COVID-19 Fake News in Brazilian Portuguese Language: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Patricia Takako Endo.

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically.

  • COVID-19
  • fake news
  • health misinformation

1. Introduction

The Internet is a major source of health information [1,2,3][1][2][3]. The public consumes health content and advice from a wide range of actors including public health agencies, corporations, healthcare professionals and increasingly influencers of all levels [2,4][2][4]. In the last decade, with the rise of social media, the volume and sources of health information have multiplied dramatically with an associated rate of propagation. Health information on social media is not subject to the same degree of filtering and quality control by professional gatekeepers common in either public health or commercial sources and is particularly prone to being out of date, incomplete and inaccurate [5]. Furthermore, there is extensive evidence that individuals and organisations promote health information that is contrary to accepted scientific evidence or public policy, and in extreme cases, is deceptive, unethical and misleading [6,7][6][7]. This is also true in the context of online communications relating to the COVID-19 pandemic [8,9][8][9]. Within two months of the disclosure of the first COVID-19 case in Wuhan, China, the Director General of the World Health Organisation (WHO) was prompted to declare: “We’re not just fighting an epidemic; we’re fighting an infodemic” [10]. During the first year of the pandemic, the WHO identified over 30 discrete topics that are the subject of misinformation in the COVID-19 discourse [11].
During the first year of the pandemic, Brazil was one of the global epicentres of the COVID-19 pandemic, both in terms of infections and deaths. From 3 January 2020 to 9 February 2021, the WHO reported 9,524,640 confirmed cases of COVID-19 and 231,534 COVID-19-related deaths in Brazil, the third highest rate in the world after the USA and India [12]. As most governments worldwide, the Brazilian government was blindsided by the rapid transmission and impact of COVID-19. For much of 2020, COVID-19 was a pandemic characterised by uncertainty in transmission, pathogenicity and strain-specific control options [13]. Against this backdrop, the Brazilian government had to balance disease mitigation through interventions such as social distancing, travel restrictions and closure of educational institutions and non-essential businesses with the effects they have on Brazilian society and economy. The success of such strategies depends on the effectiveness of government authorities executing both communication and enabling measures, and the response of individuals and communities [14]. Unfortunately, research suggests significant incongruities between advice offered by Brazilian federal government officials and public health agencies [15,16,17][15][16][17].
Even before the COVID-19 pandemic, Brazil faced challenges in health literacy levels [18] and increasing distrust in vaccines and vaccination [19]. It is established that humans can be both irrational and vulnerable when distinguishing between truth and falsehood [20]. This situation is exacerbated where truths and falsehoods are repeated by traditional sources of trustworthy information, namely the news media and government, and then shared and amplified by peers via social media. In Brazil, the threat of fake COVID-19 news resulted in the Ministry of Health launching an initiative, Saúde sem Fake News [21], in an effort to identify and counteract the spread of fake news. There have been no updates since June 2020. In the absence of adequate countermeasures to stem the rise of fake news and against the backdrop of conflicting communications from federal government and public health agencies, Cardoso et al. [18] described Brazil as “… a fertile field for misinformation that hinders adequate measures taken to mitigate COVID-19”.
The complexity of dealing with communication during a health crisis is quite high, as social media, compared with traditional media, is more difficult to monitor, track and analyse [22], and people can easily become misinformed [23]. Given this context, it is important to develop mechanisms to monitor and mitigate the dissemination of online fake news at scale [24]. To this end, machine learning and deep learning models provide a potential solution. However, most of the current literature and datasets are based on resource-rich languages, such as English, to train and test models, and studies with other languages, such as Portuguese, face many challenges to find or to produce benchmark datasets. OurThe focus is on fake news about COVID-19 in the Brazilian Portuguese language that circulated in Brazil during the period of January 2020 to February 2021. Portuguese is a pluricentric or polycentric language, in that it possesses more than one standard (national) variety, e.g., European Portuguese and Brazilian Portuguese, as well as African varieties. Furthermore, Brazilian Portuguese has been characterised as highly diglossic, i.e., it has a formal traditional form of the language, the so-called H-variant, and a vernacular form, the L-variant, as well as a wide range of dialects [25,26][25][26]. The COVID-19 pandemic introduced new terms and new public health concepts to the global linguistic repertoire, which in turn introduced a number of language challenges, not least problems related to the translation and use of multilingual terminology in public health information and medical research from dominant languages [27]. Consequently, building models based on English language translation which do not take into account the specific features of the Brazilian Portuguese language and the specific language challenges of COVID-19 are likely to be inadequate, thus motivating this work.

