research-article

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19

Authors:

Dong WangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2423 - 2433

https://doi.org/10.1145/3511808.3557263

Published: 17 October 2022 Publication History

Abstract

Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). Specifically, we leverage pseudo labeling to generate high-confidence target examples for joint training with source data. We additionally design a label correction component to estimate and correct the label shifts (i.e., class priors) between the source and target domains. Moreover, a contrastive adaptation loss is integrated in the objective function to reduce the intra-class discrepancy and enlarge the inter-class discrepancy. As such, the adapted model learns corrected class priors and an invariant conditional distribution across both domains for improved estimation of the target data distribution. To demonstrate the effectiveness of the proposed CANMD, we study the case of COVID-19 early misinformation detection and perform extensive experiments using multiple real-world datasets. The results suggest that CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.

Supplementary Material

MP4 File (CIKM22-fp0139.mp4)

Presentation video

Download
27.49 MB

References

[1]

Amr Alexandari, Anshul Kundaje, and Avanti Shrikumar. 2020. Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation. In International Conference on Machine Learning. PMLR, 222--232.

[2]

Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar. 2019. Regularized learning for domain adaptation under label shifts. arXiv preprint arXiv:1903.09734 (2019).

[3]

Cody Buntain and Jennifer Golbeck. 2017. Automatically identifying fake news in popular twitter threads. In 2017 IEEE International Conference on Smart Cloud (SmartCloud). IEEE, 208--215.

[4]

Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885 (2020).

[5]

Limeng Cui, Haeseung Seo, Maryam Tabar, Fenglong Ma, Suhang Wang, and Dongwon Lee. 2020. Deterrent: Knowledge guided graph attention network for detecting healthcare misinformation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 492--502.

Digital Library

[6]

Sourya Dipta Das, Ayan Basak, and Saikat Dutta. 2021. A heuristic-driven ensemble framework for COVID-19 fake news detection. In International Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation. Springer, 164--176.

[7]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186. https://doi.org/10.18653/v1/N19-1423

[8]

Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020. Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 4019--4028. https://doi.org/10.18653/v1/2020.acl-main.370

[9]

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Francc ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The journal of machine learning research, Vol. 17, 1 (2016), 2096--2030.

[10]

Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A Kernel Two-Sample Test. The Journal of Machine Learning Research, Vol. 13, 1 (2012), 723--773.

Digital Library

[11]

Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. In International Conference on Machine Learning. PMLR, 1321--1330.

[12]

Han Guo, Juan Cao, Yazi Zhang, Junbo Guo, and Jintao Li. 2018. Rumor detection with hierarchical social attention network. In Proceedings of the 27th ACM international conference on information and knowledge management. 943--951.

Digital Library

[13]

Kadhim Hayawi, Sakib Shahriar, Mohamed Adel Serhani, Ikbal Taleb, and Sujith Samuel Mathew. 2022. ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public health, Vol. 203 (2022), 23--30.

[14]

Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 Misinformation on Social Media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.nlpcovid19-2.11

[15]

Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on Multimedia. 795--816.

Digital Library

[16]

Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.

[17]

Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann. 2019. Contrastive adaptation network for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4893--4902.

[18]

Hamid Karimi and Jiliang Tang. 2019. Learning Hierarchical Discourse-level Structure for Fake News Detection. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 3432--3442. https://doi.org/10.18653/v1/N19-1347

[19]

Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. Mvae: Multimodal variational autoencoder for fake news detection. In The world wide web conference. 2915--2921.

[20]

Ziyi Kou, Lanyu Shang, Yang Zhang, and Dong Wang. 2022a. HC-COVID: A Hierarchical Crowdsource Knowledge Graph Approach to Explainable COVID-19 Misinformation Detection. Proceedings of the ACM on Human-Computer Interaction, Vol. 6, GROUP (2022), 1--25.

Digital Library

[21]

Ziyi Kou, Lanyu Shang, Yang Zhang, Christina Youn, and Dong Wang. 2021. 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.

[22]

Ziyi Kou, Lanyu Shang, Yang Zhang, Zhenrui Yue, Huimin Zeng, and Dong Wang. 2022b. Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection. In IJCAI.

[23]

Yichuan Li, Kyumin Lee, Nima Kordzadeh, Brenton Faber, Cameron Fiddes, Elaine Chen, and Kai Shu. 2021. 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.

[24]

Zachary Lipton, Yu-Xiang Wang, and Alexander Smola. 2018. Detecting and correcting for label shift with black box predictors. In International conference on machine learning. PMLR, 3122--3130.

[25]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).

