Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 14 de 14
Filter
1.
2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1932099

ABSTRACT

COVID-19 is an infectious disease, which was first appeared in December 2019 in Wuhan, China. This virus has spread all over the world. So in a situation like this, Twitter is helping people by giving the latest information and to connect with others. As the WHO giving health information, this paper work is an implementation of automation for extracting details of Covid-19 from the latest Tweets of Twitter Social media. Most of the people started with Negative tweets about covid19, but with increasing time people shifted towards positive and neutral comments. At some time most of the comments are about winning against coronavirus. To understand the people's opinion towards this pandemic through their tweets, we have tried to come up with an algorithm that will try to analyze the tweets using the modern computational power and some of the advanced algorithms and finally concluded at a point. Sentiment analysis using LSTM (Long Short Term Memory) which is a type of Recurrent Neural Networks, has been applied to tweets having covid19 Hash tags to see people's reactions to the pandemic. The tweets are classified and labeled as positive, negative, and neutral then visualized the result. Tweets are categorized into three classes and derive some useful patterns from them and trying to come up with some generalized algorithms so that it cannot only be applied for Covid19 or some health-related, rather apply all kind of tweets or some other social media platform such as Instagram or LinkedIn. © 2022 IEEE.

2.
Int J Disaster Risk Reduct ; 79: 103161, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1914467

ABSTRACT

Background and aims: The COVID-19 pandemic outbreak has created severe public health crises and economic consequences across the globe. This study used text analytics techniques to investigate the key concerns of Indian citizens raised in social media during the second wave of COVID-19. Methods: In this study, we performed a sentiment and emotion analysis of tweets to understand the attitude of Indian citizens during the second wave of COVID-19. Moreover, we performed topic modeling to understand the significant issues and concerns related to COVID-19. Results: Our results show that most social media posts were in neutral tone, and the percentage of posts that showed positive sentiment was less. Furthermore, emotion analysis results show that 'Fear' and 'Surprise' were the prominent emotions expressed by the citizens. Topic modeling results reveal that 'High crowd' and 'political rallies' are the two primary topics of concern raised by Indian citizens during the second wave of COVID-19. Conclusions: Hence, Indian government agencies should communicate crisis information and combating strategies to citizens more effectively in order to minimize the fear and anxiety amongst the public.

3.
Machine Learning-Driven Digital Technologies for Educational Innovation Workshop ; 2021.
Article in English | Web of Science | ID: covidwho-1895924

ABSTRACT

The Covid-19 outbreak forced education into a distance modality. Professors and educators suddenly confronted unexpected challenges, including a lack of technical skills to implement efficient pedagogies in this modality. One rescuing element was social media, increasingly used inside organizations. It allows users to create content and provide valuable information on human interactions and collective behavior, mainly textual data. The objective of the current research was to identify professorial concerns after the shift to distance education and the first 15 months of confinements. Specifically, we analyzed the comments expressed in the social networks by more than 5,700 faculty members of a Mexican private university that implemented online teaching. Applying Educational Data Mining to 680 remarks retrieved from the social network, we used Voyant Tools and R programming for text and sentiment analysis. The results evidenced that the professors created a kind social network, sharing tips and digital media as educational resources, which led to a natural learning curve for developing online teaching competencies. Other relevant findings included the need to provide the professors continuous training in communication and learning management platforms to engage in ongoing discussions on topics, such as whether turning on the cameras should be compulsory during online lectures. This work's results have value to higher education institutions and professors seeking a better understanding of their requirements and decision-making to improve education delivery under current and future constraints.

4.
Production and Operations Management ; : 20, 2022.
Article in English | Web of Science | ID: covidwho-1868688

ABSTRACT

Governments and healthcare organizations increasingly pay attention to social media for handling a disease outbreak. The institutions and organizations need information support to gain insights into the situation and act accordingly. Currently, they primarily rely on ground-level data, collecting which is a long and cumbersome process. Social media data present immense opportunities to use ground data quickly and effectively. Governments and HOs can use these data in launching rapid and speedy remedial actions. Social media data contain rich content in the form of people's reactions, calls-for-help, and feedback. However, in healthcare operations, the research on social media for providing information support is limited. Our study attempts to fill the gap mentioned above by investigating the relationship between the activity on social media and the quantum of the outbreak and further using content analytics to construct a model for segregating tweets. We use the case example of the COVID-19 outbreak. The pandemic has advantages in contributing to the generalizability of results and facilitating the model's validation through data from multiple waves. The findings show that social media activity reflects the outbreak situation on the ground. In particular, we find that negative tweets posted by people during a crisis outbreak concur with the quantum of a disease outbreak. Further, we find a positive association between this relationship and increased information sharing through retweets. Building further on this insight, we propose a model using advanced analytical methods to reduce a large amount of unstructured data into four key categories-irrelevant posts, emotional outbursts, distress alarm, and relief measures. The supply-side stakeholders (such as policy makers and humanitarian organizations) could use this information on time and optimize resources and relief packages in the right direction proactively.

5.
Sustainability ; 14(6):3643, 2022.
Article in English | ProQuest Central | ID: covidwho-1765915

ABSTRACT

The COVID-19 pandemic influenced people’s everyday lives because of the health emergency and the resulting socio-economic crisis. People use social media to share experiences and search for information about the disease more than before. This paper aims at analysing the discourse on COVID-19 developed in 2020 by Italian tweeters, creating a digital storytelling of the pandemic. Employing thematic analysis, an approach used in bibliometrics to highlight the conceptual structure of a research domain, different time slices have been described, bringing out the most discussed topics. The graphical mapping of these topics allowed obtaining an easily readable representation of the discourse, paving the way for novel uses of thematic analyses in social sciences.

6.
17th International Computer Engineering Conference, ICENCO 2021 ; : 14-17, 2021.
Article in English | Scopus | ID: covidwho-1759075

ABSTRACT

In this research, we analyzed the Covid-19 phenomena in the USA through analysis of Twitter data related to the Covid-19 pandemic in USA. We made this analysis with Twitter data from April and May of the year 2020. What we did differently in this research was focusing on one hashtag only so that we could focus on a fixed community. Our goal is to see if there is a connection or a pattern that could be found between the different output measures and plots. To do this, we focused on the country of the USA as a use-case. The difference in this analysis is that we didn't create our dataset by downloading data generally related to Covid-19 in the USA (from multiple tags), but rather we tracked one Twitter hashtag, ensuring that we track a certain group of the population so we could be sure about our population interest calculation results. © 2021 IEEE.

7.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 6052-6054, 2021.
Article in English | Scopus | ID: covidwho-1730877

ABSTRACT

With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features. © 2021 IEEE.

8.
Nutr Res Pract ; 15(Suppl 1): S110-S121, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1614111

ABSTRACT

BACKGROUND/OBJECTIVES: Coronavirus disease 2019 (COVID-19) cases were first reported in December 2019, in China, and an increasing number of cases have since been detected all over the world. The purpose of this study was to collect significant news media reports on food services during the COVID-19 crisis and identify public communication and significant concerns regarding COVID-19 for suggesting future directions for the food industry and services. SUBJECTS/METHODS: News articles pertaining to food services were extracted from the home pages of major news media websites such as BBC, CNN, and Fox News between March 2020 and February 2021. The retrieved data was sorted and analyzed using Python software. RESULTS: The results of text analytics were presented in the format of the topic label and category for individual topics. The food and health category presented the effects of the COVID-19 pandemic on food and health, such as an increase in delivery services. The policy category was indicative of a change in government policy. The lifestyle change category addressed topics such as an increase in social media usage. CONCLUSIONS: This study is the first to analyze major news media (i.e., BBC, CNN, and Fox News) data related to food services in the context of the COVID-19 pandemic. Text analytics research on the food services domain revealed different categories such as food and health, policy, and lifestyle change. Therefore, this study contributes to the body of knowledge on food services research, through the use of text analytics to elicit findings from media sources.

9.
PeerJ Comput Sci ; 7: e813, 2021.
Article in English | MEDLINE | ID: covidwho-1591221

ABSTRACT

Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach this with algorithms, methods, and models. These include machine learning and deep learning (DL) methods, unigram and skip-gram based algorithms, as well as the Artificial Neural Network (ANN) and bag-of-word (BOW) regression model. Studies and research have revealed incoherence in polarity, model overfitting and performance issues, as well as high cost in data processing. This experiment was conducted to solve these revealing issues, by building a high performance yet cost-effective model for predicting accurate sentiments from large datasets containing customer reviews. This model uses the fastText library from Facebook's AI research (FAIR) Lab, as well as the traditional Linear Support Vector Machine (LSVM) to classify text and word embedding. Comparisons of this model were also done with the author's a custom multi-layer Sentiment Analysis (SA) Bi-directional Long Short-Term Memory (SA-BLSTM) model. The proposed fastText model, based on results, obtains a higher accuracy of 90.71% as well as 20% in performance compared to LSVM and SA-BLSTM models.

10.
J Bus Res ; 140: 670-683, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1517322

ABSTRACT

Amid the flood of fake news on Coronavirus disease of 2019 (COVID-19), now referred to as COVID-19 infodemic, it is critical to understand the nature and characteristics of COVID-19 infodemic since it not only results in altered individual perception and behavior shift such as irrational preventative actions but also presents imminent threat to the public safety and health. In this study, we build on First Amendment theory, integrate text and network analytics and deploy a three-pronged approach to develop a deeper understanding of COVID-19 infodemic. The first prong uses Latent Direchlet Allocation (LDA) to identify topics and key themes that emerge in COVID-19 fake and real news. The second prong compares and contrasts different emotions in fake and real news. The third prong uses network analytics to understand various network-oriented characteristics embedded in the COVID-19 real and fake news such as page rank algorithms, betweenness centrality, eccentricity and closeness centrality. This study carries important implications for building next generation trustworthy technology by providing strong guidance for the design and development of fake news detection and recommendation systems for coping with COVID-19 infodemic. Additionally, based on our findings, we provide actionable system focused guidelines for dealing with immediate and long-term threats from COVID-19 infodemic.

11.
Soc Sci Med ; 280: 114057, 2021 07.
Article in English | MEDLINE | ID: covidwho-1240623

ABSTRACT

Research has shown that the temporal focus of individuals can have a real effect on behavior. In the context of the COVID-19 pandemic, this study posits that temporal focus will affect adherence behavior regarding health control measures, such as social distancing, hand washing and mask wearing, which will be manifested through the degree of spread of COVID-19. It is suggested that social media can provide an indicator of the general temporal focus of the population at a particular time. In this study, we examine the temporal focus of Twitter text data and the number of COVID-19 cases in the US over a 317-day period from the inception of the pandemic, using text analytics to classify the temporal content of 0.76 million tweets. The data is then analyzed using dynamic regression via advanced ARIMA modelling, differencing the data, removing weekly seasonality and creating a stationary time series. The result of the dynamic regression finds that past orientation does indeed have an effect on the growth of COVID-19 cases in the US. However, a present focus tends to reduce the spread of COVID cases. Future focus had no effect in the model. Overall, the research suggests that detecting and managing temporal focus could be an important tool in managing public health during a pandemic.


Subject(s)
COVID-19 , Social Media , Humans , Pandemics , SARS-CoV-2
12.
13.
Diabetes Metab Syndr ; 15(2): 595-599, 2021.
Article in English | MEDLINE | ID: covidwho-1103831

ABSTRACT

BACKGROUND AND AIMS: The government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media. METHODS: For this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine. RESULTS: Our results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine. CONCLUSION: With the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.


Subject(s)
Attitude to Health , COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , Fear , Social Media , Humans , India , Machine Learning , Natural Language Processing , SARS-CoV-2
14.
J Am Med Inform Assoc ; 27(8): 1321-1325, 2020 08 01.
Article in English | MEDLINE | ID: covidwho-629242

ABSTRACT

OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits. MATERIALS AND METHODS: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms. RESULTS: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. CONCLUSIONS: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnosis , Natural Language Processing , Pneumonia, Viral/diagnosis , Telemedicine , Algorithms , Betacoronavirus , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Deep Learning , Electronic Health Records , Humans , Neural Networks, Computer , Organizational Case Studies , Pandemics , ROC Curve , Risk Assessment , SARS-CoV-2 , South Carolina
SELECTION OF CITATIONS
SEARCH DETAIL