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Article ; Online: A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset.

Nasralah, Tareq / Elnoshokaty, Ahmed / El-Gayar, Omar / Al-Ramahi, Mohammad / Wahbeh, Abdullah

2022 Volume 28, Issue 4, Page(s) 14604582221135831

Abstract: This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine ... ...

Abstract	This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, "government and politics" and "conspiracy theories," and decreased for "developmental disabilities." Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.
MeSH term(s)	Humans ; COVID-19/epidemiology ; COVID-19/prevention & control ; Social Media ; COVID-19 Vaccines ; Measles/epidemiology ; Measles/prevention & control ; Public Health
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2022-11-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/14604582221135831
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Mining Physicians' Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis.

Wahbeh, Abdullah / Nasralah, Tareq / Al-Ramahi, Mohammad / El-Gayar, Omar

JMIR public health and surveillance

2020 Volume 6, Issue 2, Page(s) e19276

Abstract: Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control ... ...

Abstract	Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. Objective: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. Methods: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19-related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. Results: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). Conclusions: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic.
MeSH term(s)	Attitude of Health Personnel ; COVID-19 ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Data Mining ; Humans ; Pandemics/prevention & control ; Physicians/psychology ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Qualitative Research ; Social Media
Keywords	covid19
Language	English
Publishing date	2020-06-18
Publishing country	Canada
Document type	Journal Article
ISSN	2369-2960
ISSN (online)	2369-2960
DOI	10.2196/19276
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data.

Al-Ramahi, Mohammad / Elnoshokaty, Ahmed / El-Gayar, Omar / Nasralah, Tareq / Wahbeh, Abdullah

JMIR public health and surveillance

2021 Volume 7, Issue 4, Page(s) e26780

Abstract: Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.: Objective: This study aimed to investigate the ... ...

Abstract	Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; Data Mining ; Humans ; Machine Learning ; Masks ; Public Opinion ; Social Media/statistics & numerical data ; United States/epidemiology
Language	English
Publishing date	2021-04-05
Publishing country	Canada
Document type	Journal Article
ISSN	2369-2960
ISSN (online)	2369-2960
DOI	10.2196/26780
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Mining Physicians' Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

Wahbeh, Abdullah / Nasralah, Tareq / Al-Ramahi, Mohammad / El-Gayar, Omar

JMIR Public Health Surveill

Abstract: BACKGROUND: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control ... ...

Abstract	BACKGROUND: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. OBJECTIVE: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. METHODS: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19-related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. RESULTS: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). CONCLUSIONS: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #305920
Database	COVID19

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Article ; Online: Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19

Wahbeh, Abdullah / Nasralah, Tareq / Al-Ramahi, Mohammad / El-Gayar, Omar

JMIR Public Health and Surveillance, Vol 6, Iss 2, p e

Mixed Methods Analysis

2020 Volume 19276

Abstract: BackgroundThe coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this ... ...

Abstract	BackgroundThe coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. ObjectiveThe objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. MethodsUsing a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19–related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. ResultsData were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). ConclusionsOur findings indicate ...
Keywords	Public aspects of medicine ; RA1-1270
Subject code	028
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Public Discourse Against Masks in the COVID-19 Era

Al-Ramahi, Mohammad / Elnoshokaty, Ahmed / El-Gayar, Omar / Nasralah, Tareq / Wahbeh, Abdullah

JMIR Public Health and Surveillance, Vol 7, Iss 4, p e

Infodemiology Study of Twitter Data

2021 Volume 26780

Abstract: BackgroundDespite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. ObjectiveThis study aimed to investigate the topics ... ...

Abstract	BackgroundDespite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. ObjectiveThis study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. MethodsWe collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. ResultsThe results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. ConclusionsThese findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising ...
Keywords	Public aspects of medicine ; RA1-1270
Subject code	900
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: The mediating effect of time management on the relationship between knowledge management and organization performance comparison between local and the international NGOs

Wahbeh, Nemer / Daher, Omar Al / Mohammed, Hamza M / Shatter, Abdullah Al

Inventi impact: microfinance & banking , No. 2 , p. 73-82

2017 , Issue 2, Page(s) 73–82

Author's details	Nemer Wahbeh, Hamza M. Mohammed, Omar Al Daher, Abdullah Al Shatter
Keywords	Time management ; Knowledge management ; Organization performance
Language	English
Dates of publication	2017-9999
Publisher	IJPL
Publishing place	Bhopal
Document type	Article
ZDB-ID	2681334-8
ISSN	2249-1007
Database	ECONomics Information System

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Book ; Online: Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19

Wahbeh, Abdullah / Nasralah, Tareq / al-Ramahi, Mohammad / El-Gayar, Omar F

Faculty Research & Publications

Mixed Methods Analysis

2020

Abstract	Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. Objective: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. Methods: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19–related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. Results: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). Conclusions: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic. [JMIR Public Health Surveill 2020;6(2):e19276]
Keywords	covid19
Publishing date	2020-01-01T08:00:00Z
Publisher	Beadle Scholar
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Comparison Study between Data Mining Tools over some Classification Methods

Abdullah H. Wahbeh / Qasem A. Al-Radaideh / Mohammed N. Al-Kabi / Emad M. Al-Shawakfa

International Journal of Advanced Computer Science and Applications, Vol Special Issue, Iss Artificial Intelligence, Pp 18-

2011 Volume 26

Abstract: Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, ... ...

Abstract	Nowadays, huge amount of data and information are available for everyone, Data can now be stored in many different kinds of databases and information repositories, besides being available on the Internet or in printed form. With such amount of data, there is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. In order to reveal the best tools for dealing with the classification task that helps in decision making, this paper has conducted a comparative study between a number of some of the free available data mining and knowledge discovery tools and software packages. Results have showed that the performance of the tools for the classification task is affected by the kind of dataset used and by the way the classification algorithms were implemented within the toolkits. For the applicability issue, the WEKA toolkit has achieved the highest applicability followed by Orange, Tanagra, and KNIME respectively. Finally; WEKA toolkit has achieved the highest improvement in classification performance; when moving from the percentage split test mode to the Cross Validation test mode, followed by Orange, KNIME and finally Tanagra respectively.
Keywords	component ; data mining tools ; data classification ; Wekak ; Orange ; Tanagra ; KNIME ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Computer Science ; DOAJ:Technology and Engineering
Subject code	006
Language	English
Publishing date	2011-09-01T00:00:00Z
Publisher	The Science and Information (SAI) Organization
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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