scholarly journals The Growth of Negative Sentiment in Post-Umbrella Movement Hong Kong: Analyzing Public Opinion Online from 2017 to 2019

2021 ◽  
Author(s):  
Fei Shen ◽  
Chuanli Xia ◽  
Wenting Yu ◽  
Chen Min ◽  
Tianjiao Wang ◽  
...  
Keyword(s):  
Hong Kong ◽  
Public Opinion ◽  
News Media ◽  
Social Issues ◽  
Online News ◽  
Time Span ◽  
Discussion Forums ◽  
Public Figures ◽  
Mining Project ◽  
Negative Sentiment

This report presents a part of the findings from the Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) that aims to collect and analyze online public opinion towards different issues and topics in Hong Kong. The report provides an overview of public opinion on a variety of topics such as public figures, organizations, and social issues. A total of 12 online platforms including discussion forums, news portal sites, and online news media are included in the analysis (for methodological details, see Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures https://osf.io/preprints/socarxiv/b2mex/). The time span of the analysis in this report is from July 2017 to December 2019.

scholarly journals The Ebb and Flow of Public Sentiments in Hong Kong: Analyzing Public Opinion Online from 2000 to 2017

2021 ◽  
Author(s):  
Fei Shen ◽  
Chuanli Xia ◽  
Wenting Yu ◽  
Chen Min ◽  
Tianjiao Wang ◽  
...  
Keyword(s):  
Hong Kong ◽  
Public Opinion ◽  
News Media ◽  
Social Issues ◽  
Chief Executive ◽  
Online News ◽  
Discussion Forums ◽  
The Third ◽  
Public Figures ◽  
Mining Project

This report presents a part of the findings from the Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) that aims to collect and analyze online public opinion towards different issues and topics in Hong Kong. The report provides an overview of public opinion on a variety of topics such as public figures, organizations, and social issues. A total of 12 online platforms including discussion forums, news portal sites, and online news media are included in the analysis (for methodological details, see Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures https://osf.io/preprints/socarxiv/b2mex/). The time span of the analysis in this report is from January 2000 (i.e., the earliest data point obtained) to June 2017 (i.e., the end of the third Chief Executive Leung Chun-ying’s administration).

scholarly journals Public Opinion on National Exam Policies in Indonesia

2018 ◽  
Vol 1 (1) ◽  
pp. 24-35
Author(s):  
Danny Manongga ◽  
Ade Iriani ◽  
Sutarto Wijono
Keyword(s):  
Public Opinion ◽  
Extreme Events ◽  
News Media ◽  
Media Coverage ◽  
Online News ◽  
Political Pressure ◽  
Indonesian Government ◽  
National Examination ◽  
Public Sentiment ◽  
Negative Sentiment

Abstract Every new policy by Indonesian government in National Examination (NE) implementation always obtains different respond from public. Since the implementation, NE system already experienced many changes, but in recent years this system receives serious critiques. As a result, government then abolished this system as graduation determinant in 2014. This research analyzes public opinion, in the form of positive and negative sentiment toward NE policy, and factors that drive the opinions. Data in this research obtained from online news media from 2012 to 2015. The result shows that public sentiment fluctuating from year to year and depends on three important factors, i.e. political pressure, extreme events, and media coverage.

scholarly journals Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

10.2196/21504 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e21504
Author(s):  
Angela Chang ◽  
Peter Johannes Schulz ◽  
ShengTsung Tu ◽  
Matthew Tingchi Liu
Keyword(s):  
Language Processing ◽  
Social Stigma ◽  
Online Communication ◽  
Ethnic Discrimination ◽  
Online News ◽  
World Health ◽  
Discussion Forums ◽  
Public Attention ◽  
The World ◽  
Negative Sentiment

Background Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. Objective This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. Methods We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. Results We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China—and a specific city within China through references to the “Wuhan pneumonia”—potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. Conclusions Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

scholarly journals Public Opinion Polls and Experts in Election News

2016 ◽  
Vol 12 (1) ◽  
pp. 127
Author(s):  
Guðbjörg Hildur Kolbeins
Keyword(s):  
Public Opinion ◽  
News Media ◽  
News Coverage ◽  
Online News ◽  
Parliamentary Election ◽  
Radio Stations ◽  
News Stories ◽  
Election News ◽  
Public Opinion Polls ◽  
Opinion Polls

By employing the theoretical framework of framing, the present paper attempts to examine the Icelandic media’s coverage of the 2013 parliamentary election by paying particular attention to coverage of public opinion polls and the policies of the political parties, i.e. the “horse-race” frame and the issue frame, and to examine media’s reliance on experts for interpretation of election news. Seven online news media, two newspapers, two radio stations and two television channels were monitored for 25 days prior to Election Day, i.e. from April 2 to April 26, 2013, - resulting in 1377 election news stories. The findings show, for example, that 29.8% of all the election news stories had public opinion polls as their primary angle while 12% of the stories were primarily issue-oriented. In addition, the media rely on experts for interpretation of the polls; five of the 10 most interviewed or quoted sources on public opinion surveys were political science experts who were affiliated with universities. Finally, news coverage of polls was generally amplified as media outlets had a tendency to report on public opinion polls that were commissioned by other media.

scholarly journals Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques (Preprint)

2020 ◽  
Author(s):  
Angela Chang ◽  
Peter Johannes Schulz ◽  
ShengTsung Tu ◽  
Matthew Tingchi Liu
Keyword(s):  
Language Processing ◽  
Social Stigma ◽  
Online Communication ◽  
Ethnic Discrimination ◽  
Online News ◽  
World Health ◽  
Discussion Forums ◽  
Public Attention ◽  
The World ◽  
Negative Sentiment

BACKGROUND Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. OBJECTIVE This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. METHODS We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. RESULTS We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China—and a specific city within China through references to the “Wuhan pneumonia”—potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. CONCLUSIONS Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

scholarly journals Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data (Preprint)

2021 ◽  
Author(s):  
Antony Chum ◽  
Andrew Nielsen ◽  
Zachary Bellows ◽  
Eddie Farrell ◽  
Pierre-Nicolas Durette ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
News Media ◽  
School Closure ◽  
Social Distancing ◽  
Public Response ◽  
Collective Attention ◽  
Positive Sentiment ◽  
Negative Sentiment

BACKGROUND News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. OBJECTIVE Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). METHODS COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). RESULTS In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. CONCLUSIONS Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

Multimedia, interactive and hypertextual features in divergent online news platforms: An exploratory study of Flemish online news

First Monday ◽  
2011 ◽  
Author(s):  
Michael Opgenhaffen
Keyword(s):  
Content Analysis ◽  
News Media ◽  
Exploratory Study ◽  
Homogeneous Medium ◽  
Online News ◽  
Discussion Forums ◽  
Presentation Style ◽  
Online Platforms ◽  
The Web ◽  
Digital Newspapers

This paper argues for not studying the Web as one, homogeneous medium, but instead as a meta–medium that carries various divergent news media like news blogs, discussion forums, Web TV, and RSS news feeds, each with a specific presentation style. Based on a content analysis of Flemish online news media, the level of multimedia, interactivity and hypertext of different divergent news platforms are identified. Results suggest that multiple online platforms are used to present the news, even within meta–media like digital newspapers or portal sites. This analysis also reveals that the use of online news features varies among divergent news platforms, some being innovative and others rather traditional.

scholarly journals Believing is Seeing: Korean Immigrants' Perception of the Former South Korean President Roh

2014 ◽  
Author(s):  
Hyunjung Kim
Keyword(s):  
News Media ◽  
Media Framing ◽  
Online News ◽  
Individual Characteristics ◽  
Political Orientation ◽  
Korean Immigrants ◽  
Media Choice ◽  
South Korean ◽  
Public Figures ◽  
On Line

This study explores how individual characteristics interact with news media choice and people’s perception of mediated news events and public figures focusing on South Korean immigrants’ perception of the former South Korean President Roh. Thirteen South Korean immigrants were interviewed, and the results demonstrate a three-way relationship between (a) interviewees’ political orientation, (b) media choice, and (c) perception of Roh and the newspapers. The interviewees supporting Roh, who read online news on a regular basis but did not read conservative newspapers, recognized the political claim that the oligopoly of the conservative newspapers has influenced public opinion on Roh, while the readers of the conservative newspapers, who do not read on-line news, did not acknowledge the claim. Individuals’ opinions on Roh and the newspapers were not directed by media framing, but interviewees selectively chose what they read and accept.

scholarly journals Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures

2021 ◽  
Author(s):  
Fei Shen ◽  
Chuanli Xia ◽  
Wenting Yu ◽  
Chen Min ◽  
Tianjiao Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Hong Kong ◽  
Public Opinion ◽  
Social Science ◽  
The Internet ◽  
Research Grants ◽  
Mining Project ◽  
Media And Communication ◽  
Communication Methods

The Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) aims to collect and study online public opinion in Hong Kong. Funded by the Research Grants Council of Hong Kong (Project No. 11600717), this project started in January 2018 and is currently housed in the Department of Media and Communication at City University of Hong Kong. The project team is led by the principal investigator Dr. Fei Shen and comprised of scholars and researchers with specializations in communication, computer science, and other social science domains. Utilizing a big data approach, the current project uses a series of computational communication methods to extract and analyze the dynamics of public opinion on the internet in Hong Kong.

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