scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Twitter and Facebook towards COVID-19 vaccination (Preprint)

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Kia Dashtipour ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

UNSTRUCTURED Background: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. Methods: Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural-language processing and deep learning-based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual- eading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. Results: We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. Conclusions: AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Mandar Gogate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

AbstractBackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern.MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis.ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly.ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

2020 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

scholarly journals Using Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps (Preprint)

2020 ◽  
Author(s):  
Kathrin Cresswell ◽  
Ahsen Tahir ◽  
Zakariya Sheikh ◽  
Zain Hussain ◽  
Andrés Domínguez Hernández ◽  
...  
Keyword(s):  
Social Media ◽  
Public Attitudes ◽  
Public Perceptions ◽  
Media Analysis ◽  
Contact Tracing ◽  
Health Campaigns ◽  
Social Media Analysis ◽  
Public Health Campaigns ◽  
The Uk ◽  
Effective Public Health

UNSTRUCTURED We here report on an exploratory analysis of the suitability of AI-enabled social media analysis of Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the UK. We extracted over 10,000 relevant social media posts and analysed these over an eight month period, from 1st of March to 31st of October 2020. Overall, we observed 76% positive and 12% negative sentiments, and discuss how the government's decision to move from a centralised to a decentralised contact-tracing model is likely to have influenced sentiment trends. In doing so, we demonstrate how AI-enabled social media analysis of public attitudes in healthcare can help to facilitate the implementation of effective public health campaigns.

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020

2021 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

AbstractBackgroundThe coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs.ObjectiveTo measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic.MethodsNatural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US.ResultsThe final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%).ConclusionsThere was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

scholarly journals Faster than warp speed: early attention to COVD-19 by anti-vaccine groups on Facebook

2021 ◽  
Author(s):  
Seth C Kalichman ◽  
Lisa A Eaton ◽  
Valerie A Earnshaw ◽  
Natalie Brousseau
Keyword(s):  
Social Media ◽  
Vaccine Development ◽  
Rapid Development ◽  
Development Program ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
Conspiracy Theories ◽  
The Us ◽  
Us Government ◽  
Vaccine Information

Abstract Background The unprecedented rapid development of COVID-19 vaccines has faced SARS-CoV- (COVID-19) vaccine hesitancy, which is partially fueled by the misinformation and conspiracy theories propagated by anti-vaccine groups on social media. Research is needed to better understand the early COVID-19 anti-vaccine activities on social media. Methods This study chronicles the social media posts concerning COVID-19 and COVID-19 vaccines by leading anti-vaccine groups (Dr Tenpenny on Vaccines, the National Vaccine Information Center [NVIC] the Vaccination Information Network [VINE]) and Vaccine Machine in the early months of the COVID-19 pandemic (February–May 2020). Results Analysis of 2060 Facebook posts showed that anti-vaccine groups were discussing COVID-19 in the first week of February 2020 and were specifically discussing COVID-19 vaccines by mid-February 2020. COVID-19 posts by NVIC were more widely disseminated and showed greater influence than non-COVID-19 posts. Early COVID-19 posts concerned mistrust of vaccine safety and conspiracy theories. Conclusion Major anti-vaccine groups were sowing seeds of doubt on Facebook weeks before the US government launched its vaccine development program ‘Operation Warp Speed’. Early anti-vaccine misinformation campaigns outpaced public health messaging and hampered the rollout of COVID-19 vaccines.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising 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 specialist NLP platform to explore tweet frequency and sentiment across the UK 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. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, 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 Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse 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 analysed 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.

How Twitter drives the global news agenda: Tweets from Brazil, Russia, India, China, the UK and US and online discourse about the 2016 US presidential election

2021 ◽  
pp. 174276652110399
Author(s):  
Jane O’Boyle ◽  
Carol J Pardun
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
News Media ◽  
Presidential Election ◽  
Online Discourse ◽  
Political Communications ◽  
The Us ◽  
Us Presidential Election ◽  
The Uk ◽  
India And China

A manual content analysis compares 6019 Twitter comments from six countries during the 2016 US presidential election. Twitter comments were positive about Trump and negative about Clinton in Russia, the US and also in India and China. In the UK and Brazil, Twitter comments were largely negative about both candidates. Twitter sources for Clinton comments were more frequently from journalists and news companies, and still more negative than positive in tone. Topics on Twitter varied from those in mainstream news media. This foundational study expands communications research on social media, as well as political communications and international distinctions.

Coda

2020 ◽  
pp. 267-270
Author(s):  
Héctor Fernández L’Hoeste ◽  
Juan Carlos Rodríguez
Keyword(s):  
Social Media ◽  
Latin American ◽  
Social Relations ◽  
Digital Humanities ◽  
The Us ◽  
The Uk

This chapter provides a balance for the volume, accounting for the implications of recent political shift in the US, much of which has been linked to social media. It emphasizes how the texts included in this collection also suggest the speed with which technology is playing a preeminent role in cultural, political, and social relations, be it north or south of the border. In the end, it seeks to strike a balance between the scholarly worlds in Portuguese and Spanish and academic spheres of the Anglo domain, clarifying the volume's intention not to pontificate, but rather to serve as a bridge between the Latin American digital humanities and the Anglo academe in the US and the UK, in a fashion as independent as possible to hegemonic proclivities.

scholarly journals Who shares news on mobile messaging applications, why and in what ways? A cross-national analysis

2020 ◽  
pp. 205015792095844
Author(s):  
Antonis Kalogeropoulos
Keyword(s):  
Social Media ◽  
Breaking News ◽  
Main Motivation ◽  
News Stories ◽  
Current Affairs ◽  
The Us ◽  
The Uk ◽  
National Analysis ◽  
News Sharing ◽  
Cross National

Recently, in many countries, the use of mobile messaging applications for news has risen while the use of Facebook for news has declined. The purpose of this study is to identify who shares news on messaging applications, why and in what ways. Findings from a survey and focus groups in the US, the UK, Germany, and Brazil show that (a) the main motivation for news users to share news in these spaces is context collapse; their aversion to news sharing on an open network like Facebook, (b) the anytime/anywhere mobile affordance facilitates their need for private news sharing, (c) news stories chosen for sharing usually revolve around niche interests or breaking news events and not politics and current affairs, (d) news sharers are likely to be young, and to trust in news found on social media in the Western countries of our sample, while they tend to be older and partisan in Brazil where 38% of the population shares news on mobile messaging apps during an average week.

scholarly journals Detecting contrast patterns in newspaper articles by combining discourse analysis and text mining

Pragmatics ◽  
2011 ◽  
Vol 21 (4) ◽  
pp. 647-683 ◽  
Author(s):  
Senja Pollak ◽  
Roel Coesemans ◽  
Walter Daelemans ◽  
Nada Lavrač
Keyword(s):  
Discourse Analysis ◽  
Text Mining ◽  
Media Analysis ◽  
Press Coverage ◽  
Classification Models ◽  
News Reports ◽  
Text Collections ◽  
The Us ◽  
Interpretive Frame ◽  
The Uk

Text mining aims at constructing classification models and finding interesting patterns in large text collections. This paper investigates the utility of applying these techniques to media analysis, more specifically to support discourse analysis of news reports about the 2007 Kenyan elections and post-election crisis in local (Kenyan) and Western (British and US) newspapers. It illustrates how text mining methods can assist discourse analysis by finding contrast patterns which provide evidence for ideological differences between local and international press coverage. Our experiments indicate that most significant differences pertain to the interpretive frame of the news events: whereas the newspapers from the UK and the US focus on ethnicity in their coverage, the Kenyan press concentrates on sociopolitical aspects.

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