun Moon, Parul Awasthy, Jian Ni, and Radu Florian. 2019 · 1912
Earlier work this paper cites.
Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0
Eric Hulburd. 2020 · 2002
Earlier work this paper cites.
What Happens To BERT Embeddings During Fine-tuning?
Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, and Ian Tenney. 2020 · 2004
Earlier work this paper cites.
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Eu Wern Teh, Terrance DeVries, and Graham W Taylor. 2020 · 2004
Earlier work this paper cites.
Learning under concept drift: an overview
Indrė Žliobaitė. 2010 · 2010
Earlier work this paper cites.
Probabilistic lipschitzness a niceness assumption for deterministic labels. In Learning Faster from Easy Data-Workshop NIPS , Vol. 2. 1
Ruth Urner and Shai Ben-David. 2013 · 2013
Earlier work this paper cites.
A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014 · 2014
Earlier work this paper cites.
Google Flu Trends still appears sick: An evaluation of the 2013-2014 flu season
David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014 · 2014
Earlier work this paper cites.
Accurate estimation of influenza epidemics using Google search data via ARGO
Shihao Yang, Mauricio Santillana, and Samuel C Kou. 2015 · 2015
Earlier work this paper cites.
Analysing patterns of spatial and niche overlap among species at multiple resolutions
Marcel Cardillo and Dan L. Warren. 2016 · 2016
Earlier work this paper cites.
No fuss distance metric learning using proxies. In Proceedings of the IEEE International Conference on Computer Vision . 360–368
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh. 2017 · 2017
Earlier work this paper cites.
Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases , Vol. 11. NIH Public Access, 269
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
Earlier work this paper cites.
Interpretable machine learning with reject option
Johannes Brinkrolf and Barbara Hammer. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
An introduction to domain adaptation and transfer learning
Wouter M Kouw and Marco Loog. 2018 · 2018
Earlier work this paper cites.
Selectivenet: A deep neural network with an integrated reject option. In International Conference on Machine Learning . PMLR, 2151–2159
Yonatan Geifman and Ran El-Yaniv. 2019 · 2019
Earlier work this paper cites.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 2019
Cited alongside, same era.
Concept drift adaptive physical event detection for social media streams. In World Congress on Services . Springer, 92–105
Abhijit Suprem, Aibek Musaev, and Calton Pu. 2019 · 2019
Cited alongside, same era.
Covid 19 Fake News Dataset
Isha Agarwal. 2020 · 2020
Cited alongside, same era.
Coaid: Covid-19 healthcare misinformation dataset
Limeng Cui and Dongwon Lee. 2020 · 2020
Cited alongside, same era.
The different forms of COVID-19 misinformation and their consequences
Adam M Enders, Joseph E Uscinski, Casey Klofstad, and Justin Stoler. 2020a · 2020
Cited alongside, same era.
A COVID-19 Rumor Dataset
Mingxi Cheng, Songli Wang, Xiaofeng Yan, Tianqi Yang, Wenshuo Wang, Zehao Huang, Xiongye Xiao, Shahin Nazarian, and Paul Bogdan. 2021 · 2021
Later among the works it cites.
A heuristic-driven uncertainty based ensemble framework for fake news detection in tweets and news articles
Sourya Dipta Das, Ayan Basak, and Saikat Dutta. 2021 · 2021
Later among the works it cites.
Machine Learning with a Reject Option: A survey
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, and Jesse Davis. 2021 · 2021
Later among the works it cites.
LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2021 · 2021
Later among the works it cites.
MCNNet: generalizing Fake News Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19 Dataset
Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang. 2021 · 2021
Later among the works it cites.
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The different forms of COVID-19 misinformation and their consequences
Adam M Enders, Joseph E Uscinski, Casey Klofstad, and Justin Stoler. 2020b · 2020
Cited alongside, same era.
COVIDLies: Detecting COVID-19 misinformation on social media
Tamanna Hossain, Robert L Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Characterizing covid-19 misinformation communities using a novel twitter dataset
Shahan Ali Memon and Kathleen M Carley. 2020 · 2020
Cited alongside, same era.
Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter
Martin Müller, Marcel Salathé, and Per E Kummervold. 2020 · 2020
Cited alongside, same era.
A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19
Ece C Mutlu, Toktam Oghaz, Jasser Jasser, Ege Tutunculer, Amirarsalan Rajabi, Aida Tayebi, Ozlem Ozmen, and Ivan Garibay. 2020 · 2020
Cited alongside, same era.
Beyond artificial reality: Finding and monitoring live events from social sensors
Calton Pu, Abhijit Suprem, Rodrigo Alves Lima, Aibek Musaev, De Wang, Danesh Irani, Steve Webb, and Joao Eduardo Ferreira. 2020 · 2020
Cited alongside, same era.
NLP-based feature extraction for the detection of COVID-19 misinformation videos on YouTube. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon Hegelich. 2020 · 2020
Cited alongside, same era.
Fakesens: A social sensing approach to covid-19 misinformation detection on social media. In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS) . IEEE, 140–147
Ziyi Kou, Lanyu Shang, Yang Zhang, Christina Youn, and Dong Wang. 2021 · 2021
Later among the works it cites.
Multi-Source Domain Adaptation with Weak Supervision for Early Fake News Detection. In 2021 IEEE International Conference on Big Data (Big Data) . IEEE, 668–676
Yichuan Li, Kyumin Lee, Nima Kordzadeh, Brenton Faber, Cameron Fiddes, Elaine Chen, and Kai Shu. 2021 · 2021
Later among the works it cites.
Data management opportunities for foundation models. In 12th Annual Conference on Innovative Data Systems Research
Laurel Orr, Karan Goel, and Christopher Ré. 2021 · 2021
Later among the works it cites.
Covid-19 Fake News Dataset (Kaggle)
Sameer Patel. 2021 · 2021
Later among the works it cites.
The Instagram infodemic: cobranding of conspiracy theories, coronavirus disease 2019 and authority-questioning beliefs
Emma K Quinn, Sajjad S Fazel, and Cheryl E Peters. 2021 · 2021
Later among the works it cites.
End-to-End Weak Supervision
Salva Rühling Cachay, Benedikt Boecking, and Artur Dubrawski. 2021 · 2021
Later among the works it cites.
Misinformation adoption or rejection in the era of covid-19. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), AAAI Press
Maxwell Weinzierl, Suellen Hopfer, and Sanda M Harabagiu. 2021 · 2021
Later among the works it cites.
Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
Mayee F Chen, Daniel Y Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré. 2022 · 2022
Closest in time.
Convergence of online k-means. In International Conference on Artificial Intelligence and Statistics . PMLR, 8534–8569
Geelon So, Gaurav Mahajan, and Sanjoy Dasgupta. 2022 · 2022
Closest in time.
Testing the generalization of neural language models for COVID-19 misinformation detection. In International Conference on Information . Springer, 381–392
Jan Philip Wahle, Nischal Ashok, Terry Ruas, Norman Meuschke, Tirthankar Ghosal, and Bela Gipp. 2022 · 2022
Closest in time.
Explainable Misinformation Detection Across Multiple Social Media Platforms
Rahee Walambe, Ananya Srivastava, Bhargav Yagnik, Mohammed Hasan, Zainuddin Saiyed, Gargi Joshi, and Ketan Kotecha. 2022 · 2022
Closest in time.