@inproceedings{bertweet,
title     = {{BERTweet: A pre-trained language model for English Tweets}},
author    = {Dat Quoc Nguyen and Thanh Vu and Anh Tuan Nguyen},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
pages     = {9--14},
year      = {2020}
}

@misc{devlin2019bert,
      title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}, 
      author={Jacob Devlin and Ming-Wei Chang and Kenton Lee and Kristina Toutanova},
      year={2019},
      eprint={1810.04805},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{twitterDataUsage,
    title={How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read},
    author={Bernard Marr},
    year={2018}
}

@inproceedings{twitterNews,
author = {Kwak, Haewoon and Lee, Changhyun and Park, Hosung and Moon, Sue},
title = {What is Twitter, a Social Network or a News Media?},
year = {2010},
isbn = {9781605587998},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1772690.1772751},
doi = {10.1145/1772690.1772751},
booktitle = {Proceedings of the 19th International Conference on World Wide Web},
pages = {591–600},
numpages = {10},
keywords = {Twitter, online social network, information diffusion, influential, retweet, homophily, reciprocity, degree of separation, pagerank},
location = {Raleigh, North Carolina, USA},
series = {WWW '10}
}

@article{rumourMislead,
author = {Zhang, Liwei and Chen, Kelin and Jiang, He and Zhao, Ji},
title = {How the Health Rumor Misleads People’s Perception in a Public Health Emergency: Lessons from a Purchase Craze during the COVID-19 Outbreak in China},
JOURNAL = {International Journal of Environmental Research and Public Health},
volume = {17},
year = {2020},
number = {19},
article-number = {7213},
url = {https://www.mdpi.com/1660-4601/17/19/7213},
PubMedID = {33023103},
ISSN = {1660-4601},
doi = {10.3390/ijerph17197213}
}

@misc{zubiaga2015detecting,
      title={Towards Detecting Rumours in Social Media}, 
      author={Arkaitz Zubiaga and Maria Liakata and Rob Procter and Kalina Bontcheva and Peter Tolmie},
      year={2015},
      eprint={1504.04712},
      archivePrefix={arXiv},
      primaryClass={cs.SI}
}

@article{2016Election,
Author = {Allcott, Hunt and Gentzkow, Matthew},
Title = {Social Media and Fake News in the 2016 Election},
Journal = {Journal of Economic Perspectives},
Volume = {31},
Number = {2},
Year = {2017},
Month = {May},
Pages = {211-36},
DOI = {10.1257/jep.31.2.211},
URL = {https://www.aeaweb.org/articles?id=10.1257/jep.31.2.211}}

@article{2012Election,
author = {Jieun Shin and Lian Jian and Kevin Driscoll and François Bar},
title ={Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction},
journal = {New Media \& Society},
volume = {19},
number = {8},
pages = {1214-1235},
year = {2017},
doi = {10.1177/1461444816634054},
URL = {https://doi.org/10.1177/1461444816634054},
eprint = {https://doi.org/10.1177/1461444816634054}
}

@misc{li2021data,
      title={Data Augmentation Approaches in Natural Language Processing: A Survey}, 
      author={Bohan Li and Yutai Hou and Wanxiang Che},
      year={2021},
      eprint={2110.01852},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Zubiaga2019,
   title={Detection and Resolution of Rumours in Social Media},
   volume={51},
   ISSN={1557-7341},
   url={http://dx.doi.org/10.1145/3161603},
   DOI={10.1145/3161603},
   number={2},
   journal={ACM Computing Surveys},
   publisher={Association for Computing Machinery (ACM)},
   author={Zubiaga, Arkaitz and Aker, Ahmet and Bontcheva, Kalina and Liakata, Maria and Procter, Rob},
   year={2019},
   month={Mar},
   pages={1–36} 
}

@article{rumourDetectionLotfi,
author = {Lotfi, Serveh and Mirzarezaee, Mitra and Hosseinzadeh, Mehdi and Seydi, Vahid},
year = {2021},
month = {12},
pages = {},
title = {Rumor conversations detection in twitter through extraction of structural features},
volume = {22},
journal = {Information Technology and Management},
doi = {10.1007/s10799-021-00335-7}
}

