A large-scale analysis of Persian Tweets regarding Covid-19 vaccination
Tóm tắt
Từ khóa
Tài liệu tham khảo
Barbieri F, Camacho-Collados J, Neves L, et al. (2020) Tweeteval: Unified benchmark and comparative evaluation for tweet classification. CoRR abs/2010.12421. arXiv:2010.12421
Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
Bonnevie E, Goldbarg J, Gallegos-Jeffrey AK et al. (2020) Content themes and influential voices within vaccine opposition on twitter. Am J Public Health 110(S3):S326–S330. https://doi.org/10.2105/AJPH.2020.305901
Bonnevie E, Gallegos-Jeffrey A, Goldbarg J et al. (2021) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. J Commun Healthcare 14(1):12–19. https://doi.org/10.1080/17538068.2020.1858222
Bonnevie E, Gallegos-Jeffrey A, Goldbarg J et al. (2021) Quantifying the rise of vaccine opposition on twitter during the covid-19 pandemic. J Commun Healthcare 14(1):12–19. https://doi.org/10.1080/17538068.2020.1858222
Cascini F, Pantovic A, Al-Ajlouni YA, et al. (2022) Social media and attitudes towards a covid-19 vaccination: A systematic review of the literature. EClinicalMedicine
Chang J, Gerrish S, Wang C et al. (2009) Reading tea leaves: How humans interpret topic models. In: Bengio Y, Schuurmans D, Lafferty J et al. (eds) Advances in Neural Information Processing Systems. Curran Associates Inc
Chopra H, Vashishtha A, Pal R, et al. (2021) Mining trends of COVID-19 vaccine beliefs on twitter with lexical embeddings. CoRR arXiv:2104.01131
Conneau A, Khandelwal K, Goyal N, et al. (2019) Unsupervised cross-lingual representation learning at scale. CoRR arXiv:1911.02116
Devlin J, Chang M, Lee K, et al. (2018) BERT: pre-training of deep bidirectional transformers for language understanding. CoRR arXiv:1810.04805
Dodds PS, Harris KD, Kloumann IM et al. (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLOS ONE 6(12):1–1. https://doi.org/10.1371/journal.pone.0026752
Durmaz N, Hengirmen E (2022) The dramatic increase in anti-vaccine discourses during the covid-19 pandemic: a social network analysis of twitter. Human Vaccin Immunotherap 18(1):2025
Gharachorloo M, Farahani M, Farahani M et al. (2021) Parsbert: transformer-based model for Persian language understanding. Neural Process Lett. https://doi.org/10.1007/s11063-021-10528-4
Filter J (2022) Functions to preprocess and normalize text. https://pypi.org/project/clean-text/, [Online; accessed 21-April-2022]
HAZM (2018) Python library for digesting Persian text. https://github.com/sobhe/hazm, [Online; accessed 21-April-2022]
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv:1503.02531
Hosseini P, Hosseini P, Broniatowski DA (2020) Content analysis of persian/farsi tweets during COVID-19 pandemic in iran using NLP. CoRR arXiv:2005.08400
Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media 8(1):216–225. https://ojs.aaai.org/index.php/ICWSM/article/view/14550
Khan S (2014) Qualitative research method: grounded theory. Int J Bus Manag. https://doi.org/10.5539/ijbm.v9n11p224
Kharazi V (2021) Persian Stop Words List. https://github.com/kharazi/persian-stopwords, [Online; accessed 21-April-2022]
Kwok SWH, Vadde SK, Wang G (2021) Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. J Med Internet Res 23(5):e26953
Lan Z, Chen M, Goodman S, et al. (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR. arXiv:1909.11942
Le TT, Andreadakis Z, Kumar A et al. (2020) The COVID-19 vaccine development landscape. Nat Rev Drug Discov 19(5):305–306. https://doi.org/10.1038/d41573-020-00073-5
Liu Y, Ott M, Goyal N, et al. (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR arXiv:1907.11692
Lyu H, Wang J, Wu W et al. (2022) Social media study of public opinions on potential covid-19 vaccines: informing dissent, disparities, and dissemination. Intell Med 2(1):1–12
Lyu JC, Han EL, Luli GK (2021) Covid-19 vaccine-related discussion on twitter: topic modeling and sentiment analysis. J Med Internet Res 23(6):e24435. https://doi.org/10.2196/24435
Newman D, Lau J, Grieser K, et al. (2010) Automatic evaluation of topic coherence. pp 100–108
Nezhad ZB, Deihimi MA (2022) Analyzing Iranian opinions toward covid-19 vaccination. IJID Regions. https://doi.org/10.1016/j.ijregi.2021.12.011
Organization WH (2021) Listings of WHO’s response to COVID-19. https://www.who.int/news/item/29-06-2020-covidtimeline, [Online; accessed 10-April-2022]
Sahu A, Gupta P, Chatterjee B (2014) Depression is more than just sadness: a case of excessive anger and its management in depression. Indian J Psychol Med 36(1):77–79
Sanh V, Debut L, Chaumond J, et al. (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR arXiv:1910.01108
Shokrollahi O, Hashemi N, Dehghani M (2021) Discourse analysis of covid-19 in persian twitter social networks using graph mining and natural language processing. CoRR arXiv:2109.00298
Smedt TD, Daelemans W (2012) Pattern for python. J Mach Learn Res 13(66):2063–2067
Thelwall M, Kousha K, Thelwall S (2021) Covid-19 vaccine hesitancy on english-language twitter. Profesional de la Información 30(2). https://doi.org/10.3145/epi.2021.mar.12,https://revista.profesionaldelainformacion.com/index.php/EPI/article/view/86322
Troiano G, Nardi A (2021) Vaccine hesitancy in the era of covid-19. Public Health 194:245–251. https://doi.org/10.1016/j.puhe.2021.02.025
Villavicencio C, Macrohon JJ, Inbaraj XA et al. (2021) Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information. https://doi.org/10.3390/info12050204
Wicke P, Bolognesi MM (2021) Covid-19 discourse on twitter: How the topics, sentiments, subjectivity, and figurative frames changed over time. Front Commun. https://doi.org/10.3389/fcomm.2021.651997
Yang Z, Dai Z, Yang Y, et al. (2019) Xlnet: Generalized autoregressive pretraining for language understanding. CoRR arXiv:1906.08237
Yin J, Wang J (2014) A dirichlet multinomial mixture model-based approach for short text clustering. Association for Computing Machinery, New York, NY, USA, KDD ’14, p 233–242, https://doi.org/10.1145/2623330.2623715
Yousefinaghani S, Dara R, Mubareka S, et al. (2021) An analysis of covid-19 vaccine sentiments and opinions on twitter. International Journal of Infectious Diseases 108:256–262. https://doi.org/10.1016/j.ijid.2021.05.059,https://www.sciencedirect.com/science/article/pii/S1201971221004628
Zacharias C (2020) Twitter Intelligence Tool. https://pypi.org/project/twint/, [Online; accessed 19-April-2022]