Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach

Mutanga, Murimo Bethel; Abayomi, Abdultaofeek

doi:10.1080/20421338.2020.1817262

Cited by 76 publications

(61 citation statements)

References 30 publications

(25 reference statements)

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“…Social media platforms have been major contributors to the COVID-19 infodemic, overloading users with (mis)information 92 , such as the film 'Plandemic', which quickly gained millions of views 93 . Although not all social media posts containing a related hashtag (e.g., #5GCoronavirus) are actually supporting a conspiracy theory 94 and although some social media companies have responded to the proliferation of dangerous misinformation about COVID-19 on their platforms 95,96 , conspiratorial claims are among the most attention-grabbing content on social media [97][98][99] . Like other conspiracy theories, COVID-19 conspiracy theories shared over social media are transmitted more rapidly and reach broader audiences than fact-checking information 100,101 .…”

Section: Social Factorsmentioning

confidence: 99%

Antecedents and consequences of COVID-19 conspiracy beliefs: a systematic review

Mulukom¹,

Pummerer²,

Alper³

et al. 2020

Preprint

100

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COVID-19 conspiracy theories emerged almost immediately after the beginning of the pandemic, and the number of believers does not appear to decline. Believing in these theories can negatively affect adherence to safety guidelines and vaccination intentions, potentially endangering the lives of many. Thus, one part in successfully fighting the pandemic is to understand the antecedents and consequences of COVID-19 conspiracy beliefs, which are here presented in a rapid review summarizing research from more than 28 countries. We evaluate the contribution of individual difference variables (demographic variables, personality traits, coping with threat and uncertainty), beliefs, biases, and attitudes (epistemically suspect beliefs, thinking styles and cognitive biases, attitudes towards science), and social factors (group identities, trust in authorities, social media) to COVID-19 conspiracy theories. We discuss the consequences of COVID-19 conspiracy beliefs in regard to safeguarding behaviours (hygiene, distancing, and mask-wearing), self-centred (hoarding) and misguided behaviours (pseudoscientific practices), vaccination intentions, mental health and negative social consequences (e.g., discrimination and violence). Differences between countries as well as various conspiracy theories are considered. Summarising, we suggest that belief in COVID-19 conspiracy theories is boosted by low levels of trust in a context of threat and low levels of comprehensive, accessible information in a context of uncertainty and unknowns. We conclude that research is urgently needed to address potential interventions to (re-)establish trust and provide accessible information about COVID-19.

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Section: Social Factorsmentioning

confidence: 99%

Antecedents and consequences of COVID-19 conspiracy beliefs: a systematic review

Mulukom¹,

Pummerer²,

Alper³

et al. 2020

Preprint

100

View full text Add to dashboard Cite

show abstract

“…In general, existing studies have investigated people’s reactions towards COVID-19 vaccines, with a geographical emphasis on the United States [ 14 - 19 ]. Some papers have also studied other countries in the world, including China [ 20 ], South Africa [ 21 ], Australia [ 22 ], the United Kingdom [ 14 , 23 ], Canada [ 24 ], and Africa [ 25 ], and to a global scale [ 26 ]. However, the study period of these works is relatively limited to or predominantly focused on the early stage of the pandemic or up to the end of 2020.…”

Section: Introductionmentioning

confidence: 99%

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Hu¹,

Wang²,

Luo³

et al. 2021

J Med Internet Res

112

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Background The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

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“… Opinion Mining HPV [ 70 ] Twitter 184,214 (t) Nov 2015–Mar 2016. HPV vaccine Extract public opinions towards HPV vaccines Machine Learning HPV [ 71 ] Twitter 287,100 (t) Jan 2008–Dec 2017 HPV vaccine Analyze the opinions on HPV vaccination NLP Framework HPV [ 72 ] Scholarly Journals 44 (a) Before Dec 2018 HPV vaccine Examine how social media may impact HPV vaccine Content Analyses HPV [ 73 ] Twitter 68,000 (t) Mar 2020–Apr 2020 COVID-19 vaccine Discover what topical issues relating to the COVID-19 pandemic and what impacts these issues Topic Modelling COVID-19 [ 33 ] Twitter ...…”

Section: Taxonomymentioning

confidence: 99%

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy

Alamoodi

Zaidan

Al-Masawa

et al. 2021

Computers in Biology and Medicine

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A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline ( i.e., social, medical, and technological ). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: ( 1 ) social, ( 2 ) medical, public health, and ( 3 ) technology sciences. Therefore, this research aimed to review and analyze articles related different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science , Scopus , PubMed , IEEEXplore , ScienceDirect , and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.

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Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach

Cited by 76 publications

References 30 publications

Antecedents and consequences of COVID-19 conspiracy beliefs: a systematic review

Antecedents and consequences of COVID-19 conspiracy beliefs: a systematic review

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy

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