@INPROCEEDINGS{CaiRumourDetection,
  author={Cai, Guoyong and Wu, Hao and Lv, Rui},
  booktitle={2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)}, 
  title={Rumors detection in Chinese via crowd responses}, 
  year={2014},
  volume={},
  number={},
  pages={912-917},
  doi={10.1109/ASONAM.2014.6921694}
}
@article{3162956,
author = {Tolmie, Peter and Procter, Rob and Rouncefield, Mark and Liakata, Maria and Zubiaga, Arkaitz},
title = {Microblog Analysis as a Program of Work},
year = {2018},
issue_date = {March 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {1},
number = {1},
issn = {2469-7818},
url = {https://doi.org/10.1145/3162956},
doi = {10.1145/3162956},
month = {jan},
articleno = {2},
numpages = {40},
keywords = {annotation, ethnomethodology, Conversation analysis, Twitter, rumor, turn-taking systems, microblogs}
}

@misc{ma2019nlpaug,
  title={NLP Augmentation},
  author={Edward Ma},
  howpublished={https://github.com/makcedward/nlpaug},
  year={2019}
}

@misc{dataAug,
      title={A Survey of Data Augmentation Approaches for NLP}, 
      author={Steven Y. Feng and Varun Gangal and Jason Wei and Sarath Chandar and Soroush Vosoughi and Teruko Mitamura and Eduard Hovy},
      year={2021},
      eprint={2105.03075},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{sennrich2016neural,
      title={Neural Machine Translation of Rare Words with Subword Units}, 
      author={Rico Sennrich and Barry Haddow and Alexandra Birch},
      year={2016},
      eprint={1508.07909},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{song2021fast,
      title={Fast WordPiece Tokenization}, 
      author={Xinying Song and Alex Salcianu and Yang Song and Dave Dopson and Denny Zhou},
      year={2021},
      eprint={2012.15524},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{https://doi.org/10.48550/arxiv.1807.03505,
  doi = {10.48550/ARXIV.1807.03505},
  url = {https://arxiv.org/abs/1807.03505},
  author = {Cao, Juan and Guo, Junbo and Li, Xirong and Jin, Zhiwei and Guo, Han and Li, Jintao},
  keywords = {Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Automatic Rumor Detection on Microblogs: A Survey},
  publisher = {arXiv},
  year = {2018},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{https://doi.org/10.48550/arxiv.1712.07709,
  doi = {10.48550/ARXIV.1712.07709},
  url = {https://arxiv.org/abs/1712.07709},
  author = {Shu, Kai and Wang, Suhang and Liu, Huan},
  keywords = {Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Beyond News Contents: The Role of Social Context for Fake News Detection},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{10.5555/3061053.3061153,
author = {Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J. and Wong, Kam-Fai and Cha, Meeyoung},
title = {Detecting Rumors from Microblogs with Recurrent Neural Networks},
year = {2016},
isbn = {9781577357704},
publisher = {AAAI Press},
abstract = {Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence},
pages = {3818–3824},
numpages = {7},
location = {New York, New York, USA},
series = {IJCAI'16}
}

@article{Zhou_2020,
	doi = {10.1145/3395046},
	url = {https://doi.org/10.1145%2F3395046},
	year = 2020,
	month = {oct},
	publisher = {Association for Computing Machinery ({ACM})},
	volume = {53},
	number = {5},
	pages = {1--40},
	author = {Xinyi Zhou and Reza Zafarani},
	title = {A Survey of Fake News},
	journal = {{ACM} Computing Surveys}
}

@article{https://doi.org/10.48550/arxiv.1911.07199,
  doi = {10.48550/ARXIV.1911.07199},
  url = {https://arxiv.org/abs/1911.07199},
  author = {Li, Quanzhi and Zhang, Qiong and Si, Luo and Liu, Yingchi},
  keywords = {Information Retrieval (cs.IR), Computation and Language (cs.CL), Computers and Society (cs.CY), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Rumor Detection on Social Media: Datasets, Methods and Opportunities},
  publisher = {arXiv},
  year = {2019},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{wei-etal-2021-towards,     title = "Towards Propagation Uncertainty: Edge-enhanced {B}ayesian Graph Convolutional Networks for Rumor Detection",     author = "Wei, Lingwei  and       Hu, Dou  and       Zhou, Wei  and       Yue, Zhaojuan  and       Hu, Songlin",     booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",     month = aug,     year = "2021",     address = "Online",     publisher = "Association for Computational Linguistics",     url = "https://aclanthology.org/2021.acl-long.297",     doi = "10.18653/v1/2021.acl-long.297",     pages = "3845--3854",
}