2. Background

Fake news has been defined both in broad and narrow terms and can be characterised by authenticity, intention and whether it is news at all [28]. The broad definition includes non-factual content that misleads the public (e.g., deceptive and false news, disinformation and misinformation), rumour and satire, amongst others [28]. The narrow definition focuses on intentionally false news published by a recognised news outlet [28]. Extent research focuses on differentiating between fake news and true news, and the types of actors that propagate fake news. This paper is focused on the former, i.e., the attributes of the fake news itself. As such, it is concerned with identifying fake news based on characteristics such as writing style and quality [29], word counts [30], sentiment [31] and topic-agnostic features (e.g., a large number of ads or a frequency of morphological patterns in text) [32]. As discussed in the Introduction, the Internet, and in particular social media, is transforming public health promotion, surveillance, public response to health crises, as well as tracking disease outbreaks, monitoring the spread of misinformation and identifying intervention opportunities [33,34][33][34]. The public benefits from improved and convenient access to easily available and tailored information in addition to the opportunity to potentially influence health policy [33,35][33][35]. It has had a liberating effect on individuals, enabling users to search for both health and vaccine-related content and exchange information, opinions and support [36,37][36][37]. Notwithstanding this, research suggests that there are significant concerns about information inaccuracy and potential risks associated with the use of inaccurate health information, amongst others [38,39,40][38][39][40]. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to mitigate disease outbreak, delay or result in failure to seek or continue legitimate medical treatment as well as interfere with sound public health policy and attempts to disseminate public health messages by undermining trust in health institutions [23,41][23][41]. Historically, the news media has played a significant role in Brazilian society [42]. However, traditional media has been in steady decline in the last decade against the backdrop of media distrust (due to perceived media bias and corruption) and the rise of the Internet and social media [43]. According to the Reuters Institute Digital News Report 2020 [44], the Internet (including social media) is the main source of news in Brazil. It is noteworthy that Brazil is one of a handful of countries where across all media sources the public prefers partial news, a factor that can create a false sense of uniformity and validity and foster the propagation of misinformation [44]. While Facebook is a source of misinformation concern in most countries worldwide, Brazil is relatively unique in that WhatsApp is a significant channel of news and misinformation [44]. This preference of partial news sources and social media in Brazil has lead to significant issues in the context of COVID-19. From the beginning of the COVID-19 pandemic, the WHO has reported on a wide variety of misinformation related to COVID-19 [11]. These include unsubstantiated claims and conspiracy theories related to hydroxychloroquine, reduced risk of infection, 5G mobile networks and sunny and hot weather, amongst others [11]. What differs in the Brazilian context is that the Brazilian public has been exposed to statements from the political elite, including the Brazilian President, that have contradicted the Brazilian Ministry of Health, pharmaceutical companies and health experts. Indeed, the political elite in Brazil have actively promoted many of the misleading claims identified by the WHO. This has included statements promoting erroneous information on the effects of COVID-19, “cures” and treatments unsupported by scientific evidence and an end to social distancing, amongst others [45]. These statements by government officials become news and lend legitimacy to them. As vaccines and vaccination programmes to mitigate COVID-19 become available, such statements sow mistrust in health systems but provide additional legitimacy to anti-vaccination movements that focus on similar messaging strategies, e.g., questioning the safety and effectiveness of vaccines, sharing conspiracy theories, publishing general misinformation and rumours, promoting that Big Pharma and scientific experts are not to be trusted, stating that civil liberties and human’s freedom of choice are endangered, questioning whether vaccinated individuals spread diseases and promoting alternative medicine [46,47,48][46][47][48]. While vaccines and vaccinations are a central building block of efforts to control and reduce the impact of COVID-19, vaccination denial and misinformation propagated by the anti-vaccination movement represents a tension between freedom of speech and public health. Social network platforms have been reluctant to intervene on this topic and on misinformation in general [49], however, there have been indicators that this attitude is changing, particularly in the context of COVID-19 [50]. However, even where there is a desire to curb misinformation by platforms, the identification of fake news and misinformation, in general, is labour intensive and particularly difficult to moderate on closed networks such as WhatsApp. To scale such monitoring requires automation. While over 282 million people speak Portuguese worldwide, commercial tools and research has overwhelmingly focused on the most popular languages, namely English and Chinese. This may be due to the concentration of Portuguese speakers in a relatively small number of countries. Over 73% of native Portuguese speakers are located in Brazil and a further 24% in just three other countries—Angola, Mozambique and Portugal [51]. As discussed earlier, it is important to note that Portuguese as a language is pluricentric and Brazilian Portuguese is highly diglossic, thus requiring native language datasets for accurate classification.

