scholarly journals Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986546 ◽  
Author(s):  
Xiaoyi Yuan ◽  
Ross J. Schuchard ◽  
Andrew T. Crooks
Keyword(s):  
Social Media ◽  
The United States ◽  
Supervised Machine Learning ◽  
Mmr Vaccine ◽  
Health Organizations ◽  
Governmental Organizations ◽  
Measles Outbreak ◽  
Health Social Media ◽  
Twitter Users

Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

Twitter Frames

2013 ◽  
pp. 202-217 ◽  
Author(s):  
Aziz Douai ◽  
Mohamed Ben Moussa
Keyword(s):  
Social Media ◽  
Communication Technologies ◽  
Twitter Users ◽  
Media And Communication

This chapter reports preliminary findings from a larger investigation of the role of social media and communication technologies in the “Arab Democracy Spring.” The goal of the study is to analyze how Egyptian activists used Twitter during the 2011 protests. This stage of the project specifically outlines ways of identifying and classifying some of the most influential Egyptian Twitter users during these events. In addition to profiling the “influentials,” this study applies a framing perspective to understanding Twitter’s use among Egyptian activists.

Preliminary Investigation into the Role of Physiotherapists in Disaster Response

2007 ◽  
Vol 22 (5) ◽  
pp. 462-465 ◽  
Author(s):  
Rosalind M. Harrison
Keyword(s):  
Disaster Response ◽  
Potential Role ◽  
Preliminary Investigation ◽  
The United States ◽  
Multiple Roles ◽  
Burn Patients ◽  
Telephone Interviews ◽  
Governmental Organizations ◽  
Non Governmental Organizations

AbstractIntroduction:Increasingly, disasters and disaster response have become prominent issues in recent years. Despite their involvement, there have been almost no investigations into the roles of physiotherapists in emergency disaster responses.Additionally, physiotherapists are not employed in emergency disaster response by many of the principal non-governmental organizations supplying such care, although they are included in military responses in the United States and United Kingdom, and in Disaster Medical Assistance Teams in the US.This paper, based on a small qualitative study, focuses on the potential role and nature of input of physiotherapists in disaster response.Methods:A qualitative approach was chosen due to the emergent nature of the phenomenon. Four physiotherapists, all of whom had been involved in some type of disaster response, agreed to participate. Semi-structured telephone interviews were used to explore participants' experiences following disaster response, and to gain ideas about future roles for physiotherapists. Interviews were recorded, transcribed, and later analyzed using coding and categorization of data.Results:Four main themes emerged: (1) descriptions of disasters; (2) current roles of the physiotherapist; (3) future roles of physiotherapists; and (4) overcoming barriers. Although all four physiotherapists had been ill-prepared for disaster response, they took on multiple roles, primarily in organization and treatment. However, participants identified several barriers to future involvement, including organizational and professional barriers, and gave suggestions for overcoming these.Conclusions:The participants had participated in disaster response, but in ill-defined roles, indicating a need for a greater understanding of disaster response among the physiotherapy community and by organizations supplying such care. The findings of this study have implications for such organizations in terms of employing skilled physiotherapists in order to improve disaster response. In future disasters, physiotherapy will be of benefit in treating and preventing rescue worker injury and treating musculoskeletal, critical, respiratory, and burn patients.

scholarly journals Incidental Effects of Automated Retweeting: An Exploratory Network Perspective on Bot Activity During Sri Lanka’s Presidential Election in 2015

2017 ◽  
Vol 37 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Chamil Rathnayake ◽  
Wayne Buente
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Sri Lankan ◽  
Eigenvector Centrality ◽  
Data Set ◽  
Political Processes ◽  
User Expectations ◽  
Twitter Users ◽  
And Behavior

The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction.

scholarly journals Identity Crisis: The Agency of Instagram in Schools of Architecture

2018 ◽  
Author(s):  
Benjamin J. Smith ◽  
Keyword(s):  
Higher Education ◽  
United States ◽  
Social Media ◽  
Identity Formation ◽  
The United States ◽  
Identity Crisis ◽  
Prospective Student ◽  
Representational Practice ◽  
Contemporary Architecture

The focus of this paper addresses themes of neoliberalism, university commercialization and marketing, architecture school identity formation as a representational practice through social media, and the role of image curation and its production in contemporary architecture. This paper emerged after hearing the phrase ‘buyer’s motive,’ which explained what schools needed to consider for attracting students to their programs at a conference by Ruffalo Noel Levtiz on recruitment, marketing, and retention in higher education in the United States. The use of the word, ‘buyer’, instead of ‘student’, or ‘prospective student’, or ‘learner’ seemingly transformed the production of engaged education to its passive consumption.

scholarly journals Specific features of using social media in presidential campaigns in 2016 and 2020 in the USA: a comparative analysis