@article{Kumar2021,
  doi = {10.1007/s11042-021-11340-x},
  url = {https://doi.org/10.1007/s11042-021-11340-x},
  year = {2021},
  month = aug,
  publisher = {Springer Science and Business Media {LLC}},
  author = {Akshi Kumar and M. P. S. Bhatia and Saurabh Raj Sangwan},
  title = {Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset},
  journal = {Multimedia Tools and Applications}
}

@misc{https://doi.org/10.48550/arxiv.2001.06362,
  doi = {10.48550/ARXIV.2001.06362},
  
  url = {https://arxiv.org/abs/2001.06362},
  
  author = {Bian, Tian and Xiao, Xi and Xu, Tingyang and Zhao, Peilin and Huang, Wenbing and Rong, Yu and Huang, Junzhou},
  
  keywords = {Social and Information Networks (cs.SI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Asghar2019,
  doi = {10.1007/s12652-019-01527-4},
  url = {https://doi.org/10.1007/s12652-019-01527-4},
  year = {2019},
  month = oct,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {12},
  number = {4},
  pages = {4315--4333},
  author = {Muhammad Zubair Asghar and Ammara Habib and Anam Habib and Adil Khan and Rehman Ali and Asad Khattak},
  title = {Exploring deep neural networks for rumor detection},
  journal = {Journal of Ambient Intelligence and Humanized Computing}
}

@inproceedings{wei-etal-2019-modeling,
    title = "Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity",
    author = "Wei, Penghui  and
      Xu, Nan  and
      Mao, Wenji",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-1485",
    doi = "10.18653/v1/D19-1485",
    pages = "4787--4798",
}

@inproceedings{Kochkina2018AllinoneML,
  title={All-in-one: Multi-task Learning for Rumour Verification},
  author={E. Kochkina and Maria Liakata and Arkaitz Zubiaga},
  booktitle={COLING},
  year={2018}
}

@inproceedings{ma-etal-2018-rumor,
    title = "Rumor Detection on {T}witter with Tree-structured Recursive Neural Networks",
    author = "Ma, Jing  and
      Gao, Wei  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1184",
    doi = "10.18653/v1/P18-1184",
    pages = "1980--1989",
}

@inproceedings{enayet-el-beltagy-2017-niletmrg,
    title = "{N}ile{TMRG} at {S}em{E}val-2017 Task 8: Determining Rumour and Veracity Support for Rumours on {T}witter.",
    author = "Enayet, Omar  and
      El-Beltagy, Samhaa R.",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S17-2082",
    doi = "10.18653/v1/S17-2082",
    pages = "470--474",
}

@inproceedings{10.1145/2350190.2350203,
author = {Yang, Fan and Liu, Yang and Yu, Xiaohui and Yang, Min},
title = {Automatic Detection of Rumor on Sina Weibo},
year = {2012},
isbn = {9781450315463},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2350190.2350203},
doi = {10.1145/2350190.2350203},
abstract = {The problem of gauging information credibility on social networks has received considerable attention in recent years. Most previous work has chosen Twitter, the world's largest micro-blogging platform, as the premise of research. In this work, we shift the premise and study the problem of information credibility on Sina Weibo, China's leading micro-blogging service provider. With eight times more users than Twitter, Sina Weibo is more of a Facebook-Twitter hybrid than a pure Twitter clone, and exhibits several important characteristics that distinguish it from Twitter. We collect an extensive set of microblogs which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Sina Weibo. Unlike previous studies on Twitter where the labeling of rumors is done manually by the participants of the experiments, the official nature of this service ensures the high quality of the dataset. We then examine an extensive set of features that can be extracted from the microblogs, and train a classifier to automatically detect the rumors from a mixed set of true information and false information. The experiments show that some of the new features we propose are indeed effective in the classification, and even the features considered in previous studies have different implications with Sina Weibo than with Twitter. To the best of our knowledge, this is the first study on rumor analysis and detection on Sina Weibo.},
booktitle = {Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics},
articleno = {13},
numpages = {7},
keywords = {rumor detection, classification, Sina Weibo},
location = {Beijing, China},
series = {MDS '12}
}