3. Current Works

Research on automated fake news detection typically falls in to two main categories, approaches based on knowledge, and those based on style [20]. Style-based fake news detection, the focus of this article, attattempts to analyse the writing style of the target article to identify whether there is an attempt to mislead the reader. These approaches typically rely on binary classification techniques to classify news as fake or not based on general textual features (lexicon, syntax, discourse, and semantic), latent textual features (word, sentence and document) and associated images [20]. These are typically based on data mining and information retrieval, natural language processing (NLP) and machine learning techniques, amongst others [20,52][20][52]. This study compares machine learning and deep learning techniques for fake news detection. There is well-established literature on the use of traditional machine learning for both knowledge-based and style-based detection. For example, naive Bayes [53[53][54],54], support vector machine (SVM) [54[54][55][56][57][58],55,56,57,58], Random Forest [59[59][60],60], and XGBoost [59,61][59][61] are widely cited in the literature. Similarly, a wide variety of deep learning techniques have been used including convolutional neural networks (CNNs) [62,63,64,65][62][63][64][65] long short term memory (LSTM) [66[66][67],67], recurrent neural networks (RNN) and general recurrent units (GRU) models [66[66][67][68],67,68], other deep learning neural networks architectures [69,70,71][69][70][71] and ensemble approaches [63,72,73][63][72][73]. While automated fake news detection has been explored in health and disease contexts, the volume of research has expanded rapidly since the commencement of the COVID-19 pandemic. While a comprehensive review of the literature is beyond the scope of this article, four significant trends are worthy of mention. Firstly, although some studies use a variety of news sources (e.g., [74]) and multi-source datasets such as CoAID [75], the majority of studies focus on data sets comprising social media data and specifically Twitter data, e.g., [76,77][76][77]. This is not wholly unsurprising as access to the Twitter API is easily accessible and the public data sets on the COVID-19 discourse have been made available, e.g., [78,79,80][78][79][80]. Secondly, though a wide range of machine learning and deep learning techniques feature in studies including CNNs, LSTMs and others, there is a notable increase in the use of bidirectional encoder representations from transformers (BERT) [74,76,77][74][76][77]. This can be explained by the relative recency and availability of BERT as a technique and early performance indicators. Thirdly, and related to the previous points, few datasets or research identified use a Brazilian Portuguese language corpus and a Brazilian empirical context. For example, the COVID-19 Twitter Chatter dataset features English, French, Spanish and German language data [79]. CoAID does not identify its language, but all sources and search queries identified are English language only. The Real Worry Dataset is English language only [80]. The dataset described in [78] does feature a significant portion of Portuguese tweets, however, none of the keywords used are in the Portuguese language and the data is Twitter only. Similarly, the MM-COVID dataset features 3981 fake news items and 7192 trustworthy items in six languages including Portuguese [81]. While Brazilian Portuguese is included, it would appear both European and Brazilian Portuguese are labelled as one homogeneous language, and the total number of fake Portuguese language items is relatively small (371). Notwithstanding the foregoing, there has been a small number of studies that explore fake news in the Brazilian context. Galhardi et al. [82] used data collected from the Eu Fiscalizo, a crowdsourcing tool where users can send content that they believe is inappropriate or fake. Analysis suggests that fake news about COVID-19 is primarily related to homemade methods of COVID-19 prevention or cure (85%), largely disseminated via WhatsApp [82]. While this study is consistent with other reports, e.g., [44], it comprises a small sample (154 items) and classification is based on self-reports. In line with [83[83][84],84], Garcia Filho et al. [85] examined temporal trends in COVID-19. Using Google Health Trends, they identified a sudden increase in interest in issues related to COVID-19 from March 2020 after the adoption of the first measures of social distance. Of specific interest to this paper is the suggestion by Garcia Filho et al. that unclear messaging between the President, State Governors and the Minister of Health may have resulted in a reduction in search volumes. Ceron et al. [86] proposed a new Markov-inspired method for clustering COVID-19 topics based on evolution across a time series. Using a dataset 5115 tweets published by two Brazilian fact-checking organisations, Aos Fatos and Agência Lupa, their data also suggested the data clearly revealed a complex intertwining between politics and the health crisis during the period under study. Fake news detection is a relatively new phenomenon. Monteiro et al. [87] presented the first reference corpus in Portuguese focused on fake news, Fake.Br corpus, in 2018. The Fake.Br. corpus comprises 7200 true and fake news items and was used to evaluate an SVM approach to automatically classify fake news messages. The SVM model achieved 89% accuracy using five-fold cross validation. Subsequently, the Fake.Br corpus was used to evaluate other techniques to detect fake news. For example, Silva et al. [88] compare the performance of six techniques to detect fake news, i.e., logistic