Public Reaction on Social Media During COVID-19: A Comparison Between Twitter and Weibo

2021 ◽  
pp. 612-625
Author(s):  
Tian Wang ◽  
Ian Brooks ◽  
Masooda Bashir
Keyword(s):  
Social Media ◽  
Public Reaction

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aasif Ahmad Mir ◽  
Sevukan Rathinam ◽  
Sumeer Gul
Keyword(s):  
Social Media ◽  
Public Perception ◽  
English Language ◽  
Social Issues ◽  
Application Programming Interface ◽  
Content Type ◽  
The Public ◽  
Public Reaction ◽  
Types Of Information ◽  
The Impact

PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

scholarly journals How public reaction to disease information across scales and the impacts of vector control methods influence disease prevalence and control efficacy

2021 ◽  
Vol 17 (6) ◽  
pp. e1008762
Author(s):  
Jing Jiao ◽  
Gonzalo P. Suarez ◽  
Nina H. Fefferman
Keyword(s):  
Social Media ◽  
Vector Control ◽  
Spatial Scales ◽  
Disease Incidence ◽  
Control Efficacy ◽  
Focal Concerns ◽  
Disease Information ◽  
Mosquito Breeding ◽  
Local Risk ◽  
Public Reaction

With the development of social media, the information about vector-borne disease incidence over broad spatial scales can cause demand for local vector control before local risk exists. Anticipatory intervention may still benefit local disease control efforts; however, infection risks are not the only focal concerns governing public demand for vector control. Concern for environmental contamination from pesticides and economic limitations on the frequency and magnitude of control measures also play key roles. Further, public concern may be focused more on ecological factors (i.e., controlling mosquito populations) or on epidemiological factors (i.e., controlling infection-carrying mosquitoes), which may lead to very different control outcomes. Here we introduced a generic Ross-MacDonald model, incorporating these factors under three spatial scales of disease information: local, regional, and global. We tailored and parameterized the model for Zika virus transmitted by Aedes aegypti mosquito. We found that sensitive reactivity caused by larger-scale incidence information could decrease average human infections per patch breeding capacity, however, the associated increase in total control effort plays a larger role, which leads to an overall decrease in control efficacy. The shift of focal concerns from epidemiological to ecological risk could relax the negative effect of the sensitive reactivity on control efficacy when mosquito breeding capacity populations are expected to be large. This work demonstrates that, depending on expected total mosquito breeding capacity population size, and weights of different focal concerns, large-scale disease information can reduce disease infections without lowering control efficacy. Our findings provide guidance for vector-control strategies by considering public reaction through social media.

scholarly journals “These Women are Making a Statement Against Rape and yet the only Thing Y'All can Focus on is ‘Eww They're Naked’”: Exploring Rape Culture on Facebook in South Africa

Gender Issues ◽  
2020 ◽  
Author(s):  
Zaida Orth ◽  
Michelle Andipatin ◽  
Brian van Wyk
Keyword(s):  
Social Media ◽  
South African ◽  
Public Discourse ◽  
Public Health Problem ◽  
Victim Blaming ◽  
Rape Culture ◽  
The Public ◽  
Social Media Platforms ◽  
Aggressive Male ◽  
Public Reaction

Abstract Sexual assault on campuses has been identified as a pervasive public health problem. In April 2016, students across South African universities launched the #Endrapeculture campaign to express their frustration against university policies which served to perpetuate a rape culture. The use of hashtag activism during the protest served to spark online public debates and mobilize support for the protests. This article describes the public reactions to the South African #Endrapeculture protests on the Facebook social media platform. Data was collected through natural observations of comment threads on news articles and public posts on the student protests, and subjected to content analysis. The findings suggest that the #nakedprotest was successful in initiating public conversations concerning the issue of rape culture. However, the reactions towards the #nakedprotest were divided with some perpetuating a mainstream public discourse which perpetuates rape culture, and others (re)presenting a counter-public that challenged current dominant views about rape culture. Two related main themes emerged: Victim-blaming and Trivialising Rape Culture. Victim-blaming narratives emerged from the commenters and suggested that the protesters were increasing their chances of being sexually assaulted by marching topless. This discourse seems to perpetuate the notion of the aggressive male sexual desire and places the onus on women to protect themselves. Other commenters criticised the #nakedprotest method through demeaning comments which served to derail the conversation and trivialise the message behind the protest. The public reaction to the #nakedprotest demonstrated that rape culture is pervasive in society and continues to be re(produced) through discourse on social media platforms. However, social media also offers individuals the opportunity to draw from and participate in multiple counter-publics which challenge these mainstream rape culture discourses.

