Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong

2021 ◽  
Vol 12 (4) ◽  
pp. 1-20
Author(s):  
Guanqing Liang ◽  
Jingxin Zhao ◽  
Helena Yan Ping Lau ◽  
Cane Wing-Ki Leung
Keyword(s):  
Social Media ◽  
Hong Kong ◽  
Large Scale ◽  
Early Stage ◽  
Media Analysis ◽  
Classification Model ◽  
Public Discussion ◽  
The Public ◽  
Evaluation Phase ◽  
Key Issues

The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.

Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts

2021 ◽  
Author(s):  
Sterling E Braun ◽  
Michaela K O’Connor ◽  
Margaret M Hornick ◽  
Melissa E Cullom ◽  
James A Butterworth
Keyword(s):  
Social Media ◽  
Plastic Surgery ◽  
Large Scale ◽  
Descriptive Analysis ◽  
The United States ◽  
Media Analysis ◽  
Classification Model ◽  
Global Trends ◽  
Large Scale Data ◽  
Textual Data

Abstract Background Plastic Surgeons and patients increasingly use social media. Despite evidence implicating its importance in Plastic Surgery, the large amount of data has made social media difficult to study. Objectives This study seeks to provide a comprehensive assessment of Plastic Surgery content throughout the world using techniques for analyzing large-scale data. Methods ‘#PlasticSurgery’ was used to search public Instagram posts. Metadata was collected from posts between December 2018 and August 2020. In addition to descriptive analysis, we created two instruments to characterize textual data: a multi-lingual dictionary of procedural hashtags and a rule-based text classification model to categorize the source of the post. Results Plastic Surgery content yielded more than 2 million posts, 369 million likes, and 6 billion views globally over the 21-month study. The United States had the most posts of 182 countries studied (26.8%, 566,206). Various other regions had substantial presence including Istanbul, Turkey, which led all cities (4.8%, 102,208). Our classification model achieved high accuracy (94.9%) and strong agreement with independent raters (κ= 0.88). Providers accounted for 40% of all posts (847,356) and included Physician (28%), Plastic Surgery (9%), Advanced-Practice-Practitioners and Nurses (1.6%), Facial Plastics (1.3%), and Oculoplastics (0.2%). Content between Plastics and non-Plastics groups demonstrated high textual similarity, and only 1.4% of posts had a verified source. Conclusions Plastic Surgery content has immense global reach in social media. Textual similarity between groups coupled with the lack of an effective verification mechanism presents challenges in discerning the source and veracity of information.

Canada

2018 ◽  
Author(s):  
Elizabeth Dubois ◽  
Fenwick McKelvey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Civil Society ◽  
Media Literacy ◽  
Qualitative Interviews ◽  
Media Analysis ◽  
Digital Systems ◽  
Social Media Analysis ◽  
Public Discussion ◽  
The Public

Are bots active in Canada? Yes. Are they influential? Maybe. Using a combination of quantitative social media analysis, content analysis of news articles, and qualitative interviews, we study the use of political bots in Canada. We identify four kinds of bots. Amplifiers game digital systems to promote a message or channel. Dampeners suppress and remove information online. Alongside these problematic bots, we also find a number of benign bots that help journalists, civil society, and governments. These bots include transparency bots that disclose information to the public and servant bots that help maintain services and infrastructures. Even though bots might not yet be influential in Canada, improved media literacy and increased public discussion of the pitfalls of social media are required.

scholarly journals The public accountability of social platforms: lessons from a study on bots and trolls in the Brexit campaign

2018 ◽  
Vol 376 (2128) ◽  
pp. 20180003 ◽  
Author(s):  
Marco Bastos ◽  
Dan Mercea
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ethical Issues ◽  
Lessons Learned ◽  
Information Warfare ◽  
The Public ◽  
Social Media Platforms ◽  
Application Programming ◽  
Access To Data ◽  
The Uk

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

Liable for Unlawful Assembly and Riot by Virtue of Liking or Comment on Social Media Platforms?