regression, SVM, decision tree, Random Forest, bootstrap aggregating (bagging) and adaptive boosting (AdaBoost). The best F1 score, 97.1%, was achieved by logistic regression when stop words were not removed and the traditional bag-of-words (BoW) was applied to represent the text. Souza et al. [89] proposed a linguistic-based method based on grammatical classification, sentiment analysis and emotions analysis, and evaluated five classifiers, i.e., naive Bayes, AdaBoost, SVM, gradient boost (GB) and K-nearest neighbours (KNN) using the Fake.Br corpus. GB presented the best accuracy, 92.53%, when using emotion lexicons as complementary information for classification. Faustini et al. [90] also used the Fake.Br corpus and two other datasets, one comprising fake news disseminated via WhatsApp, as well as a dataset comprising tweets, to compare four different techniques in one-class classification (OCC)—SVM, document-class distance (DCD), EcoOCC (an algorithm based on k-means) and naive Bayes classifier for OCC. All algorithms performed similarly with the exception of the one-class SVM, which showed greater F-score variance. More recently, the Digital Lighthouse project at the Universidade Federal do Ceara in Brazil has published a number of studies and datasets relating to misinformation on WhatsApp in Brazil. These include FakeWhatsApp.BR [91] and COVID19.BR [92,93][92][93]. The FakeWhatsApp.BR dataset contains 282,601 WhatsApp messages from users and groups from all Brazilian states collected from 59 groups from July 2018 to November of 2018 [91]. The FakeWhatsApp.BR corpus contains 2193 messages labelled misinformation and 3091 messages labelled non-misinformation [91]. The COVID-19.BR contains messages from 236 open WhatsApp groups with at least 100 members collected from April 2020 to June 2020. The corpus contains 2043 messages, 865 labelled as misinformation and 1178 labelled as non-misinformation. Both datasets contain similar data, i.e., message text, time and date, phone number, Brazilian state, word count, character count and whether the message contained media [91,93][91][93]. Cabral et al. [91] combined classic natural language processing approaches for feature extraction with nine different machine learning classification algorithms to detect fake news on WhatsApp, i.e., logistic regression, Bernoulli, complement naive Bayes, SVM with a linear kernel (LSVM), SVM trained with stochastic gradient descent (SGD), SVM trained with an RBF kernel, K-nearest neighbours, Random Forest (RF), gradient boosting and a multilayer perceptron neural network (MLP). The best performing results were generated by MLP, LSVM and SGD, with a best F1 score of 0.73, however, when short messages were removed, the best performing F1 score rose to 0.87. Using the COVID19.BR dataset, Martins et al. [92] compared machine learning classifiers to detect COVID-19 misinformation on WhatsApp. Similar to their earlier work [91], they tested LSVM and MLP models to detect misinformation in WhatsApp messages, in this case related to COVID-19. Here, they achieved a highest F1 score of 0.778; an analysis of errors indicated errors occurred primarily due to short message length. In Martins et al. [93], they extend their work to detect COVID-19 misinformation in Brazilian Portuguese WhatsApp messages using bidirectional long–short term memory (BiLSTM) neural networks, pooling operations and an attention mechanism. This solution, called MIDeepBR, outperformed their previous proposal as reported in [92] with an F1 score of 0.834. In contrast with previous research, we build and present a new dataset comprising fake news in the Brazilian Portuguese language relating exclusively to COVID-19 in Brazil. In contrast with Martins et al. [92] and Cabral et al. [91], we do not use a WhatsApp dataset, which may due to its nature be dominated by L-variant Brazilian Portuguese. Furthermore, the dataset used in this study is over a longer period (12 months) compared with Martins et al. [92,93] and Cabral et al. [91]. Furthermore, unlike Li et al. [81], we specifically focus on the Brazilian Portuguese language as distinct from European or African variants. To this end, the scale of items in our dataset is significantly larger than, for example, the MM-COVID dataset. In addition to an exploratory data analysis of the content, we evaluate and compare machine learning and deep learning approaches for detecting fake news. In contrast with Martins et al. [93], we include gated recurrent units (GRUs) and evaluate both unidirectional and bidirectional GRUs and LSTMs, as well as machine learning classifiers.

References

  1. Bujnowska-Fedak, M.M.; Waligóra, J.; Mastalerz-Migas, A. The internet as a source of health information and services. In Advancements and Innovations in Health Sciences; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1–16.
  2. Ofcom. Online Nation 2020 Report. 2020. Available online: https://www.ofcom.org.uk/__data/assets/pdf_file/0027/196407/online-nation-2020-report.pdf (accessed on 24 January 2022).
  3. Eurobarometer Flash. European citizens’ digital health literacy. Rep. Eur. Comm. 2014.
  4. Lynn, T.; Rosati, P.; Leoni Santos, G.; Endo, P.T. Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter. Int. J. Environ. Res. Public Health 2020, 17, 8557.
  5. Sinapuelas, I.C.; Ho, F.N. Information exchange in social networks for health care. J. Consum. Mark. 2019, 36, 692–702.
  6. Allem, J.P.; Ferrara, E. Could social bots pose a threat to public health? Am. J. Public Health 2018, 108, 1005.
  7. Broniatowski, D.A.; Jamison, A.M.; Qi, S.; AlKulaib, L.; Chen, T.; Benton, A.; Quinn, S.C.; Dredze, M. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. Am. J. Public Health 2018, 108, 1378–1384.
