OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19
Oguzhan Gencoglu
Machine Learning and Knowledge Extraction (2020) Vol. 2, Iss. 4, pp. 603-616
Open Access | Times Cited: 16

Showing 16 citing articles:

A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration
Juan M. Banda, Ramya Tekumalla, Guanyu Wang, et al.
Epidemiologia (2021) Vol. 2, Iss. 3, pp. 315-324
Open Access | Times Cited: 302

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets
Md. Shahriare Satu, Md. Imran Khan, Mufti Mahmud, et al.
Knowledge-Based Systems (2021) Vol. 226, pp. 107126-107126
Open Access | Times Cited: 88

COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning
Ebtesam Alomari, Iyad Katib, Aiiad Albeshri, et al.
International Journal of Environmental Research and Public Health (2021) Vol. 18, Iss. 1, pp. 282-282
Open Access | Times Cited: 86

Classification aware neural topic model for COVID-19 disinformation categorisation
Xingyi Song, Johann Petrak, Ye Jiang, et al.
PLoS ONE (2021) Vol. 16, Iss. 2, pp. e0247086-e0247086
Open Access | Times Cited: 52

Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
Nesrine Bannour, Sahar Ghannay, Aurélie Névéol, et al.
(2021)
Open Access | Times Cited: 46

Future Forecasting of COVID-19: A Supervised Learning Approach
Mujeeb Ur Rehman, Arslan Shafique, Sohail Khalid, et al.
Sensors (2021) Vol. 21, Iss. 10, pp. 3322-3322
Open Access | Times Cited: 32

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model
Karlo Babić, Milan Petrović, Slobodan Beliga, et al.
Applied Sciences (2021) Vol. 11, Iss. 21, pp. 10442-10442
Open Access | Times Cited: 22

A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration
Juan M. Banda, Ramya Tekumalla, Guanyu Wang, et al.
Zenodo (CERN European Organization for Nuclear Research) (2021)
Open Access | Times Cited: 13

Classification of COVID19 tweets using Machine Learning Approaches
Anupam Mondal, Sainik Kumar Mahata, Monalisa Dey, et al.
(2021), pp. 135-137
Closed Access | Times Cited: 6

Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model
Yunus Emre Karaca, Serpil Aslan
Computer Science (2021)
Open Access | Times Cited: 6

Language Scaling for Universal Suggested Replies Model
Qianlan Ying, Payal Bajaj, Budhaditya Deb, et al.
(2021), pp. 138-145
Open Access | Times Cited: 4

Leadership, Public Health Messaging, and Containment of Mobility in Mexico During the COVID-19 Pandemic
Sandra Aguilar-Gómez, Eva O. Arceo-Gómez, Elia De la Cruz Toledo, et al.
SSRN Electronic Journal (2021)
Open Access | Times Cited: 3

A Risk Communication Event Detection Model via Contrastive Learning
Mingi Shin, Sungwon Han, Sungkyu Park, et al.
(2020), pp. 39-43
Closed Access | Times Cited: 2

A Topic Modeling Method for Analyzes of Short-Text Data in Social Media Networks
Ian Macedo Maiwald Santos, Luciana Rech, Ricardo Moraes
EPiC series in computing (2022) Vol. 82, pp. 112-101
Open Access

A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets.
Elnaz Zafarani-Moattar, Mohammad Reza Kangavari, Amir Masoud Rahmani
arXiv (Cornell University) (2021)
Closed Access

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