scholarly journals Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

2020 ◽  
Vol 17 (22) ◽  
pp. 8701 ◽  
Author(s):  
Wen Shi ◽  
Diyi Liu ◽  
Jing Yang ◽  
Jing Zhang ◽  
Sanmei Wen ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Healthcare System ◽  
Health Risks ◽  
Emotional Support ◽  
Social Distancing ◽  
The Us ◽  
Almost All

During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

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

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 ◽  
Series Data ◽  
Social Distancing ◽  
Public Response ◽  
Collective Attention ◽  
Positive 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

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.

Trend of Parental Refusal or Delay of Childhood Vaccinations

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Meagan Marie Daoust
Keyword(s):  
Public Health ◽  
Social Media ◽  
Healthcare System ◽  
Educational Context ◽  
Health Consequences ◽  
Missed Opportunities ◽  
The Public ◽  
Health Related ◽  
Parental Refusal

The healthcare trend of parental refusal or delay of childhood vaccinations will be investigated through a complex Cynefin Framework component in an economic and educational context, allowing patterns to emerge that suggest recommendations of change for the RN role and healthcare system. As a major contributing factor adding complexity to this trend, social media is heavily used for health related knowledge, making it is difficult to determine which information is most trustworthy. Missed opportunities for immunization can result, leading to economic and health consequences for the healthcare system and population. Through analysis of the powerful impact social media has on this evolving trend and public health, an upstream recommendation for RNs to respond with is to utilize reliable social media to the parents’ advantage within practice. The healthcare system should focus on incorporating vaccine-related education into existing programs and classes offered to parents, and implementing new vaccine classes for the public.

The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

2020 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Komang Dhiyo Yonatha Wijaya ◽  
Anak Agung Istri Ngurah Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Kernel Method ◽  
Idle Time ◽  
Linear Kernel ◽  
Social Distancing ◽  
The Social ◽  
Negative Sentiment ◽  
F Measure ◽  
Kernel Yield

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

Profiling the Users of High Influence on Social Media in the Context of Public Events

2021 ◽  
Vol 32 (2) ◽  
pp. 36-49
Author(s):  
Lu An ◽  
Junyang Hu ◽  
Manting Xu ◽  
Gang Li ◽  
Chuanming Yu
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Emergency Management ◽  
Emotional Support ◽  
Opinion Leader ◽  
Negative Effects ◽  
The Public ◽  
Public Events ◽  
Social Media Platforms ◽  
Influential Users

The highly influential users on social media platforms may lead the public opinion about public events and have positive or negative effects on the later evolution of events. Identifying highly influential users on social media is of great significance for the management of public opinion in the context of public events. In this study, the highly influential users of social media are divided into three types (i.e., topic initiator, opinion leader, and opinion reverser). A method of profiling highly influential users is proposed based on topic consistency and emotional support. The event of “Jiankui He Editing the Infants' Genes” was investigated. The three types of users were identified, and their opinion differences and dynamic evolution were revealed. The comprehensive profiles of highly influential users were constructed. The findings can help emergency management departments master the focus of attention and emotional attitudes of the key users and provide the method and data support for opinion management and decision-making of public events.

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

2020 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

Revealing public opinion towards COVID-19 vaccines with Twitter data in the United States: a spatiotemporal perspective (Preprint)

2021 ◽  
Author(s):  
Tao Hu ◽  
Siqin Wang ◽  
Wei Luo ◽  
Mengxi Zhang ◽  
Xiao Huang ◽  
...  
Keyword(s):  
Public Health ◽  
Public Opinion ◽  
Public Attitudes ◽  
The United States ◽  
Health Crisis ◽  
The Public ◽  
Public Sentiment ◽  
Spatiotemporal Trends ◽  
The Us ◽  
Key Events