@misc{https://doi.org/10.48550/arxiv.2204.09371,
  doi = {10.48550/ARXIV.2204.09371},
  
  url = {https://arxiv.org/abs/2204.09371},
  
  author = {Zhu, Dawei and Hedderich, Michael A. and Zhai, Fangzhou and Adelani, David Ifeoluwa and Klakow, Dietrich},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{https://doi.org/10.48550/arxiv.2003.12932,
  doi = {10.48550/ARXIV.2003.12932},
  
  url = {https://arxiv.org/abs/2003.12932},
  
  author = {Kumar, Ankit and Makhija, Piyush and Gupta, Anuj},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Noisy Text Data: Achilles' Heel of BERT},
  
  publisher = {arXiv},
  
  year = {2020},
  
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

@InProceedings{10.1007/978-981-16-2597-8_8,
author="Patel, Shaswat
and Shah, Binil
and Kaur, Preeti",
editor="Khanna, Ashish
and Gupta, Deepak
and Bhattacharyya, Siddhartha
and Hassanien, Aboul Ella
and Anand, Sameer
and Jaiswal, Ajay",
title="Leveraging User Comments in Tweets for Rumor Detection",
booktitle="International Conference on Innovative Computing and Communications",
year="2022",
publisher="Springer Singapore",
address="Singapore",
pages="87--99",
isbn="978-981-16-2597-8"
}

@book{bird2009natural,
  title={Natural language processing with Python: analyzing text with the natural language toolkit},
  author={Bird, Steven and Klein, Ewan and Loper, Edward},
  year={2009},
  publisher={" O'Reilly Media, Inc."}
}

@misc{ma2019nlpaug,
  title={NLP Augmentation},
  author={Edward Ma},
  howpublished={https://github.com/makcedward/nlpaug},
  year={2019}
}

@article{Buda_2018,
	doi = {10.1016/j.neunet.2018.07.011},
  
	url = {https://doi.org/10.1016%2Fj.neunet.2018.07.011},
  
	year = 2018,
	month = {oct},
  
	publisher = {Elsevier {BV}
},
  
	volume = {106},
  
	pages = {249--259},
  
	author = {Mateusz Buda and Atsuto Maki and Maciej A. Mazurowski},
  
	title = {A systematic study of the class imbalance problem in convolutional neural networks},
  
	journal = {Neural Networks}
}

@InProceedings{10.1007/978-981-16-2594-7_38,
author="Sharma, Shweta
and Gosain, Anjana
and Jain, Shreya",
editor="Khanna, Ashish
and Gupta, Deepak
and Bhattacharyya, Siddhartha
and Hassanien, Aboul Ella
and Anand, Sameer
and Jaiswal, Ajay",
title="A Review of the Oversampling Techniques in Class Imbalance Problem",
booktitle="International Conference on Innovative Computing and Communications",
year="2022",
publisher="Springer Singapore",
address="Singapore",
pages="459--472",
isbn="978-981-16-2594-7"
}

@inproceedings{gosain_handling_2017,
	title = {Handling class imbalance problem using oversampling techniques: {A} review},
	shorttitle = {Handling class imbalance problem using oversampling techniques},
	doi = {10.1109/ICACCI.2017.8125820},
	booktitle = {2017 {International} {Conference} on {Advances} in {Computing}, {Communications} and {Informatics} ({ICACCI})},
	author = {Gosain, Anjana and Sardana, Saanchi},
	month = sep,
	year = {2017},
	keywords = {ADASYN, BORDERLINE SMOTE, class imbalance problem, Classification algorithms, Diabetes, Heart, Oversampling, Prediction algorithms, Radar, SAFE LEVEL SMOTE, Sensitivity, SMOTE},
	pages = {79--85},
	file = {IEEE Xplore Abstract Record:/Users/s0p0df5/Zotero/storage/Y8G3R9EI/citations.html:text/html},
}

