Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
5,235
result(s) for
"Twitter."
Sort by:
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring.
The objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users.
We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, \"coronavirus,\" \"COVID-19,\" \"quarantine\") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets.
Popular unigrams included \"virus,\" \"lockdown,\" and \"quarantine.\" Popular bigrams included \"COVID-19,\" \"stay home,\" \"corona virus,\" \"social distancing,\" and \"new cases.\" We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics.
This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
Journal Article
X Data-Based Scientific Research: A Review of Trends and Challenges
Introduction: The growth of social media, especially X (formerly Twitter), has become a key resource for scientific research. This literature review identifies the factors driving its use, forecasts trends, and addresses challenges faced by researchers. Methodology: The review, based on a systematic search in Scopus, employed thematic mapping to identify interdisciplinary applications, methodological innovations, and the impact of global events. Key among these innovations was natural language processing (NLP) for data analysis, which grew 268% from 2019 to 2023. Results: NLP has established itself as a vital tool. However, publications based on X data showed a slowdown between 2021 and 2023, while Instagram and TikTok- based publications accelerated, signaling increased interest in these platforms. X remains the most used platform, followed by Facebook. Conclusions: The review highlights the need for more advanced analysis methods, stronger ethical standards concerning privacy and consent, and interdisciplinary approaches in social media research. Keywords: social media; Twitter; X; research; trends.
Journal Article
Mining Twitter Data for Improved Understanding of Disaster Resilience
Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial-temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012. The study area includes 126 counties affected by Hurricane Sandy. The objectives are threefold: (1) to derive a set of common indexes from Twitter data so that they can be used for emergency management and resilience analysis; (2) to examine whether there are significant geographical and social disparities in disaster-related Twitter use; and (3) to test whether Twitter data can improve postdisaster damage estimation. Three corresponding hypotheses were tested. Results show that common indexes derived from Twitter data, including ratio, normalized ratio, and sentiment, could enable comparison across regions and events and should be documented. Social and geographical disparities in Twitter use existed in the Hurricane Sandy event, with higher disaster-related Twitter use communities generally being communities of higher socioeconomic status. Finally, adding Twitter indexes into a damage estimation model improved the adjusted R
2
from 0.46 to 0.56, indicating that social media data could help improve postdisaster damage estimation, but other environmental and socioeconomic variables influencing the capacity to reducing damage might need to be included. The knowledge gained from this study could provide valuable insights into strategies for utilizing social media data to increase resilience to disasters.
Journal Article
Twitterھ : how Jack Dorsey changed the way we communicate
Discover the story of Jack Dorsey, Twitter's co-founder, and how he helped to create one of the Internet's biggest successes. Learn how Jack and his friends came up with the ideas for the business that would change their lives and the lives of so many Internet users forever.
Book
Fragmentation in the Twitter Following of News Outlets
In recent years, Twitter emerged as an important news driver as most major news organizations now provide newsfeeds via Twitter. We classified 34 South Korean news outlets based on the pattern of co-following among 709,586 Twitter users. We also had a rare opportunity to match their following behavior with individual-level attributes by relying on supplementary survey data on 1,811 members of an online survey panel. Our results reveal that partisan and generational selectivity sharply polarizes news following on Twitter, suggesting that Twitter is likely to reinforce the existing political divisions in society by reducing the likelihood of chance encounters with the disagreeable views. (Author abstract)
Journal Article
A thematic analysis of South African opinions about COVID-19 vaccination on Twitter
Vaccine hesitancy is a public health concern in South Africa and internationally. Literature on vaccine hesitancy associates this with mistrust of the government. We present a qualitative analysis of opinions about COVID-19 vaccination expressed by South African Twitter (now X) users during the first year of the vaccine rollout in South Africa. We conducted a thematic analysis of 800 randomly selected tweets containing vaccine-related keywords, sampled from four time periods in 2021. We categorised comprehensible South African non-news tweets as pro-vaccination (24.75% of sample), anti-vaccination (20.25%) or ambivalent (4.5%), and then identified themes. Among pro-vaccination tweets, the most common themes were criticism of the government's handling of vaccine procurement and the rollout; concerns that the vaccine was urgently needed and/or not being made available fast enough; and statements that vaccines were safe and/or effective against COVID-19. Among anti-vaccination tweets, the most common themes were claims that the vaccine was harmful or too risky; suspicion of the government's intentions with respect to the vaccine it was offering the public; and opposition to mandatory or 'forced' vaccination. Criticism and mistrust of the government were present among both pro- and anti-vaccination tweets, though for different reasons. We discuss this in light of literature recommending trust-building as a response to vaccine hesitancy.Significance:Numerous studies recognise mistrust in the government as a correlate of anti-vaccination opinions, butour findings suggest that holders of both pro- and anti-vaccination opinions in South Africa mistrust the government – albeit for different reasons. Several South African authors propose ‘trust-building’ as a solution to vaccine hesitancy and refusal, but we suggest that in a context of government corruption, it is not more trust that the South African public needs, but more critical literacy in order to discern when the governmentis and is not acting in the public (health) interest.
Journal Article