2021 ◽  
pp. 002201832110546
Author(s):  
Trevor TW Wan ◽  
Thomas Yeon
Keyword(s):  
Social Media ◽  
Hong Kong ◽  
Crime Scene ◽  
Public Order ◽  
The Public ◽  
Physical Presence ◽  
Social Media Platforms ◽  
Joint Enterprise ◽  
Court Of Appeal ◽  
Evidential Basis

In Secretary of Justice v Tong Wai Hung [2021] HKCA 404, the Hong Kong Court of Appeal affirmed that the doctrine of joint enterprise, as a matter of statutory construction, is applicable onwards to the offences of unlawful assembly and riot under the Public Order Ordinance (Cap. 245), and physical presence at the crime scene is not a pre-requisite to establish liability. The Court argued that such an interpretation strikes a balance between public order concerns and the need to avoid the risk of over-charging. This note contends that the Court of Appeal’s decision will risk exposing numerous citizens, who can hardly be said to share culpability comparable to that of the actual and principal perpetrators of unlawful and riotous assemblies, to prosecution and conviction on questionable legal and evidential basis.

scholarly journals Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7115
Author(s):  
Amin Muhammad Sadiq ◽  
Huynsik Ahn ◽  
Young Bok Choi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Activity Analysis ◽  
Physical Activities ◽  
Future Research ◽  
Crowd Sourcing ◽  
Emergency Relief ◽  
The Public ◽  
Text Streams

A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

scholarly journals Whitewashing Narratives: Israeli Media and the Framing of the Kidnapped Yemenite Babies Affair

2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Shoshana Madmoni-Gerber
Keyword(s):  
Social Media ◽  
Public Sphere ◽  
Identity Formation ◽  
Public Awareness ◽  
Media Analysis ◽  
Future Generations ◽  
The Public ◽  
The Public Sphere ◽  
Society Today ◽  
Full Cooperation

This essay offers a review of ongoing media analysis of the kidnapped Yemenite Babies Affair in light of recent changes in public awareness since the emergence of social media and the more recent formal governmental recognition. It argues that the government’s efforts to silence this affair over decades would not have been possible without the media’s full cooperation. Moreover, the public denial of this affair contributes to the ongoing intra-Jewish rift and racism in Israeli society today. Questions regarding the reconciliation and remembrance of this affair in the public sphere will strongly influence the identity formation of Yemenite and Mizrahi children of future generations.  

scholarly journals A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Meng Cai ◽  
Han Luo ◽  
Ying Cui
Keyword(s):  
Public Health ◽  
Social Media ◽  
Time Series ◽  
Public Opinion ◽  
Public Health Emergency ◽  
Classification Model ◽  
Temporal Networks ◽  
Public Opinions ◽  
The Public ◽  
And Diffusion

With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.

scholarly journals Developmental Trend of Subjective Well-Being of Weibo Users During COVID-19: Online Text Analysis Based on Machine Learning Method

2022 ◽  
Vol 12 ◽  
Author(s):  
Yingying Han ◽  
Wenhao Pan ◽  
Jinjin Li ◽  
Ting Zhang ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Well Being ◽  
Development Rate ◽  
Classification Model ◽  
Dynamic State ◽  
Developmental Trend ◽  
Subjective Well Being ◽  
Latent Growth ◽  
The Public