@inproceedings{pytorch-geometric,
  title={Fast Graph Representation Learning with {PyTorch Geometric}},
  author={Fey, Matthias and Lenssen, Jan E.},
  booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
  year={2019},
}

@inproceedings{pytorch,
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {8024--8035},
publisher = {Curran Associates, Inc.},
title = {{PyTorch: An Imperative Style, High-Performance Deep Learning Library}},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf},
year = {2019}
}

@ARTICLE{Ma2021-ro,
  title     = "Improving rumor detection by promoting information campaigns
               with transformer-based generative adversarial learning",
  author    = "Ma, Jing and Li, Jun and Gao, Wei and Yang, Yang and Wong,
               Kam-Fai",
  journal   = "IEEE Trans. Knowl. Data Eng.",
  publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
  pages     = "1--1",
  year      =  2021
}

@inproceedings{10.1145/3477495.3531930,
author = {Yang, Ruichao and Ma, Jing and Lin, Hongzhan and Gao, Wei},
title = {A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531930},
doi = {10.1145/3477495.3531930},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1761–1772},
numpages = {12},
keywords = {propagation tree, stance detection, hierarchical attention mechanism, mil, rumor verification},
location = {Madrid, Spain},
series = {SIGIR '22}
}

@inproceedings{lin-etal-2021-rumor,
    title = "Rumor Detection on {T}witter with Claim-Guided Hierarchical Graph Attention Networks",
    author = "Lin, Hongzhan  and
      Ma, Jing  and
      Cheng, Mingfei  and
      Yang, Zhiwei  and
      Chen, Liangliang  and
      Chen, Guang",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.786",
    doi = "10.18653/v1/2021.emnlp-main.786",
    pages = "10035--10047"
}

@inproceedings{Zubiaga2017ExploitingCF,
  title={Exploiting Context for Rumour Detection in Social Media},
  author={Arkaitz Zubiaga and Maria Liakata and Rob Procter},
  booktitle={SocInfo},
  year={2017}
}

@misc{https://doi.org/10.48550/arxiv.1812.08434,
  doi = {10.48550/ARXIV.1812.08434},
  url = {https://arxiv.org/abs/1812.08434},
  author = {Zhou, Jie and Cui, Ganqu and Hu, Shengding and Zhang, Zhengyan and Yang, Cheng and Liu, Zhiyuan and Wang, Lifeng and Li, Changcheng and Sun, Maosong},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Graph Neural Networks: A Review of Methods and Applications},
  publisher = {arXiv},
  year = {2018},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{jalilov_emoji_nodate,
	title = {emoji: {Emoji} for {Python}},
	copyright = {BSD License},
	shorttitle = {emoji},
	url = {https://github.com/carpedm20/emoji/},
	urldate = {2022-10-31},
	author = {Jalilov, Kevin Wurster {and} Tahir, Taehoon Kim},
	keywords = {emoji, Internet - WWW/HTTP - Dynamic Content, Multimedia - Graphics - Presentation, Software Development - Libraries - Python Modules},
	file = {Snapshot:/Users/s0p0df5/Zotero/storage/ELEE2A64/emoji.html:text/html},
}

@misc{word2vec,
  doi = {10.48550/ARXIV.1301.3781},
  url = {https://arxiv.org/abs/1301.3781},
  author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Efficient Estimation of Word Representations in Vector Space},
  publisher = {arXiv},
  year = {2013},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{glove,
    title = "{G}lo{V}e: Global Vectors for Word Representation",
    author = "Pennington, Jeffrey  and
      Socher, Richard  and
      Manning, Christopher",
    booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
    month = oct,
    year = "2014",
    address = "Doha, Qatar",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D14-1162",
    doi = "10.3115/v1/D14-1162",
    pages = "1532--1543",
}

