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 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 Social media polarization and echo chambers: A case study of COVID-19 (Preprint)

2021 ◽  
Author(s):  
Julie Jiang ◽  
Xiang Ren ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Health Campaigns ◽  
Factual Information ◽  
Production And Consumption ◽  
Echo Chambers ◽  
Public Health Campaigns ◽  
Effective Public Health ◽  
The U.S

UNSTRUCTURED During 2020, social media chatter has been largely dominated by the COVID-19 pandemic. In this paper, we study the extent of polarization of COVID-19 discourse on Twitter in the U.S. First, we propose Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provide new insights into the characterization of partisan users. Right-leaning users, we find, are noticeably more vocal and active in the production and consumption of COVID-19 information. Our analysis also shows that most of the highly influential users are partisan, which may contribute to further polarization. Crucially, we provide empirical evidence that political echo chambers are prevalent, exacerbating the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.

scholarly journals Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study

JMIRx Med ◽  
10.2196/29570 ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. e29570
Author(s):  
Julie Jiang ◽  
Xiang Ren ◽  
Emilio Ferrara
Keyword(s):  
Public Health ◽  
Social Media ◽  
Information Dissemination ◽  
The United States ◽  
Health Campaigns ◽  
Political Preference ◽  
Echo Chambers ◽  
Public Health Campaigns ◽  
The Right ◽  
Effective Public Health

Background Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one’s confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users’ views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.

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.

A Study on the Festival Attributes of Gwangju Biennale Using the Social Media Analysis

2017 ◽  
Vol 29 (4) ◽  
pp. 125-151
Author(s):  
Sung-Mun Jung ◽  
Yeo-Woon Ju ◽  
Chi-Ok Oh
Keyword(s):  
Social Media ◽  
Media Analysis ◽  
Social Media Analysis ◽  
The Social

scholarly journals A Tale of Two Disasters

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 694-694
Author(s):  
Tammy Mermelstein
Keyword(s):  
Older Adults ◽  
Social Media ◽  
Interest Group ◽  
Media Analysis ◽  
Ice Storm ◽  
Social Media Analysis ◽  
Individual Survival ◽  
Social Media Platforms

Abstract Preparing for or experiencing a disaster is never easy, but how leaders communicate with older adults can ease a situation or make it exponentially worse. This case study describes two disasters in the same city: Hurricane Harvey and the 2018 Houston Texas Ice Storm and the variation in messaging provided to and regarding older adults. For example, during Hurricane Harvey, the primary pre-disaster message was self-preparedness. During the storm, messages were also about individual survival. Statements such as “do not [climb into your attic] unless you have an ax or means to break through,” generated additional fear for older adults and loved ones. Yet, when an ice storm paralyzed Houston a few months later, public messaging had a strong “check on your elderly neighbors” component. This talk will explore how messaging for these events impacted older adults through traditional and social media analysis, and describe how social media platforms assisted people with rescue and recovery. Part of a symposium sponsored by Disasters and Older Adults Interest Group.

scholarly journals Investigating transportation research based on social media analysis: a systematic mapping review

2021 ◽  
Author(s):  
Tasnim M. A. Zayet ◽  
Maizatul Akmar Ismail ◽  
Kasturi Dewi Varathan ◽  
Rafidah M. D. Noor ◽  
Hui Na Chua ◽  
...  
Keyword(s):  
Social Media ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Systematic Mapping ◽  
Mapping Review ◽  
Transportation Research ◽  
Systematic Mapping Review
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