[26]

Yang Liu and Yi-Fang Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.

[27]

Zhenghao Liu, Chenyan Xiong, Maosong Sun, and Zhiyuan Liu. 2020. Fine-grained Fact Verification with Kernel Graph Attention Network. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7342--7351. https://doi.org/10.18653/v1/2020.acl-main.655

[28]

Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015. Learning transferable features with deep adaptation networks. In International conference on machine learning. PMLR, 97--105.

[29]

Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu. 2013. Transfer feature learning with joint distribution adaptation. In Proceedings of the IEEE international conference on computer vision. 2200--2207.

Digital Library

[30]

Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan. 2017. Deep transfer learning with joint adaptation networks. In International conference on machine learning. PMLR, 2208--2217.

[31]

Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon Hegelich. 2020. 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. Association for Computational Linguistics, Online. https://aclanthology.org/2020.nlpcovid19-acl.17

[32]

Martin Müller, Marcel Salathé, and Per E Kummervold. 2020. Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter. arXiv preprint arXiv:2005.07503 (2020).

[33]

Jeff Z Pan, Siyana Pavlova, Chenxi Li, Ningxi Li, Yangmei Li, and Jinshuo Liu. 2018. Content based fake news detection using knowledge graphs. In International semantic web conference. Springer, 669--683.

[34]

Parth Patwa, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, and Tanmoy Chakraborty. 2021. Fighting an infodemic: Covid-19 fake news dataset. In International Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation. Springer, 21--29.

[35]

Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. 2018. Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence.

[36]

Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018. DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 22--32. https://doi.org/10.18653/v1/D18-1003

[37]

Md Tahmid Rashid and Dong Wang. 2021. CovidSens: a vision on reliable social sensing for COVID-19. Artificial intelligence review, Vol. 54, 1 (2021), 1--25.

[38]

Megan Risdal. 2016. Getting Real about Fake News. https://doi.org/10.34740/KAGGLE/DSV/911

[39]

Kevin Roitero, Michael Soprano, Beatrice Portelli, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, and Gianluca Demartini. 2020. The covid-19 infodemic: Can the crowd judge recent misinformation objectively?. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1305--1314.

Digital Library

[40]

Jon Roozenbeek, Claudia R Schneider, Sarah Dryhurst, John Kerr, Alexandra LJ Freeman, Gabriel Recchia, Anne Marthe Van Der Bles, and Sander Van Der Linden. 2020. Susceptibility to misinformation about COVID-19 around the world. Royal Society open science, Vol. 7, 10 (2020), 201199.

[41]

Lanyu Shang, Ziyi Kou, Yang Zhang, Jin Chen, and Dong Wang. 2022b. A Privacy-aware Distributed Knowledge Graph Approach to QoIS-driven COVID-19 Misinformation Detection. In 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS). IEEE, 1--10.

[42]

Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. 2021. A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 899--908.

[43]

Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang. 2022a. A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection. In Proceedings of the ACM Web Conference 2022. 3623--3631.

Digital Library

[44]

Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019a. defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 395--405.

Digital Library

[45]

Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data, Vol. 8, 3 (2020), 171--188.

[46]

Kai Shu, Suhang Wang, and Huan Liu. 2019b. Beyond news contents: The role of social context for fake news detection. In Proceedings of the twelfth ACM international conference on web search and data mining. 312--320.

Digital Library

[47]

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. Fever: a large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355 (2018).

[48]

Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7167--7176.

[49]

Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014).

[50]

Nguyen Vo and Kyumin Lee. 2020. Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 7717--7731. https://doi.org/10.18653/v1/2020.emnlp-main.621

[51]

William Yang Wang. 2017a. "liar, liar pants on fire": A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017).

[52]

William Yang Wang. 2017b. "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Vancouver, Canada, 422--426. https://doi.org/10.18653/v1/P17-2067

[53]

Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining. 849--857.

Digital Library

[54]

Brian Xu, Mitra Mohtarami, and James Glass. 2019. Adversarial domain adaptation for stance detection. arXiv preprint arXiv:1902.02401 (2019).

[55]

Zhenrui Yue, Bernhard Kratzwald, and Stefan Feuerriegel. 2021. Contrastive Domain Adaptation for Question Answering using Limited Text Corpora. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 9575--9593. https://doi.org/10.18653/v1/2021.emnlp-main.754

[56]

Huimin Zeng, Zhenrui Yue, Yang Zhang, Ziyi Kou, Lanyu Shang, and Dong Wang. 2022. On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks. In IJCAI.

