Infodemics, often including rumors, stigma, and conspiracy theories, have been common during the COVID-19 pandemic. Monitoring social media data has been identified as the best method for tracking rumors in real time and as a possible way to dispel misinformation and reduce stigma. However, the detection, assessment, and response to rumors, stigma, and conspiracy theories in real time are a challenge. Therefore, we followed and examined COVID-19-related rumors, stigma, and conspiracy theories circulating on online platforms, including fact-checking agency websites, Facebook, Twitter, and online newspapers, and their impacts on public health. Information was extracted between December 31, 2019 and April 5, 2020, and descriptively analyzed. We performed a content analysis of the news articles to compare and contrast data collected from other sources. We identified 2,311 reports of rumors, stigma, and conspiracy theories in 25 languages from 87 countries. Claims were related to illness, transmission and mortality (24%), control measures (21%), treatment and cure (19%), cause of disease including the origin (15%), violence (1%), and miscellaneous (20%). Of the 2,276 reports for which text ratings were available, 1,856 claims were false (82%). Misinformation fueled by rumors, stigma, and conspiracy theories can have potentially serious implications on the individual and community if prioritized over evidence-based guidelines. Health agencies must track misinformation associated with the COVID-19 in real time, and engage local communities and government stakeholders to debunk misinformation.

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COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis.

The current Coronavirus Disease (COVID-19) outbreak has affected over 200 countries including Nigeria It is one of the largest respiratory disease outbreaks affecting several countries simultaneously and a novel strain of Coronavirus (SARS-CoV 2) has been identified as the causative agent Sequel to the advice of the International Health Regulation Emergency Committee, the Director-General of WHO declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 and characterized it as a pandemic on 11 March 2020 The aim of the study was to describe the current situation of the outbreak in Nigeria and argued the need for effective engagement of community health workers for an appropriate response to COVID-19 We reviewed published articles on COVID-19 and daily epidemiological reports from the website of the Nigeria Centre for Disease Control (NCDC) from 27 February 2020 till 3 May 2020 (Epidemiology week 7 – 17) to describe the outbreak We also reviewed ongoing responses by the government and other relevant agencies Our findings revealed possible evidence of ongoing and increasing community transmission of COVID-19 infections, inadequate testing capacity and overwhelming of health resources Our review also revealed infection of several health workers in the face of existing critical skilled health workforce shortage With surging of new COVID-19 cases and a huge number of contacts to be traced, we recommended that the government needs to promptly bring community health workers on board, deploy rapid epidemic intelligence and scale up the use of mobile Apps for contact tracing This will result in an effective and coordinated response to the ongoing outbreak, sustain routine health services especially at the community level, reduce morbidity and mortality, and preserve health indices gains already made in the health system

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COVID-19 Outbreak Situation in Nigeria and the Need for Effective Engagement of Community Health Workers for Epidemic Response

Background: Ghana developed an Emergency Preparedness and Response Plan (EPRP) in response to the Severe Acute Respiratory Syndrome Coronavirus (SARS CoV-2) pandemic. A key strategy in the EPRP is to mobilize national resources and put in place strategies for improved risk and behavioral change communication. Nonetheless, concerns have been raised on social media about COVID-19 misinformation and misconceptions. This study used social media content to determine the types, forms and the effects of the myths, misconceptions and misinformation in Ghana's COVID-19 containment. Method: The study was conducted in three phases involving the use of both primary and secondary data. Review of social media information on COVID-19 was done. This was complemented with document review and interviews with key stakeholders with expertise in the management of public health emergencies and mass communication experts (N = 18). All interviews were transcribed verbatim and analyzed using NVivo 12. Results: The study showed a changing pattern in the misconceptions and misinformation about COVID-19. Initially myths were largely on causes and vulnerability. It was widely speculated that black people had some immunity against COVID-19. Also, the condition was perceived to cause severe disease among the elderly. These misconceptions served as risk attenuators among Ghanaians, especially the younger generation. As the infection evolved in the country, another misconception emerged that the hot climate in Africa inhibited viral replication and transmission only to be followed by speculations and conjectures that COVID-19 was being used as a biological weapon to target developed economics. For the management of COVID-19, the use of local remedies such as Neem tree (Azadirachta indica) and herbal preparation also emerged. Myths about the efficacy of locally manufactured gin (akpeteshie) and hydroxychloroquine as prophylaxis led to abuse of such substances. Interview segments revealed the use of myths to propagate political agenda in the country. Conclusion: The study concludes that COVID-19 misconceptions and misinformation are widespread and cover the course of the condition. These myths necessitate culturally sensitive health communication strategies that take into account local perceptions of COVID-19 in order to tackle the circulation of misconceived messages about the pandemic in Ghana.

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Misconceptions, Misinformation and Politics of COVID-19 on Social Media: A Multi-Level Analysis in Ghana

Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its impact. Identifying personal health mentions (PHM) is the first step of social media public health surveillance. It aims to identify whether a person's health condition is mentioned in a tweet, and it serves as a crucial method in tracking pandemic conditions in real time. However, social media texts contain noise, many creative and novel phrases, sarcastic emoji expressions, and misspellings. In addition, the class imbalance issue is usually very serious. To address these challenges, we built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and we proposed a dual convolutional neural network (CNN) framework using this dataset. An auxiliary CNN in the dual CNN structure provides supplemental information for the primary CNN in order to detect PHMs from tweets more effectively. The experiment shows that the proposed structure could alleviate the effect of class imbalance and could achieve promising results. This automated approach could monitor public health in real time and save disease-prevention departments from the tedious manual work in public health surveillance. 

https://doi.org/10.1016/j.eswa.2022.117139

COVID-19 personal health mention detection from tweets using dual convolutional neural network

Since the start of COVID-19, several relevant corpora from various sources are presented in the literature that contain millions of data points. While these corpora are valuable in supporting many analyses on this specific pandemic, researchers require additional benchmark corpora that contain other epidemics to facilitate cross-epidemic pattern recognition and trend analysis tasks. During our other efforts on COVID-19 related work, we discover very little disease related corpora in the literature that are sizable and rich enough to support such cross-epidemic analysis tasks. In this paper, we present EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the properties of the corpus with statistics of key terms and hashtags and trends analysis for each subset. Finally, we demonstrate the value and impact that EPIC30M could create through a discussion of multiple use cases of cross-epidemic research topics that attract growing interest in recent years. These use cases span multiple research areas, such as epidemiological modeling, pattern recognition, natural language understanding and economical modeling.

EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets

First reported in March 2014, an Ebola epidemic impacted West Africa, most notably Liberia, Guinea and Sierra Leone. We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic. We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately and then aggregates the set of alerts generated by the architecture. Using a dataset of tweets posted from the affected region from 2011 to 2014, we obtain alerts in December 2013, which is three months prior to the official announcement of the epidemic. Among the two variations, the second, which produces a restricted but useful set of alerts, can potentially be applied to other infectious disease surveillance and alert systems.

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Sheng-Lun Jason Yan

Papers

Automated monitoring of tweets for early detection of the 2014 Ebola epidemic.