The Experts below are selected from a list of 100062 Experts worldwide ranked by ideXlab platform

Aziz Sheikh - One of the best experts on this subject based on the ideXlab platform.

  • artificial intelligence enabled analysis of Public Attitudes on facebook and twitter toward covid 19 vaccines in the united kingdom and the united states observational study
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    BACKGROUND: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. To achieve herd immunity, widespread administration is required which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiment towards vaccines, which can help guide educational campaigns and other targeted policy interventions. OBJECTIVE: The aim of this study was to develop and apply an artificial-intelligence (AI)-based approach to analyse social-media Public sentiment in the United Kingdom (UK) and the United States (US) towards COVID-19 vaccinations, to better 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-reading 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.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    medRxiv, 2020
    Co-Authors: Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh
    Abstract:

    Abstract 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 reading 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.

Amir Hussain - One of the best experts on this subject based on the ideXlab platform.

  • artificial intelligence enabled analysis of Public Attitudes on facebook and twitter toward covid 19 vaccines in the united kingdom and the united states observational study
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    BACKGROUND: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. To achieve herd immunity, widespread administration is required which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiment towards vaccines, which can help guide educational campaigns and other targeted policy interventions. OBJECTIVE: The aim of this study was to develop and apply an artificial-intelligence (AI)-based approach to analyse social-media Public sentiment in the United Kingdom (UK) and the United States (US) towards COVID-19 vaccinations, to better 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-reading 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.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    medRxiv, 2020
    Co-Authors: Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh
    Abstract:

    Abstract 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 reading 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.

Amit P Sheth - One of the best experts on this subject based on the ideXlab platform.

  • gender based violence in 140 characters or fewer a bigdata case study of twitter
    First Monday, 2016
    Co-Authors: Hemant Purohit, Tanvi Banerjee, Andrew J Hampton, Valerie L Shalin, Nayanesh Bhandutia, Amit P Sheth
    Abstract:

    Public institutions are increasingly reliant on data from social media sites to measure Public Attitude and provide timely Public engagement. Such reliance includes the exploration of Public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly 14 million tweets collected from Twitter over a period of 10 months to analyze Public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of computational social science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal Public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development.

  • gender based violence in 140 characters or fewer a bigdata case study of twitter
    arXiv: Social and Information Networks, 2015
    Co-Authors: Hemant Purohit, Tanvi Banerjee, Andrew J Hampton, Valerie L Shalin, Nayanesh Bhandutia, Amit P Sheth
    Abstract:

    Public institutions are increasingly reliant on data from social media sites to measure Public Attitude and provide timely Public engagement. Such reliance includes the exploration of Public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze Public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal Public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development.

Ahsen Tahir - One of the best experts on this subject based on the ideXlab platform.

  • artificial intelligence enabled analysis of Public Attitudes on facebook and twitter toward covid 19 vaccines in the united kingdom and the united states observational study
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    BACKGROUND: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. To achieve herd immunity, widespread administration is required which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiment towards vaccines, which can help guide educational campaigns and other targeted policy interventions. OBJECTIVE: The aim of this study was to develop and apply an artificial-intelligence (AI)-based approach to analyse social-media Public sentiment in the United Kingdom (UK) and the United States (US) towards COVID-19 vaccinations, to better 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-reading 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.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    medRxiv, 2020
    Co-Authors: Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh
    Abstract:

    Abstract 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 reading 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.

Mandar Gogate - One of the best experts on this subject based on the ideXlab platform.

  • artificial intelligence enabled analysis of Public Attitudes on facebook and twitter toward covid 19 vaccines in the united kingdom and the united states observational study
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    BACKGROUND: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. To achieve herd immunity, widespread administration is required which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiment towards vaccines, which can help guide educational campaigns and other targeted policy interventions. OBJECTIVE: The aim of this study was to develop and apply an artificial-intelligence (AI)-based approach to analyse social-media Public sentiment in the United Kingdom (UK) and the United States (US) towards COVID-19 vaccinations, to better 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-reading 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.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    Journal of Medical Internet Research, 2021
    Co-Authors: Amir Hussain, Ahsen Tahir, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Zain U Hussain, Aziz Sheikh
    Abstract:

    Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general Public. As such, it is crucial that governments and Public health agencies understand Public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze Public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the Public Attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing Public Attitude. Such analyses could enable real-time assessment, at scale, of Public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.

  • artificial intelligence enabled analysis of uk and us Public Attitudes on facebook and twitter towards covid 19 vaccinations
    medRxiv, 2020
    Co-Authors: Amir Hussain, Ahsen Tahir, Zain Hussain, Zakariya Sheikh, Mandar Gogate, Kia Dashtipour, Azhar Ali, Aziz Sheikh
    Abstract:

    Abstract 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 reading 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.