[57]

Tong Zhang, Di Wang, Huanhuan Chen, Zhiwei Zeng, Wei Guo, Chunyan Miao, and Lizhen Cui. 2020. BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection. In 2020 international joint conference on neural networks (IJCNN). IEEE, 1--8.

[58]

Honghao Zhou, Tinghuai Ma, Huan Rong, Yurong Qian, Yuan Tian, and Najla Al-Nabhan. 2022. MDMN: Multi-task and Domain Adaptation based Multi-modal Network for early rumor detection. Expert Systems with Applications, Vol. 195 (2022), 116517.

Digital Library

[59]

Han Zou, Jianfei Yang, and Xiaojian Wu. 2021. Unsupervised Energy-based Adversarial Domain Adaptation for Cross-domain Text Classification. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, 1208--1218. https://doi.org/10.18653/v1/2021.findings-acl.103

Cited By

Huang YGao MShu KLin CWang JZhou W(2024)EML: Emotion-Aware Meta Learning for Cross-Event False Information DetectionACM Transactions on Knowledge Discovery from Data10.1145/366148518:8(1-25)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3661485
Zhang YZong RShang LZeng HYue ZWang DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence AssessmentProceedings of the ACM Web Conference 202410.1145/3589334.3645519(2497-2508)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645519
Wu JXu WLiu QWu SWang L(2024)Adversarial Contrastive Learning for Evidence-Aware Fake News Detection With Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334164036:11(5591-5604)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341640
Show More Cited By

Index Terms

Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Mutual Domain Adaptation
Highlights
- We tackle a realistic problem setting of domain adaptation, where most domains are label-deficient and need to be helped and recent data become more sparsely labeled which makes the learning even more difficult.
- To tackle this problem, ...
Abstract
To solve the label sparsity problem, domain adaptation has been well-established, suggesting various methods such as finding a common feature space of different domains using projection matrices or neural networks. Despite recent advances, domain ...
Semi-supervised adversarial discriminative domain adaptation
Abstract
Domain adaptation is a potential method to train a powerful deep neural network across various datasets. More precisely, domain adaptation methods train the model on training data and test that model on a completely separate dataset. The ...
Contrasting augmented features for domain adaptation with limited target domain data
Abstract
Domain adaptation aims to alleviate distribution gaps between source and target domains. However, when the available target domain data are scarce for training, learning generalizable representations for domain adaptation is challenging. We ...
Highlights
- We study a challenging but realistic setting that the target domain data are limited.
- We propose a novel method which generates and contrasts augmented features.
- Extensive experiments demonstrate the superiority of our method.
- ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '22

Sponsor:

CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

Acceptance Rates

CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
460
Total Downloads

Downloads (Last 12 months)111
Downloads (Last 6 weeks)14

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Huang YGao MShu KLin CWang JZhou W(2024)EML: Emotion-Aware Meta Learning for Cross-Event False Information DetectionACM Transactions on Knowledge Discovery from Data10.1145/366148518:8(1-25)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1145/3661485
Zhang YZong RShang LZeng HYue ZWang DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence AssessmentProceedings of the ACM Web Conference 202410.1145/3589334.3645519(2497-2508)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645519
Wu JXu WLiu QWu SWang L(2024)Adversarial Contrastive Learning for Evidence-Aware Fake News Detection With Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334164036:11(5591-5604)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341640
Gong YShang LWang D(2024)Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and ChallengesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340423611:5(6705-6726)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3404236
Shi JZhao XZhang NLei YMin L(2024)Rough-Fuzzy Graph Learning Domain Adaptation for Fake News DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.331218211:4(5275-5286)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3312182
Zeng HYue ZJiang QZhang YShang LZong RWang D(2024)Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00078(785-796)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00078
Yang XWang YZhang XWang SWang HLam K(2024)UPDATE: Mining User-News Engagement Patterns for Dual-Target Cross-Domain Fake News Detection2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722833(1-10)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722833
Toney-Wails ASingh L(2024)Let Me Generate That for You: Generative Data Augmentation for Misinformation Detection in Low-Resource Environments2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722790(1-11)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722790
Lin HMa JYang RYang ZCheng M(2024)Towards low-resource rumor detectionNeurocomputing10.1016/j.neucom.2024.127438578:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127438
Chatterjee SBhattacharjee SDas ABanerjee S(2024)Imbalanced COVID-19 vaccine sentiment classification with synthetic resampling coupled deep adversarial active learningMachine Learning10.1007/s10994-024-06562-7113:10(8027-8059)Online publication date: 15-Jul-2024
https://doi.org/10.1007/s10994-024-06562-7
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents