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COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

Usman Naseem, Imran Razzak, Matloob Khushi, Peter W. Eklund, Jinman Kim
2021 IEEE Transactions on Computational Social Systems  
The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers.  ...  This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter.  ...  Different algorithmic models are used to train and validate the data set to provide the baselines for detecting sentiment related to prospective COVID-19 treatments spread on Twitter.  ... 
doi:10.1109/tcss.2021.3051189 fatcat:kdbxet73cjcqbknd5zx5dz2bey

A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Stelios Andreadis, Gerasimos Antzoulatos, Thanassis Mavropoulos, Panagiotis Giannakeris, Grigoris Tzionis, Nick Pantelidis, Konstantinos Ioannidis, Anastasios Karakostas, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
2021 Zenodo  
posted images, and a community detection approach to identify communities of Twitter users.  ...  We present and evaluate a deep learning localisation technique that geotags posts based on the locations mentioned in their text, a face detection algorithm to estimate the number of people appearing in  ...  Acknowledgements This work was supported by the EU's Horizon 2020 research and innovation programme under grant agreements H2020-883484 PathoCERT, H2020-883284 7SHIELD and H2020-832876 aqua3S.  ... 
doi:10.5281/zenodo.4696381 fatcat:nb4ypzttxfejxi56cl4tukefde

Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society [article]

Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani (+5 others)
2021 arXiv   pre-print
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.  ...  To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests  ...  We also want to thank the Atlantic Club in Bulgaria and DataBee for their support for the Bulgarian annotations.  ... 
arXiv:2005.00033v5 fatcat:pqmx6nl22jay7kvoiuunq4hrp4

Towards Explainable Fact Checking [article]

Isabelle Augenstein
2021 arXiv   pre-print
This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine  ...  Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data.  ...  Danish academic system required to obtain the degree of Doctor Scientiarum, in form and function equivalent to the French and German Habilitation and the Higher Doctorate of the Commonwealth.  ... 
arXiv:2108.10274v2 fatcat:5s4an6irezcjfmvvhmiaeqarh4

COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process

Liviu-Adrian Cotfas, Camelia Delcea, Rareș Gherai
2021 International Journal of Environmental Research and Public Health  
A stance analysis was conducted after comparing several classical machine learning and deep learning algorithms.  ...  The tweets associated to COVID-19 vaccination hesitancy were examined in connection with the major events in the analyzed period, while the main discussion topics were determined using hashtags, n-grams  ...  Acknowledgments: The work is supported by a grant from the Romanian Ministry of Research and Innovation, UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0800/86PCCDI/2018-FutureWeb, within PNCDI III.  ... 
doi:10.3390/ijerph181910438 pmid:34639738 fatcat:oyoebr536nbl5fzd3ur5fvyr5q

Towards Fairness-aware Disaster Informatics: An Interdisciplinary Perspective

Y. Yang, C. Zhang, C. Fan, A. Mostafavi, X. Hu
2020 IEEE Access  
The authors would like to acknowledge the funding support from the National Academies' Gulf Research Program Early-Career Research Fellowship.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation and National Academies  ...  [35] first built a binary support vector machine classifier using text-based features in Twitter posts to filter tweets referring to a disaster.  ... 
doi:10.1109/access.2020.3035714 fatcat:xyofuz2qu5fcjn75tcipcrzolu

Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP) [article]

Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo, Chih-Hsuan Wei, Shankai Yan, Zhiyong Lu
2021 arXiv   pre-print
We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection.  ...  The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.  ...  Acknowledgement This research is supported by the NIH Intramural Research Program, National Library of Medicine. Literature Cited  ... 
arXiv:2010.16413v2 fatcat:cxzxmnnbfrednpto4zkpnemltm

Directions in abusive language training data, a systematic review: Garbage in, garbage out

Bertie Vidgen, Leon Derczynski, Natalia Grabar
2020 PLoS ONE  
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness  ...  Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out.  ...  Acknowledgments The authors thank Mr. Alex Harris for providing research assistance and feedback. Author Contributions Conceptualization: Bertie Vidgen, Leon Derczynski.  ... 
doi:10.1371/journal.pone.0243300 pmid:33370298 fatcat:o42xcoc5ybarjac7opss6cizti

Spread Mechanism and Influence Measurement of Online Rumors in China During the COVID-19 Pandemic [article]

Yiou Lin, Hang Lei, Yu Deng
2021 arXiv   pre-print
Finally, we discuss how to use deep learning technology to reduce the forecast loss by using the Bidirectional Encoder Representations from Transformers (BERT) model.  ...  We designed several rumor features and used the above two coefficients as predictable labels.  ...  By combining machine learning and the representation of rumors, Raveena et al. investigated in retrospect a dataset in which tweets spreading rumor detection was completed machine learning algorithms,  ... 
arXiv:2012.02446v2 fatcat:q3gryoao3rdcnclsgqdgl62hpe

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science [article]

Huimin Chen, Cheng Yang, Xuanming Zhang, Zhiyuan Liu, Maosong Sun, Jianbin Jin
2021 arXiv   pre-print
To explore the answer, we give a thorough review of data representations in CSS for both text and network.  ...  However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS?  ...  Notably, the process from data to representations or representations to task formalizations usually requires the involvement of machine learning methods.  ... 
arXiv:2106.14198v1 fatcat:dvy5awnfuvbnnkzusjl5wbhfki

Neural Entity Linking: A Survey of Models Based on Deep Learning [article]

Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann
2021 arXiv   pre-print
Our goal is to systemize design features of neural entity linking systems and compare their performance to the prominent classic methods on common benchmarks.  ...  Finally, we briefly discuss applications of entity linking, focusing on the recently emerged use-case of enhancing deep pre-trained masked language models based on the transformer architecture.  ...  Acknowledgements The work was partially supported by a Deutscher Akademischer Austauschdienst (DAAD) doctoral stipend and the DFG-funded JOIN-T project BI 1544/4.  ... 
arXiv:2006.00575v3 fatcat:ra3kwc4tmbfhlmgtlevkcshcqq

The State of AI Ethics Report (June 2020) [article]

Abhishek Gupta
2020 arXiv   pre-print
Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as  ...  Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions.  ...  In the current dominant paradigm of supervised machine learning, the systems aren't truly autonomous, there is a huge amount of human input that goes into enabling the functioning of the system, and thus  ... 
arXiv:2006.14662v1 fatcat:q76dnqzh4ja5pofurjmpmyeyey

Ethical and social risks of harm from Language Models [article]

Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown (+11 others)
2021 arXiv   pre-print
The fourth considers risks from actors who try to use LMs to cause harm.  ...  The fifth focuses on risks specific to LLMs used to underpin conversational agents that interact with human users, including unsafe use, manipulation or deception.  ...  Acknowledgements The authors thank Phil Blunsom, Shane Legg, Jack Rae, Aliya Ahmad, Richard Ives, Shelly Bensal and Ben Zevenbergen for comments on earlier drafts of this report.  ... 
arXiv:2112.04359v1 fatcat:excmnsvm7fcm7aeze2pryaz7nq

Intersectional Bias in Causal Language Models [article]

Liam Magee, Lida Ghahremanlou, Karen Soldatic, Shanthi Robertson
2021 arXiv   pre-print
We conduct an experiment combining up to three social categories - gender, religion and disability - into unconditional or zero-shot prompts used to generate sentences that are then analysed for sentiment  ...  To address these difficulties, we suggest technical and community-based approaches need to combine to acknowledge and address complex and intersectional language model bias.  ...  It also has received support, in the form of researcher time and cloud computing credit, from Microsoft Corporation.  ... 
arXiv:2107.07691v1 fatcat:yt3ijvb6ena4hft5ffr7xkwapq

From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey [article]

Enrica Troiano and Aswathy Velutharambath and Roman Klinger
2021 arXiv   pre-print
Hence, our review shows how the groups relate to one another, and where specific styles, including some that have never been explored, belong in the hierarchy.  ...  They can, for instance, rephrase a formal letter in an informal way, convey a literal message with the use of figures of speech, edit a novel mimicking the style of some well-known authors.  ...  Acknowledgements This work was supported by Deutsche Forschungsgemeinschaft (project CEAT, KL 2869/1-2) and the Leibniz WissenschaftsCampus Tübingen "Cognitive Interfaces".  ... 
arXiv:2110.15871v2 fatcat:ddpowdm6pbazzd5mwl65nrge5q
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