ABSTRACT
To facilitate effective targeted COVID-19 vaccination strategies, it is important to understand reasons for vaccine hesitancy where uptake is low. Artificial intelligence (AI) techniques offer an opportunity for real-time analysis of public attitudes, sentiments, and key discussion topics from sources of soft-intelligence, including social media data. In this work, we explore the value of soft-intelligence, leveraged using AI, as an evidence source to support public health research. As a case study, we deployed a natural language processing (NLP) platform to rapidly identify and analyse key barriers to vaccine uptake from a collection of geo-located tweets from London, UK. We developed a search strategy to capture COVID-19 vaccine related tweets, identifying 91,473 tweets between 30 November 2020 and 15 August 2021. The platform's algorithm clustered tweets according to their topic and sentiment, from which we extracted 913 tweets from the top 12 negative sentiment topic clusters. These tweets were extracted for further qualitative analysis. We identified safety concerns; mistrust of government and pharmaceutical companies; and accessibility issues as key barriers limiting vaccine uptake. Our analysis also revealed widespread sharing of vaccine misinformation amongst Twitter users. This study further demonstrates that there is promising utility for using off-the-shelf NLP tools to leverage insights from social media data to support public health research. Future work to examine where this type of work might be integrated as part of a mixed-methods research approach to support local and national decision making is suggested.
ABSTRACT
IntroductionIn areas where public confidence is low and there is a lack of understanding around behaviors, such as COVID-19 vaccine hesitancy, there is a need to explore novel sources of evidence. When leveraged using artificial intelligence (AI) techniques, social media data may offer rich insights into public concerns around vaccination. Currently, sources of ‘soft-intelligence' are underutilized by policy makers, health technology assessment (HTA) and other public health research agencies. In this work, we used an AI platform to rapidly detect and analyze key barriers to vaccine uptake from a sample of geo-located tweets.MethodsAn AI-based tool was deployed using a robust search strategy to capture tweets associated with COVID-19 vaccination, posted from users in London, United Kingdom. The tool's algorithm automatically clustered tweets based on key topics of discussion and sentiment. Tweets contained within the 12 most populated topics with negative sentiment were extracted. The extracted tweets were mapped to one of six pre-determined themes (safety, mistrust, under-representation, complacency, ineffectiveness, and access) informed using the World Health Organization's 3Cs vaccine hesitancy model. All collated tweets were anonymized.ResultsWe identified 91,473 tweets posted between 30 November 2020 and 15 August 2021. A sample of 913 tweets were extracted from the twelve negative topic clusters. Of these, 302 tweets were coded to a vaccine hesitancy theme. ‘Safety' (29%) and ‘mistrust' (23%) were the most commonly coded themes;the least commonly coded was ‘under-representation' (3%). Within the main themes, adverse reactions, inadequate assessment, and rushed development of the vaccines as key findings. Our analysis also revealed widespread sharing of misinformation.ConclusionsUsing an AI-based text analytics tool, we were able to rapidly assess public confidence in COVID-19 vaccination and identify key barriers to uptake from a corpus of geo-located tweets. Our findings support a growing body of evidence and confidence surrounding the use of AI tools to efficiently analyze early sources of soft-intelligence evidence in public health research.
ABSTRACT
Background: There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective: The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods: A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter's advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results: We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people's mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions: Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people's mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.
ABSTRACT
IntroductionVarious strategies to suppress the Coronavirus have been adopted by governments across the world;one such strategy is diagnostic testing. The anxiety of testing on individuals is difficult to quantify. This analysis explores the use of soft intelligence from Twitter (USA, UK & India) in helping better understand this issue.MethodsA total of 650,000 tweets were collected between September and October 2020, using Twitter API using hashtags such as ‘#oxymeter’, ‘#oximeter’, ‘#antibodytest’, ‘#infraredthermometer’, ‘#swabtest’, ‘#rapidtest’, and ‘#antigen’. We applied natural language processing (TextBlob) to assign sentiment and categorize the tweets by emotions and attitude. WordCloud was then used to identify the single topmost 500 words in the whole tweet dataset.ResultsGlobal analysis and pre-processing of the tweets indicate that 21 percent, seven percent and four percent of tweets originated from the USA, UK, and India respectively. The tweets from #antibody, #rapid, #antigen, and #swabtest were positive sentiments, whereas #oxymeter, #infraredthermometer were mostly neutral. The underlying emotions of the tweets were approximately 2.5 times more positive than negative. The most used words in the tweets included ‘hope’ ‘insurance’, ‘symptoms’, ‘love’, ‘painful’, ‘cough’, ‘fast test’, ‘wife’, and ‘kids’.ConclusionsThe finding suggests that it may be reasonable to infer that people are generally concerned about their personal and social wellbeing, wanting to keep themselves safe and perceive testing to deliver some component of that feeling of safety. There are several limitations to this study such as it was restricted to only three countries, and includes only English language tweets with a limited number of hashtags.
ABSTRACT
IntroductionThere is increasing pressure to rapidly shape policies and inform decision-making where robust evidence is lacking. This work aimed to explore the value of soft-intelligence as a novel source of evidence. We deployed an artificial intelligence based natural language platform to identify and analyze a large collection of UK tweets relating to mental health during the COVID-19 pandemic.MethodsA search strategy comprising a list of terms relating to mental health, COVID-19 and the lockdown was developed to prospectively identify relevant tweets via Twitter's advanced search application programming interface. We used a specialist text analytics platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion for qualitative analysis. All collated tweets were anonymized.ResultsWe identified 380,728 tweets from 184,289 unique users in the UK from 30 April to 4 July 2020. The average sentiment score was fifty-two percent, suggesting overall positive sentiment. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. For example, some people described how they were using the lockdown as a positive opportunity to work on their mental health, sharing helpful strategies to support others. However, many people expressed the damaging impact the pandemic (and resulting lockdown) was having on their mental health, including worsening anxiety, stress, depression, and loneliness.ConclusionsThe results suggest that soft-intelligence is potentially a useful source of evidence. The approach taken to identify and analyze this data may offer an efficient means of establishing key insights from the ‘public voice’ relating to critical health issues. However, there are still various limitations to consider concerning the technology and representativeness of the data. Future work to explore this type of evidence further, and how it might formally support decision-making processes, is recommended.This project is funded by the NIHR [(HSRIC-2016-10009)/Innovation Observatory]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
ABSTRACT
The ongoing COVID-19 pandemic has highlighted the immediate need for the development of antiviral therapeutics targeting different stages of the SARS-CoV-2 life cycle. We developed a bioluminescence-based bioreporter to interrogate the interaction between the SARS-CoV-2 viral spike (S) protein and its host entry receptor, angiotensin-converting enzyme 2 (ACE2). The bioreporter assay is based on a nanoluciferase complementation reporter, composed of two subunits, large BiT and small BiT, fused to the S receptor-binding domain (RBD) of the SARS-CoV-2 S protein and ACE2 ectodomain, respectively. Using this bioreporter, we uncovered critical host and viral determinants of the interaction, including a role for glycosylation of asparagine residues within the RBD in mediating successful viral entry. We also demonstrate the importance of N-linked glycosylation to the RBD's antigenicity and immunogenicity. Our study demonstrates the versatility of our bioreporter in mapping key residues mediating viral entry as well as screening inhibitors of the ACE2-RBD interaction. Our findings point toward targeting RBD glycosylation for therapeutic and vaccine strategies against SARS-CoV-2.