@ARTICLE{S_Punla2022-bl,
  title     = "Are we there yet?: An analysis of the competencies of {BEED}
               graduates of {BPSU-DC}",
  author    = "S. Punla, Candida and {https://orcid.org/ 0000-0002-1094-0018,
               cspunla@bpsu.edu.ph} and C. Farro, Rosemarie and
               {https://orcid.org/0000-0002-3571-2716, rcfarro@bpsu.edu.ph} and
               {Bataan Peninsula State University Dinalupihan, Bataan,
               Philippines}",
  journal   = "International Multidisciplinary Research Journal",
  publisher = "International Organization of Educators and Researchers Inc.",
  volume    =  4,
  number    =  3,
  pages     = "50--59",
  month     =  sep,
  year      =  2022
}

@ARTICLE{Wu2020-tw,
  title     = "Rumor detection based on propagation graph neural network with
               attention mechanism",
  author    = "Wu, Zhiyuan and Pi, Dechang and Chen, Junfu and Xie, Meng and
               Cao, Jianjun",
  journal   = "Expert Syst. Appl.",
  publisher = "Elsevier BV",
  volume    =  158,
  number    =  113595,
  pages     = "113595",
  month     =  nov,
  year      =  2020,
  keywords  = "Graph neural network; Representation learning; Rumor detection;
               Social network; Social security",
  language  = "en"
}

@ARTICLE{S_Punla2022-pb,
  title     = "Are we there yet?: An analysis of the competencies of {BEED}
               graduates of {BPSU-DC}",
  author    = "S. Punla, Candida and {https://orcid.org/ 0000-0002-1094-0018,
               cspunla@bpsu.edu.ph} and C. Farro, Rosemarie and
               {https://orcid.org/0000-0002-3571-2716, rcfarro@bpsu.edu.ph} and
               {Bataan Peninsula State University Dinalupihan, Bataan,
               Philippines}",
  journal   = "International Multidisciplinary Research Journal",
  publisher = "International Organization of Educators and Researchers Inc.",
  volume    =  4,
  number    =  3,
  pages     = "50--59",
  month     =  sep,
  year      =  2022
}

@INPROCEEDINGS{Rani2021-sd,
  title           = "A hybrid deep learning model based on {CNN-BiLSTM} for
                     rumor detection",
  booktitle       = "2021 6th International Conference on Communication and
                     Electronics Systems ({ICCES})",
  author          = "Rani, Neetu and Das, Praseniit and Bhardwaj, Amit Kumar",
  publisher       = "IEEE",
  month           =  jul,
  year            =  2021,
  conference      = "2021 6th International Conference on Communication and
                     Electronics Systems (ICCES)",
  location        = "Coimbatre, India"
}

@misc{gcn,
  doi = {10.48550/ARXIV.1609.02907},
  url = {https://arxiv.org/abs/1609.02907},
  author = {Kipf, Thomas N. and Welling, Max},
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Semi-Supervised Classification with Graph Convolutional Networks},
  publisher = {arXiv},
  year = {2016},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{gat,
  doi = {10.48550/ARXIV.1710.10903},
  url = {https://arxiv.org/abs/1710.10903},
  author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
  keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Graph Attention Networks},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{Wu_2021,
	doi = {10.1109/tnnls.2020.2978386},
	url = {https://doi.org/10.1109%2Ftnnls.2020.2978386},
	year = 2021,
	month = {jan},
  
	publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  
	volume = {32},
  
	number = {1},
  
	pages = {4--24},
  
	author = {Zonghan Wu and Shirui Pan and Fengwen Chen and Guodong Long and Chengqi Zhang and Philip S. Yu},
  
	title = {A Comprehensive Survey on Graph Neural Networks},
  
	journal = {{IEEE} Transactions on Neural Networks and Learning Systems}
}

%%============================================================================%%
%% while using chicago reference style, both abbreviated and expanded form of %%
%% author name format is acceptable. Refer below example for expanded form    %%
%%============================================================================%%

%%  author		= "{Cameron, Deborah}", - single author
%%  author		= "{Saito, Yukio} and {Hyuga, Hiroyuki}", - double author 

%%======================================%%
%% Example for author names with suffix %%
%%======================================%%

%%  author		= "{Price, R. A. Jr} and {Curry, N. {III}} and McCann, K. E. and 
%%					Fielding, J. L. and {Abercrombie, E. Jr}",