  8. van der Linden, S.; Roozenbeek, J.; Compton, J. Inoculating against Fake News about COVID-19. Front. Psychol. 2020, 11, 2928.
  9. Evanega, S.; Lynas, M.; Adams, J.; Smolenyak, K.; Insights, C.G. Coronavirus Misinformation: Quantifying Sources and Themes in the COVID-19 ‘Infodemic’. Jmir Prepr. 2020. Available online: https://allianceforscience.cornell.edu/wp-content/uploads/2020/09/Evanega-et-al-Coronavirus-misinformationFINAL.pdf (accessed on 24 January 2022).
  10. WHO. Munich Security Conference. 2020. Available online: https://www.who.int/director-general/speeches/detail/munich-security-conference (accessed on 9 January 2021).
  11. Organisation, W.H. Coronavirus Disease (COVID-19) Advice for the Public: Mythbusters. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/myth-busters (accessed on 24 January 2022).
  12. WHO. COVID-19 in Brazil. 2020. Available online: https://covid19.who.int/region/amro/country/br (accessed on 9 January 2021).
  13. Endo, P.T.; Silva, I.; Lima, L.; Bezerra, L.; Gomes, R.; Ribeiro-Dantas, M.; Alves, G.; Monteiro, K.H.d.C.; Lynn, T.; Sampaio, V.d.S. # StayHome: Monitoring and benchmarking social isolation trends in Caruaru and the Região Metropolitana do Recife during the COVID-19 pandemic. Rev. Soc. Bras. Med. Trop. 2020, 53.
  14. Anderson, R.M.; Heesterbeek, H.; Klinkenberg, D.; Hollingsworth, T.D. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020, 395, 931–934.
  15. Ajzenman, N.; Cavalcanti, T.; Da Mata, D. More than Words: Leaders’ Speech and Risky Behavior during a Pandemic. 2020. Available online: https://ssrn.com/abstract=3582908 (accessed on 24 January 2022).
  16. Gugushvili, A.; Koltai, J.; Stuckler, D.; McKee, M. Votes, populism, and pandemics. Int. J. Public Health 2020, 65, 721–722.
  17. Mariani, L.; Gagete-Miranda, J.; Retti, P. Words can hurt: How political communication can change the pace of an epidemic. Covid Econ. 2020, 12, 104–137.
  18. Cardoso, C.R.d.B.; Fernandes, A.P.M.; Santos, I.K.F.d.M. What happens in Brazil? A pandemic of misinformation that culminates in an endless disease burden. Rev. Soc. Bras. Med. Trop. 2021, 54. Available online: https://www.scielo.br/j/rsbmt/a/x6z3v5bHDCKPvbdFD7CvY3f/?lang=en (accessed on 24 January 2022).
  19. de Figueiredo, A.; Simas, C.; Karafillakis, E.; Paterson, P.; Larson, H.J. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: A large-scale retrospective temporal modelling study. Lancet 2020, 396, 898–908.
  20. Zhou, X.; Zafarani, R. A survey of fake news: Fundamental theories, detection methods, and opportunities. Acm Comput. Surv. (CSUR) 2020, 53, 1–40.
  21. da Saúde Governo do Brasil, M. Novo Coronavírus Fake News. 2020. Available online: https://www.saude.gov.br/component/tags/tag/novo-coronavirus-fake-news (accessed on 27 July 2020).
  22. Ghenai, A.; Mejova, Y. Catching Zika fever: Application of crowdsourcing and machine learning for tracking health misinformation on Twitter. arXiv 2017, arXiv:1707.03778.
  23. Swire-Thompson, B.; Lazer, D. Public health and online misinformation: Challenges and recommendations. Annu. Rev. Public Health 2020, 41, 433–451.
  24. Zhang, X.; Ghorbani, A.A. An overview of online fake news: Characterization, detection, and discussion. Inf. Process. Manag. 2020, 57, 102025.
  25. Silva, R.V.M. Ensaios Para Uma Sócio-História do Português Brasileiro; Parábola Editorial: São Paulo, Brazil, 2004.
  26. da Silva, A. Measuring and parameterizing lexical convergence and divergence between European and Brazilian Portuguese. Adv. Cogn. Socioling. 2010, 45, 41.
  27. Piller, I.; Zhang, J.; Li, J. Linguistic diversity in a time of crisis: Language challenges of the COVID-19 pandemic. Multilingua 2020, 39, 503–515.
  28. Zhou, X.; Zafarani, R. Fake news: A survey of research, detection methods, and opportunities. arXiv 2018, arXiv:1812.00315.
  29. Undeutsch, U. Beurteilung der Glaubhaftigkeit von Aussagen (Evaluation of statement credibility. In Handbuch der Psychologie, Vol. 11: Forensische Psychologie; Undeutsch, U., Ed.; Hogrefe: Göttingen, Germany, 1967; pp. 26–181.