BACKGROUND The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the US and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance OBJECTIVE The aim of this study is to investigate public opinion and perception on COVID-19 vaccines by investigating the spatiotemporal trends of their sentiment and emotion towards vaccines, as well as how such trends relate to popular topics on Twitter in the US METHODS We collected over 300,000 geotagged tweets in the US from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified three phases along the pandemic timeline with the significant changes of public sentiment and emotion, further linking to eleven key events and major topics as the potential drivers to induce such changes via cloud mapping of keywords and topic modelling RESULTS An increasing trend of positive sentiment in parallel with the decrease of negative sentiment are generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the eight types of emotion implies the trustiness and anticipation of the public to vaccination, accompanied by the mixture of fear, sadness and anger. Critical social/international events and/or the announcements of political leaders and authorities may have potential impacts on the public opinion on vaccines. These factors, along with important topics and manual reading of popular posts on eleven key events, help identify underlying themes and validate insights from the analysis CONCLUSIONS The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics and promote the confidence of individuals within a certain region or community, towards vaccines

scholarly journals Blurred Shots: Investigating the Information Crisis Around Vaccination in Italy

2020 ◽  
pp. 000276422091024
Author(s):  
Alessandro Lovari ◽  
Valentina Martino ◽  
Nicola Righetti
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
Public Relations ◽  
Health Communication ◽  
News Media ◽  
Media Coverage ◽  
Social Consequences ◽  
Combining Data ◽  
Italian Context

This article aims at exploring a case of information crisis in Italy through the lens of vaccination-related topics. Such a controversial issue, dividing public opinion and political agendas, has received diverse information coverage and public policies over time in the Italian context, whose situation appears quite unique compared with other countries because of a strong media spectacularization and politicization of the topic. In particular, approval of the “Lorenzin Decree,” increasing the number of mandatory vaccinations from 4 to 10, generated a nationwide debate that divided public opinion and political parties, triggering a complex informative crisis and fostering the perception of a social emergency on social media. This resulted in negative stress on lay publics and on the public health system. The study adopted an interdisciplinary framework, including political science, public relations, and health communication studies, as well as a mixed-method approach, combining data mining techniques related to news media coverage and social media engagement, with in-depth interviews to key experts, selected among researchers, journalists, and communication managers. The article investigates reasons for the information crisis and identifies possible solutions and interventions to improve the effectiveness of public health communication and mitigate the social consequences of misinformation around vaccination.

'Perfect storm' of Venezuela risks surround COVID-19

2020 ◽  
Keyword(s):  
Public Health ◽  
Health Risks ◽  
Humanitarian Aid ◽  
Content Type ◽  
The Us ◽  
Perfect Storm

Significance Washington is increasing pressure on President Nicolas Maduro at a time of heightened vulnerability for both the Venezuelan government and people. It is a risky strategy on political and public health grounds. Impacts The US ‘carrot and stick’ approach is contradictory and encourages Maduro and his officials to hunker down. Without agreement on humanitarian aid or the lifting of US sanctions, Venezuelans face grave health risks. Falling remittances will deal a new blow to the tottering economy.

scholarly journals Mining and Validating Social Media for COVID-19-Related Human Behaviors between January and July; An Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Ashlynn R. Daughton ◽  
Courtney Diane Shelley ◽  
Martha Barnard ◽  
Dax Gerts ◽  
Chrysm Watson Ross ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Ground Truth ◽  
The United States ◽  
Training Data ◽  
Long Term Effects ◽  
Social Distancing ◽  
Human Behaviors ◽  
Mobility Data ◽  
The Impact

BACKGROUND Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging is also important for minimizing long term effects of an outbreak. OBJECTIVE We used social media data to identify human behaviors relevant to COVID-19 transmission and the perceived impacts of COVID-19 on individuals as a first step toward real time monitoring of public perceptions to inform public health communications. METHODS We develop a coding schema for 6 categories and 11 subcategories, which includes both a wide number of behaviors, as well codes focused on the impacts of the pandemic (e.g., economic and mental health impacts). We use this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that perform adequately are applied to our remaining corpus and temporal and geospatial trends are assessed. We compare the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS We apply our labeling schema to ~7200 tweets. The worst performing classifiers have F1 scores of only 0.18-0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, are much stronger with F1 scores of 0.64-0.66. We applied the social distancing classifiers to over 228 million tweets. We show temporal patterns consistent with real-world events, and show correlations of up to -0.5 between social distancing signals on Twitter and ground-truth mobility throughout the United States. CONCLUSIONS Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior as well as informing public health communication strategies by describing awareness of and compliance with suggested behaviors. CLINICALTRIAL N/A

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