Currently, the coronavirus disease 2019 (COVID-19) pandemic experienced by the international community has increased the usage frequency of borderless, highly personalized social media platforms of all age groups. Analyzing and modeling texts sent through social media online can reveal the characteristics of the psychological dynamic state and living conditions of social media users during the pandemic more extensively and comprehensively. This study selects the Sina Weibo platform, which is highly popular in China and analyzes the subjective well-being (SWB) of Weibo users during the COVID-19 pandemic in combination with the machine learning classification algorithm. The study first invokes the SWB classification model to classify the SWB level of original texts released by 1,322 Weibo active users during the COVID-19 pandemic and then combines the latent growth curve model (LGCM) and the latent growth mixture model (LGMM) to investigate the developmental trend and heterogeneity characteristics of the SWB of Weibo users after the COVID-19 outbreak. The results present a downward trend and then an upward trend of the SWB of Weibo users during the pandemic as a whole. There was a significant correlation between the initial state and the development rate of the SWB after the COVID-19 outbreak (r = 0.36, p < 0.001). LGMM results show that there were two heterogeneous classes of the SWB after the COVID-19 outbreak, and the development rate of the SWB of the two classes was significantly different. The larger class (normal growth group; n = 1,229, 93.7%) showed a slow growth, while the smaller class (high growth group; n = 93, 6.3%) showed a rapid growth. Furthermore, the slope means across the two classes were significantly different (p < 0.001). Therefore, the individuals with a higher growth rate of SWB exhibited stronger adaptability to the changes in their living environments. These results could help to formulate effective interventions on the mental health level of the public after the public health emergency outbreak.

scholarly journals Interaction and Transformation on Social Media: The Case of Twitter Campaigns

2018 ◽  
Vol 4 (1) ◽  
pp. 205630511775072 ◽  
Author(s):  
William Housley ◽  
Helena Webb ◽  
Meredydd Williams ◽  
Rob Procter ◽  
Adam Edwards ◽  
...  
Keyword(s):  
Social Media ◽  
Modern Society ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Media Campaigns ◽  
The Public ◽  
The Social ◽  
Wide Range ◽  
Social Media Platforms ◽  
Insight Into

The increasing popularity of social media platforms creates new digital social networks in which individuals can interact and share information, news, and opinion. The use of these technologies appears to have the capacity to transform current social configurations and relations, not least within the public and civic spheres. Within the social sciences, much emphasis has been placed on conceptualizing social media’s role in modern society and the interrelationships between online and offline actors and events. In contrast, little attention has been paid to exploring user practices on social media and how individual posts respond to each other. To demonstrate the value of an interactional approach toward social media analysis, we performed a detailed analysis of Twitter-based online campaigns. After categorizing social media posts based on action(s), we developed a typology of user exchanges. We found these social media campaigns to be highly heterogeneous in content, with a wide range of actions performed and substantial numbers of tweets not engaged with the substance of the campaign. We argue that this interactional approach can form the basis for further work conceptualizing the broader impact of activist campaigns and the treatment of social media as “data” more generally. In this way, analytic focus on interactional practices on social media can provide empirical insight into the micro-transformational characteristics within “campaign communication.”

scholarly journals Preliminary yield estimation of the 2020 Beirut explosion using video footage from social media

Shock Waves ◽  
2020 ◽  
Vol 30 (6) ◽  
pp. 671-675 ◽  
Author(s):  
S. E. Rigby ◽  
T. J. Lodge ◽  
S. Alotaibi ◽  
A. D. Barr ◽  
S. D. Clarke ◽  
...  
Keyword(s):  
Social Media ◽  
Emergency Response ◽  
Large Scale ◽  
Accurate Information ◽  
Time Of Arrival ◽  
Policy Makers ◽  
The Public ◽  
Video Footage ◽  
The Moment ◽  
Clear Line

Abstract Rapid, accurate assessment of the yield of a large-scale urban explosion will assist in implementing emergency response plans, will facilitate better estimates of areas at risk of high damage and casualties, and will provide policy makers and the public with more accurate information about the event. On 4 August 2020, an explosion occurred in the Port of Beirut, Lebanon. Shortly afterwards, a number of videos were posted to social media showing the moment of detonation and propagation of the resulting blast wave. In this article, we present a method to rapidly calculate explosive yield based on analysis of 16 videos with a clear line-of-sight to the explosion. The time of arrival of the blast is estimated at 38 distinct positions, and the results are correlated with well-known empirical laws in order to estimate explosive yield. The best estimate and reasonable upper limit of the 2020 Beirut explosion determined from this method are 0.50 kt TNT and 1.12 kt TNT, respectively.

Sign in / Sign up

Export Citation Format

Share Document