  30. McCornack, S.A.; Morrison, K.; Paik, J.E.; Wisner, A.M.; Zhu, X. Information manipulation theory 2: A propositional theory of deceptive discourse production. J. Lang. Soc. Psychol. 2014, 33, 348–377.
  31. Zuckerman, M.; DePaulo, B.M.; Rosenthal, R. Verbal and nonverbal communication of deception. In Advances in Experimental Social Psychology; Elsevier: Amsterdam, The Netherlands, 1981; Volume 14, pp. 1–59.
  32. Castelo, S.; Almeida, T.; Elghafari, A.; Santos, A.; Pham, K.; Nakamura, E.; Freire, J. A topic-agnostic approach for identifying fake news pages. In Proceedings of the Companion 2019 World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; pp. 975–980.
  33. Moorhead, S.A.; Hazlett, D.E.; Harrison, L.; Carroll, J.K.; Irwin, A.; Hoving, C. A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. J. Med. Internet Res. 2013, 15, e85.
  34. Chew, C.; Eysenbach, G. Pandemics in the age of Twitter: Content analysis of Tweets during the 2009 H1N1 outbreak. PLoS ONE 2010, 5, e14118.
  35. Kovic, I.; Lulic, I.; Brumini, G. Examining the medical blogosphere: An online survey of medical bloggers. J. Med. Internet Res. 2008, 10, e28.
  36. Zhao, Y.; Zhang, J. Consumer health information seeking in social media: A literature review. Health Inf. Libr. J. 2017, 34, 268–283.
  37. Mavragani, A.; Ochoa, G. The internet and the anti-vaccine movement: Tracking the 2017 EU measles outbreak. Big Data Cogn. Comput. 2018, 2, 2.
  38. Hughes, B.; Joshi, I.; Wareham, J. Health 2.0 and Medicine 2.0: Tensions and controversies in the field. J. Med. Internet Res. 2008, 10, e23.
  39. Van De Belt, T.H.; Engelen, L.J.; Berben, S.A.; Schoonhoven, L. Definition of Health 2.0 and Medicine 2.0: A systematic review. J. Med. Internet Res. 2010, 12, e18.
  40. Pagoto, S.; Waring, M.E.; Xu, R. A Call for a Public Health Agenda for Social Media Research. J. Med. Internet Res. 2019, 21, e16661.
  41. Bridgman, A.; Merkley, E.; Loewen, P.J.; Owen, T.; Ruths, D.; Teichmann, L.; Zhilin, O. The causes and consequences of COVID-19 misperceptions: Understanding the role of news and social media. Harv. Kennedy Sch. Misinform. Rev. 2020, 1. Available online: https://misinforeview.hks.harvard.edu/article/the-causes-and-consequences-of-covid-19-misperceptions-understanding-the-role-of-news-and-social-media/ (accessed on 24 January 2022).
  42. Matos, C. Journalism and Political Democracy in Brazil; Lexington Books: Lanham, MD, USA, 2008.
  43. Milhorance, F.; Singer, J. Media trust and use among urban news consumers in Brazil. Ethical Space Int. J. Commun. Ethics 2018, 15, 56–65.
  44. Reuters Institute. Digital News Report 2020. Available online: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/DNR_2020_FINAL.pdf (accessed on 10 January 2021).
  45. Ricard, J.; Medeiros, J. Using misinformation as a political weapon: COVID-19 and Bolsonaro in Brazil. Harv. Kennedy Sch. Misinf. Rev. 2020. Available online: https://misinforeview.hks.harvard.edu/article/using-misinformation-as-a-political-weapon-covid-19-and-bolsonaro-in-brazil/ (accessed on 29 March 2022).
  46. Johnson, N.F.; Velásquez, N.; Restrepo, N.J.; Leahy, R.; Gabriel, N.; El Oud, S.; Zheng, M.; Manrique, P.; Wuchty, S.; Lupu, Y. The online competition between pro-and anti-vaccination views. Nature 2020, 582, 230–233.
  47. Whitehead, M.; Taylor, N.; Gough, A.; Chambers, D.; Jessop, M.; Hyde, P. The anti-vax phenomenon. Vet. Rec. 2019, 184, 744.
  48. Kata, A. A postmodern Pandora’s box: Anti-vaccination misinformation on the Internet. Vaccine 2010, 28, 1709–1716.
  49. Helberger, N. The political power of platforms: How current attempts to regulate misinformation amplify opinion power. Digit. J. 2020, 8, 842–854.
  50. Sky. Coronavirus: Brazil President Refuses to Ramp Up COVID-19 Lockdown as Facebook Pulls Video. 2020. Available online: https://news.sky.com/story/coronavirus-brazil-president-refuses-to-ramp-up-covid-19-lockdown-as-facebook-pulls-video-11966279 (accessed on 10 January 2021).