2021 ◽  
Vol 5 (3) ◽  
pp. 172-180
Author(s):  
D. Klyuchevskiy
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Comparative Analysis ◽  
Presidential Election ◽  
Political Process ◽  
The United States ◽  
Political Marketing ◽  
Theoretical Contribution ◽  
The Us

The purpose of this article is to analyze the experience of using social networks as a political marketing tool in the US presidential elections. This article partially touches upon the global topic of marketization and digitalization of both the political process in general and at the level of the US presidential election. The paper highlights the changing role of social media as a policy tool, which today has become not only a tool for distributing content, but also one of the tools for analyzing data from the electorate. The author explores the possibilities of social networks, their strengths and weaknesses and development prospects in the field of political marketing. The work touches upon the role of social networks in the formation of «Electronic Democracy», their impact on the candidate's image and the relationship with the personalization of politics in the United States. The main method in the article is comparative analysis. The result was the definition of the role, key features of the mentioned social networks in the field of modern politics. A certain theoretical contribution is seen in the argumentation of the following observations: the speed of interaction between the candidate and the voter through social networks has increased, in addition, the area of image-making has been partially «digitalized». It was revealed that technologies of information influence on American voters, which positively influenced the results of the 2016 presidential election for the Republican candidate, lowered D. Trump's ratings during the 2020 elections.

scholarly journals Public Opinions towards COVID-19 in California and New York on Twitter

2020 ◽  
Author(s):  
Xueting Wang ◽  
Canruo Zou ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
New York ◽  
Social Media ◽  
Topic Modeling ◽  
New York State ◽  
The United States ◽  
Public Opinions ◽  
Online Social Media ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Over Time

Background: With the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak. Objective: This study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States. Methods: A dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19. Results: While the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states. Conclusions: Twitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

scholarly journals Exploring the Role of Media Sources on COVID-19–Related Discrimination Experiences and Concerns Among Asian People in the United States: Cross-Sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Nan Yu ◽  
Shuya Pan ◽  
Chia-chen Yang ◽  
Jiun-Yi Tsai
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Asian Americans ◽  
Asian American ◽  
The United States ◽  
Cross Sectional ◽  
Racial Biases ◽  
Asian People

BACKGROUND Media coverage and scholarly research have reported that Asian people who reside in the United States have been the targets of racially motivated incidents during the COVID-19 pandemic. OBJECTIVE This study aimed to examine the types of discrimination and worries experienced by Asians and Asian Americans living in the United States during the pandemic, as well as factors that were associated with everyday discrimination experience and concerns about future discrimination that the Asian community may face. METHODS A cross-sectional online survey was conducted. A total of 235 people who identified themselves as Asian or Asian American and resided in the United States completed the questionnaire. RESULTS Our study suggested that up to a third of Asians surveyed had experienced some type of discrimination. Pooling the responses “very often,” “often,” and “sometimes,” the percentages for each experienced discrimination type ranged between 14%-34%. In total, 49%-58% of respondents expressed concerns about discrimination in the future. The most frequently experienced discrimination types, as indicated by responses “very often” and “often,” were “people act as if they think you are dangerous” (25/235, 11%) and “being treated with less courtesy or respect” (24/235, 10%). About 14% (32/235) of individuals reported very often, often, or sometimes being threatened or harassed. In addition, social media use was significantly associated with a higher likelihood of experiencing discrimination (β=.18, <i>P</i>=.01) and having concerns about future episodes of discrimination the community may face (β=.20, <i>P</i>=.005). Use of print media was also positively associated with experiencing discrimination (β=.31, <i>P</i>&lt;.001). CONCLUSIONS Our study provided important empirical evidence regarding the various types of discrimination Asians residing in the United States experienced or worried about during the COVID-19 pandemic. The relationship between media sources and the perception of racial biases in this group was also identified. We noted the role of social media in reinforcing the perception of discrimination experience and concerns about future discrimination among Asians during this outbreak. Our results indicate several practical implications for public health agencies. To reduce discrimination against Asians during the pandemic, official sources and public health professionals should be cognizant of the possible impacts of stigmatizing cues in media reports on activating racial biases. Furthermore, Asians or Asian Americans could also be informed that using social media to obtain COVID-19 information is associated with an increase in concerns about future discrimination, and thus they may consider approaching this media source with caution.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

scholarly journals Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017–2019

2020 ◽  
Vol 110 (S3) ◽  
pp. S319-S325 ◽  
Author(s):  
Adam G. Dunn ◽  
Didi Surian ◽  
Jason Dalmazzo ◽  
Dana Rezazadegan ◽  
Maryke Steffens ◽  
...  
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Odds Ratio ◽  
The United States ◽  
Median Number ◽  
Critical Information ◽  
Critical Content ◽  
Twitter Users ◽  
Vaccine Information

Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States. Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017–December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets. Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168–4435), of which 27 (IQR = 6–169) were vaccine critical, and 0 (IQR = 0–12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3). Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.

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