scholarly journals Detecting Topics and Sentiments of Public Concerns on COVID-19 Vaccines with Social Media Trend Analysis (Preprint)

10.2196/30765 ◽  
2021 ◽  
Author(s):  
Michal Monselise ◽  
Chia-Hsuan Chang ◽  
Gustavo Ferreira ◽  
Rita Yang ◽  
Christopher C. Yang
Keyword(s):  
Social Media ◽  
Trend Analysis ◽  
Public Concerns

scholarly journals Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts (Preprint)

2021 ◽  
Author(s):  
Tau Ming Liew ◽  
Cia Sin Lee
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Temporal Variations ◽  
Emotional Reactions ◽  
Barriers And Facilitators ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Public Attention ◽  
Public Concerns

BACKGROUND Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. OBJECTIVE The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. METHODS Twitter was searched for tweets related to “COVID-19” and “vaccine” over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). RESULTS Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. CONCLUSIONS The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.

scholarly journals US Public Concerns About the COVID-19 Pandemic From Results of a Survey Given via Social Media

2020 ◽  
Vol 180 (7) ◽  
pp. 1020 ◽  
Author(s):  
Lorene M. Nelson ◽  
Julia F. Simard ◽  
Abiodun Oluyomi ◽  
Vanessa Nava ◽  
Lisa G. Rosas ◽  
...  
Keyword(s):  
Social Media ◽  
Public Concerns

Social Media Image Recognition for Food Trend Analysis

2017 ◽  
Author(s):  
Giuseppe Amato ◽  
Paolo Bolettieri ◽  
Vinicius Monteiro de Lira ◽  
Cristina Ioana Muntean ◽  
Raffaele Perego ◽  
...  
Keyword(s):  
Social Media ◽  
Trend Analysis ◽  
Image Recognition ◽  
Media Image

scholarly journals Research and Trends in the Field of Social Media from 2012 to 2016: A Content Analysis of Studies in Selected Journals

2019 ◽  
Vol 25 (1) ◽  
pp. 13-31
Author(s):  
Uğur Bakan ◽  
Turgay Han
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Trend Analysis ◽  
Research Methods ◽  
Research Design ◽  
Citation Count ◽  
Research Topic ◽  
Content Analyses

This paper aims at making a trend analysis of 1142 studies in the field of social media that were published in 12 SSCI journals from 2012 to 2016. Citation and content analyses were used to investigate the trends. Among the 4391 articles published in these journals, 1142 articles were identified as being related to the topic of social media. In the analysis, first, these articles were crossanalyzed by published years, journal, research topic, and citation count. Next, these articles on different sub-topics were analyzed according to their research settings, participants, research design types, and research methods.

scholarly journals Twitter Trend Analysis

2020 ◽  
pp. 94-103
Author(s):  
Sankalp Nilekar ◽  
Sudeep Rawat ◽  
Rahul Verma ◽  
Pravin Rahate
Keyword(s):  
Social Media ◽  
Trend Analysis ◽  
Total Count ◽  
Social Trends ◽  
Good Morning ◽  
Social Trend ◽  
Search Field ◽  
Common Interests ◽  
The Voice ◽  
Flowing Stream

The community of users participating in social media tends to share common interests at the same time, giving rise to what are known as social trends. A social trend reflects the voice of a large number of users which, for some reason, becomes popular in a specific moment. Through social trends, users, therefore, suggest that some occurrence of wide interest is taking place and subsequently triggering the trend. In this work, we explore the types of triggers that spark trends on the microblogging site Twitter and introduce a typology that includes the following four types: news, ongoing events, memes, and commemoratives. The user will be allowed to search for the latest trends by inputting a keyword into the search field. Based on user- provided keyword, the system will search for similar keywords in database and summarize the total count to provide the trending tweets on twitter. The trending tweets with the hashtag (#) will be displayed first and then the rest words will be displayed. By clicking on every trending tweet, the user commented tweets will be displayed. User can view all the tweets from the searched keyword. One of the main features on the homepage of Twitter shows a list of top terms so called trending topics at all times. These terms reflect the topics that are being discussed most at the very moment on the site’s fast-flowing stream of tweets. In order to avoid topics that are popular regularly (e.g., good morning or goodnight on certain times of the day), Twitter focuses on topics that are being discussed much more than usual, i.e., topics that recently suffered an increase of use, so that it trended for some reason

Initial Exploration on an Effective Social Media Analytics Method and Algorithm for Instagram Hashtags

2019 ◽  
Vol 15 (3) ◽  
pp. 1-15
Author(s):  
Nurul Atikah Ahmad Rosli ◽  
Mohd Heikal Husin
Keyword(s):  
Social Media ◽  
Trend Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Business Organizations ◽  
Analysis Method ◽  
The Public ◽  
Vector Machines ◽  
Social Media Platforms ◽  
Online Business

Over the years, social media has brought many benefits to different fields, especially in the business sector. Most of the existing organizations have taken these benefits to actively engage with the public to increase their online business value. The use of hashtags on numerous social media platforms especially on Instagram is one of the highly used benefits. By tagging specific postings, business organizations are able to promote and communicate with their customers directly in a more interactive manner. In this article, the authors are exploring the following: (1) to determine the effectiveness of the existing analytics method (text identification and trend analysis) for analyzing Instagram hashtag data and; (2) to determine the effectiveness of existing analytic techniques such as Naïve Bayes and Support Vector Machines (SVM) suited for the selected analytics method. As a result, the authors have identified that the combination of Trend Analysis method and SVM are an effective social media analytics approach for analyzing Instagram hashtag data.

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