  51. Central Intelligence Agency. The World Factbook—Country Comparison—Population. 2018. Available online: https://www.cia.gov/the-world-factbook/references/guide-to-country-comparisons/ (accessed on 10 January 2021).
  52. Oshikawa, R.; Qian, J.; Wang, W.Y. A survey on natural language processing for fake news detection. arXiv 2018, arXiv:1811.00770.
  53. Oraby, S.; Reed, L.; Compton, R.; Riloff, E.; Walker, M.; Whittaker, S. And that’s a fact: Distinguishing factual and emotional argumentation in online dialogue. arXiv 2017, arXiv:1709.05295.
  54. Aphiwongsophon, S.; Chongstitvatana, P. Detecting fake news with machine learning method. In Proceedings of the 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Rai, Thailand, 18–21 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 528–531.
  55. Singh, V.; Dasgupta, R.; Sonagra, D.; Raman, K.; Ghosh, I. Automated fake news detection using linguistic analysis and machine learning. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Washington, DC, USA, 5–8 July 2017; pp. 1–3.
  56. Pérez-Rosas, V.; Kleinberg, B.; Lefevre, A.; Mihalcea, R. Automatic detection of fake news. arXiv 2017, arXiv:1708.07104.
  57. Zhang, H.; Fan, Z.; Zheng, J.; Liu, Q. An improving deception detection method in computer-mediated communication. J. Netw. 2012, 7, 1811.
  58. Feng, S.; Banerjee, R.; Choi, Y. Syntactic stylometry for deception detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jeju Island, Korea, 8–14 July 2012; pp. 171–175.
  59. Zhou, X.; Jain, A.; Phoha, V.V.; Zafarani, R. Fake news early detection: A theory-driven model. Digit. Threat. Res. Pract. 2020, 1, 1–25.
  60. Hassan, N.; Arslan, F.; Li, C.; Tremayne, M. Toward automated fact-checking: Detecting check-worthy factual claims by claimbuster. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017; pp. 1803–1812.
  61. Reis, J.C.; Correia, A.; Murai, F.; Veloso, A.; Benevenuto, F. Explainable machine learning for fake news detection. In Proceedings of the 10th ACM Conference on Web Science, Boston, MA, USA, 30 June–3 July 2019; pp. 17–26.
  62. Kaliyar, R.K.; Goswami, A.; Narang, P.; Sinha, S. FNDNet–a deep convolutional neural network for fake news detection. Cogn. Syst. Res. 2020, 61, 32–44.
  63. Kumar, S.; Asthana, R.; Upadhyay, S.; Upreti, N.; Akbar, M. Fake news detection using deep learning models: A novel approach. Trans. Emerg. Telecommun. Technol. 2020, 31, e3767.
  64. Yang, Y.; Zheng, L.; Zhang, J.; Cui, Q.; Li, Z.; Yu, P.S. TI-CNN: Convolutional Neural Networks for Fake News Detection. arXiv 2018, arXiv:1806.00749v1.
  65. Wang, W.Y. “liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv 2017, arXiv:1705.00648.
  66. Girgis, S.; Amer, E.; Gadallah, M. Deep learning algorithms for detecting fake news in online text. In Proceedings of the 2018 13th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 18–19 December 2018; pp. 93–97.
  67. Bajaj, S. The Pope Has a New Baby! Fake News Detection Using Deep Learning. CS 224N. 2017, pp. 1–8. Available online: https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2710385.pdf (accessed on 24 January 2022).
  68. Shu, K.; Cui, L.; Wang, S.; Lee, D.; Liu, H. defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; pp. 395–405.
  69. O’Brien, N.; Latessa, S.; Evangelopoulos, G.; Boix, X. The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. 2018. Available online: https://cbmm.mit.edu/sites/default/files/publications/fake-news-paper-NIPS.pdf (accessed on 24 January 2022).
  70. Singhania, S.; Fernandez, N.; Rao, S. 3han: A deep neural network for fake news detection. In Proceedings of the International Conference on Neural Information Processing, Guangzhou, China, 14–18 November 2017; Springer: Berlin/Heidelberg, Germany, 2017; pp. 572–581.
  71. Thota, A.; Tilak, P.; Ahluwalia, S.; Lohia, N. Fake news detection: A deep learning approach. SMU Data Sci. Rev. 2018, 1, 10.
  72. Huang, Y.F.; Chen, P.H. Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms. Expert Syst. Appl. 2020, 159, 113584.
  73. Roy, A.; Basak, K.; Ekbal, A.; Bhattacharyya, P. A deep ensemble framework for fake news detection and classification. arXiv 2018, arXiv:1811.04670.
  74. Vijjali, R.; Potluri, P.; Kumar, S.; Teki, S. Two stage transformer model for COVID-19 fake news detection and fact checking. arXiv 2020, arXiv:2011.13253.