scholarly journals Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

10.2196/18700 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e18700 ◽  
Author(s):  
Jiawei Li ◽  
Qing Xu ◽  
Raphael Cuomo ◽  
Vidya Purushothaman ◽  
Tim Mackey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Hubei Province ◽  
Wuhan City ◽  
Social Media Data ◽  
Public Reaction ◽  
Media Data ◽  
Chinese Social Media

Background The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
Response Strategies ◽  
Related Information ◽  
Valence And Arousal ◽  
Public Reaction ◽  
Media Data

PurposeIn recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.Design/methodology/approachThis study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.FindingsThis research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.Originality/valueThis study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis

2021 ◽  
Author(s):  
Sejung Park ◽  
Lindsey M. Bier ◽  
Han Woo Park
Keyword(s):  
Social Media ◽  
Text Mining ◽  
North Korea ◽  
Positive Emotions ◽  
News Coverage ◽  
Application Programming Interface ◽  
Analytical Framework ◽  
Public Attention ◽  
Broadcasting System ◽  
Public Reaction

This study proposes alternative measures of infotainment’s effects on audience perception and reception of news on social media, focusing on infotainment coverage of North Korea. We determine the elements of framing strategies and narrative styles in facilitating public attention, positive and negative responses, and engagement in news content. We used the YouTube application programming interface to collect data from VideoMug, Korea’s most popular YouTube channel, run by the Seoul Broadcasting System. We examined 23,774 replies commenting on North Korea-related video clips from July 1, 2018, to May 17, 2019. The findings show that entertainment and human interest frames were effective in drawing public attention to news coverage about North Korea. Using humor and colloquial language facilitated public attention (both positive and negative) and public engagement. Over half (59.55%) of the comments generated positive emotions; less than one-third generated negative emotions (31.41%); and a few generated neutral ones (9.03%). The infotainment approach helped make South Koreans’ attitudes toward North Korea and inter-Korean relations more positive. A small number of users who served as top authorities were extremely partisan and conducted intense debates about infotainment practices. This study’s hybrid analytical framework using computerized text mining techniques offers both theoretical and methodological insights into the function of infotainment in the context of social media.

scholarly journals Opinion Mining and Trend Analysis on Twitter Data

2020 ◽  
Vol 9 (1) ◽  
pp. 1650-1653
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Web Pages ◽  
Online Data ◽  
The Public ◽  
Comparison Results ◽  
Or Economics ◽  
Smart Data ◽  
Public Reaction ◽  
The Web

With rise in Internet use across the global, there has been a trending increase in the online data. Every person from different profession gives their view from politics to entertainment, sports or economics. The web’s current evolution is a major pacesetter as it generates an effectual methodology to embed “smart data” into web pages and hence result in easy content implementation for authors. The web 2.0 has changed the way of communication on the web. Using Social Networks (SNs) they have become active participants by connecting, producing and sharing information, experiences and opinions with each other [1]. Public opinions extracted in the form of trends are interesting for researchers, sociologists, news reporters, marketing professionals and opinion tracking companies. The aim of this project is opinion mining and the analysis of the trends of the public statements gathered from different social media sources (specifically twitter). Here Binary sentiment analysis is performed on currently fetched data from twitter over various emotional quotients. W have also performed (i) Comparison between two users based on public reaction in the form of likes, shares and number of re-tweets; (ii) Visualization of comparison results by plotting graphs over popularity of social media (likes/re-tweets/shares).

scholarly journals Meaningful messaging: Sentiment in elite social media communication with the public on the COVID-19 pandemic

2021 ◽  
Vol 7 (29) ◽  
pp. eabg2898
Author(s):  
Janet M. Box-Steffensmeier ◽  
Laura Moses
Keyword(s):  
Social Media ◽  
Political Communication ◽  
International Health ◽  
International Crisis ◽  
Media Communication ◽  
Health Crisis ◽  
Pivotal Moments ◽  
Media Messages ◽  
Information Spread ◽  
Public Reaction

Elite messaging plays a crucial role in shaping public debate and spreading information. We examine elite political communication during an emergent international crisis to investigate the role of tone in messaging, information spread, and public reaction. By measuring tone in social media messages from members of the U.S. Congress related to the COVID-19 pandemic, we find clear partisan differences and a differential impact of tone on message engagement and information spread. This suggests that even in the midst of an international health crisis, partisanship and emotional rhetoric play a critical part in elite communications and contribute to the attention messages receive. The messaging on COVID-19 is polarized and fractured. The valenced messaging provokes divergence in public engagement, reaction, and information spread. These results have important implications for studies of representation, public opinion, and how government can effectively engage individuals in emergent situations or pivotal moments.

scholarly journals PUBLIC REACTION TO COVID-19 ON TWITTER: A THEMATIC ANALYSIS

2020 ◽  
pp. 349-358
Author(s):  
Akif Mustafa ◽  
Imaduddin Ansari ◽  
Subham Kumar Mohanta ◽  
Shalem Balla
Keyword(s):  
Public Health ◽  
Social Media ◽  
Thematic Analysis ◽  
Public Attention ◽  
The Public ◽  
Emergency Situations ◽  
Public Health Emergencies ◽  
Health Authorities ◽  
Social Media Platforms ◽  
Public Reaction

Emergency situations typically lead to a plethora of public attention on social media platforms like ‘Twitter’. Twitter provides a unique opportunity for public health researchers to analyze untampered information shared during a disease outbreak. Considering the ongoing public health emergency, we conducted a study investigating the public reaction to COVID-19 pandemic around the world using in-depth thematic analysis of Twitter data. A dataset of 212846 tweets was retrieved over a period of seven days (from April 13, 2020, to April 19, 2020) via Twitter Application Programme Interface (API). The following five keywords were used to collect the tweets: “coronavirus”, “covid-19”, “corona”, “covid”, “covid19”. After data filtering and cleaning 6348 tweets were randomly selected for in-depth thematic analysis. Thematic analysis was done manually using a two-level coding guide. A total of six main themes emerged from the analysis: ‘sentiments and feelings’, ‘Information’, ‘General Discussion’, ‘Politics’, ‘Food’, and ‘Sarcasm or humor’. The aforementioned themes were divided into 26 sub-themes. The results of the thematic analysis show that 30.1% of the tweets were regarding ‘sentiments and feelings’, 15.6% were regarding ‘politics’, and 6.5% were related to ‘sarcasm or humor’. The present study is the first study that has analyzed the public response to COVID-19 on Twitter. The study demonstrates that social media platforms (like Twitter) can be used to conduct infodemiological studies related to public health emergencies like the COVID-19 pandemic. We believe that the results of this study will be of potential interest to policymakers, health authorities, stakeholders, and public health and social science researchers. KEYWORDS:COVID-19, Twitter, Social Media, Coronavirus, Lockdown, Pandemic

scholarly journals Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study (Preprint)

2020 ◽  
Author(s):  
Jiawei Li ◽  
Qing Xu ◽  
Raphael Cuomo ◽  
Vidya Purushothaman ◽  
Tim Mackey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Hubei Province ◽  
Wuhan City ◽  
Social Media Data ◽  
Public Reaction ◽  
Media Data ◽  
Chinese Social Media

BACKGROUND The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. OBJECTIVE The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. METHODS Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. RESULTS A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (<i>P</i>&lt;.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. CONCLUSIONS The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

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