  75. Cui, L.; Lee, D. CoAID: COVID-19 Healthcare Misinformation Dataset. Soc. Inf. Netw. 2020, arXiv:2006.00885.
  76. Wani, A.; Joshi, I.; Khandve, S.; Wagh, V.; Joshi, R. Evaluating Deep Learning Approaches for COVID-19 Fake News Detection. arXiv 2021, arXiv:2101.04012.
  77. Glazkova, A.; Glazkov, M.; Trifonov, T. g2tmn at AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection. arXiv 2020, arXiv:2012.11967.
  78. Chen, E.; Lerman, K.; Ferrara, E. Tracking social media discourse about the covid-19 pandemic: Development of a public coronavirus twitter data set. JMIR Public Health Surveill. 2020, 6, e19273.
  79. Banda, J.M.; Tekumalla, R.; Wang, G.; Yu, J.; Liu, T.; Ding, Y.; Chowell, G. A large-scale COVID-19 Twitter chatter dataset for open scientific research—An international collaboration. Epidemiologia 2021, 2, 315–324.
  80. Kleinberg, B.; van der Vegt, I.; Mozes, M. Measuring emotions in the COVID-19 real world worry dataset. arXiv 2020, arXiv:2004.04225.
  81. Li, Y.; Jiang, B.; Shu, K.; Liu, H. MM-COVID: A multilingual and multimodal data repository for combating COVID-19 disinformation. arXiv 2020, arXiv:2011.04088.
  82. Galhardi, C.P.; Freire, N.P.; Minayo, M.C.d.S.; Fagundes, M.C.M. Fact or Fake? An analysis of disinformation regarding the COVID-19 pandemic in Brazil. Ciência Saúde Coletiva 2020, 25, 4201–4210.
  83. Pobiruchin, M.; Zowalla, R.; Wiesner, M. Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19–Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study. J. Med. Internet Res. 2020, 22, e19629.
  84. Oettershagen, L.; Kriege, N.M.; Morris, C.; Mutzel, P. Classifying Dissemination Processes in Temporal Graphs. Big Data 2020, 8, 363–378.
  85. Garcia Filho, C.; Vieira, L.J.E.d.S.; Silva, R.M.d. Buscas na internet sobre medidas de enfrentamento à COVID-19 no Brasil: Descrição de pesquisas realizadas nos primeiros 100 dias de 2020. Epidemiologia e Serviços de Saúde 2020, 29, e2020191.
  86. Ceron, W.; de Lima-Santos, M.F.; Quiles, M.G. Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content. Online Soc. Netw. Media 2020, 21, 100116.
  87. Monteiro, R.A.; Santos, R.L.; Pardo, T.A.; De Almeida, T.A.; Ruiz, E.E.; Vale, O.A. Contributions to the study of fake news in portuguese: New corpus and automatic detection results. In Proceedings of the International Conference on Computational Processing of the Portuguese Language, Canela, Brazil, 24–26 September 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 324–334.
  88. Silva, R.M.; Santos, R.L.; Almeida, T.A.; Pardo, T.A. Towards automatically filtering fake news in portuguese. Expert Syst. Appl. 2020, 146, 113199.
  89. de Souza, M.P.; da Silva, F.R.M.; Freire, P.M.S.; Goldschmidt, R.R. A Linguistic-Based Method that Combines Polarity, Emotion and Grammatical Characteristics to Detect Fake News in Portuguese. In Proceedings of the Brazilian Symposium on Multimedia and the Web, São Luís, Brazil, 30 November–4 December 2020; pp. 217–224.
  90. Faustini, P.; Covões, T.F. Fake News Detection Using One-Class Classification. In Proceedings of the 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil, 15–18 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 592–597.
  91. Cabral, L.; Monteiro, J.M.; da Silva, J.W.F.; Mattos, C.L.C.; Mourao, P.J.C. Fakewhastapp. br: NLP and machine learning techniques for misinformation detection in brazilian portuguese whatsapp messages. In Proceedings of the 23rd International Conference on Enterprise Information Systems, ICEIS, Online, 26–28 April 2021; pp. 26–28.
  92. Martins, A.D.F.; Cabral, L.; Mourao, P.J.C.; de Sá, I.C.; Monteiro, J.M.; Machado, J. COVID19. br: A dataset of misinformation about COVID-19 in brazilian portuguese whatsapp messages. In Anais do III Dataset Showcase Workshop; SBC: Porto Alegre, Brazil, 2021; pp. 138–147.
  93. Martins, A.D.F.; Cabral, L.; Mourao, P.J.C.; Monteiro, J.M.; Machado, J. Detection of misinformation about covid-19 in brazilian portuguese whatsapp messages using deep learning. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados; SBC: Porto Alegre, Brazil, 2021; pp. 85–96.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback