scholarly journals Social Media Sharing of Articles About Measles in a European Context: Text Analysis Study (Preprint)

2021 ◽  
Author(s):  
Dominik Wawrzuta ◽  
Mariusz Jaworski ◽  
Joanna Gotlib ◽  
Mariusz Panczyk
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Scientific Information ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Data Set ◽  
Vaccination Rates ◽  
Nature Of Information ◽  
Social Media Platforms

BACKGROUND Despite the existence of an effective vaccine, measles still threatens the health and lives of many Europeans. Notably, during the COVID-19 pandemic, measles vaccine uptake declined; as a result, after the pandemic, European countries will have to increase vaccination rates to restore the extent of vaccination coverage among the population. Because information obtained from social media are one of the main causes of vaccine hesitancy, knowledge of the nature of information pertaining to measles that is shared on social media may help create educational campaigns. OBJECTIVE In this study, we aim to define the characteristics of European news about measles shared on social media platforms (ie, Facebook, Twitter, and Pinterest) from 2017 to 2019. METHODS We downloaded and translated (into English) 10,305 articles on measles published in European Union countries. Using latent Dirichlet allocation, we identified main topics and estimated the sentiments expressed in these articles. Furthermore, we used linear regression to determine factors related to the number of times a given article was shared on social media. RESULTS We found that, in most European social media posts, measles is only discussed in the context of local European events. Articles containing educational information and describing world outbreaks appeared less frequently. The most common emotions identified from the study’s news data set were fear and trust. Yet, it was found that readers were more likely to share information on educational topics and the situation in Germany, Ukraine, Italy, and Samoa. A high amount of anger, joy, and sadness expressed within the text was also associated with a higher number of shares. CONCLUSIONS We identified which features of news articles were related to increased social media shares. We found that social media users prefer sharing educational news to sharing informational news. Appropriate emotional content can also increase the willingness of social media users to share an article. Effective media content that promotes measles vaccinations should contain educational or scientific information, as well as specific emotions (such as anger, joy, or sadness). Articles with this type of content may offer the best chance of disseminating vital messages to a broad social media audience.

scholarly journals Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts (Preprint)

2021 ◽  
Author(s):  
Tau Ming Liew ◽  
Cia Sin Lee
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Temporal Variations ◽  
Emotional Reactions ◽  
Barriers And Facilitators ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Public Attention ◽  
Public Concerns

BACKGROUND Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. OBJECTIVE The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. METHODS Twitter was searched for tweets related to “COVID-19” and “vaccine” over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). RESULTS Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. CONCLUSIONS The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.

scholarly journals Leveraging Natural Language Processing to Understand Public Outlook Towards the Influenza Vaccination

2021 ◽  
Author(s):  
Ankita Agarwal ◽  
William Romine ◽  
Tanvi Banerjee
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Temporal Trend ◽  
Healthcare Management ◽  
Data Set ◽  
Vaccine Preventable Diseases ◽  
The Public ◽  
Social Media Platforms ◽  
Flu Vaccine

<div>Understanding public outlook in healthcare management is important in the study of the various diseases. With respect to vaccinations, which play a major role in combating vaccine-preventable diseases, the study on their acceptance or rejection by the public becomes useful. In particular to the</div><div>influenza vaccine, studies on the public opinion and views is ongoing. Social media platforms like Twitter help us to leverage thoughts and attitudes related to the flu vaccine. The data set used for our analysis contained tweets related to vaccines which were collected using vaccine-related keywords over a period of twelve months from February, 2018 to January, 2019. Out of these tweets, we filtered out the tweets specific to the flu vaccine and generated our corpus for further study. By using Latent Dirichlet Allocation (LDA), we identified eighteen topics comprising six major themes which best represented our corpus. In this paper, we discuss these six themes and subsequently analyze the trend observed in these themes over a period of twelve months. The themes identified covered various aspects related to the flu vaccine. Among the six major themes, four showed a distinctive temporal trend with respect to the annual flu season.</div><div><br></div>

scholarly journals COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: A content analysis of Twitter data

2020 ◽  
Author(s):  
Tasmiah Nuzhath ◽  
Samia Tasnim ◽  
Rahul Kumar Sanjwal ◽  
Nusrat Fahmida Trisha ◽  
Mariya Rahman ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Vaccine Hesitancy ◽  
Preventive Strategy ◽  
Conspiracy Theories ◽  
Mortality And Morbidity ◽  
Communication Plan ◽  
Twitter Data ◽  
Social Media Platforms

Background: The coronavirus disease (COVID-19) pandemic has caused a significant burden of mortality and morbidity. A vaccine will be the most effective global preventive strategy to end the pandemic. Studies have maintained that exposure to negative sentiments related to vaccination on social media increase vaccine hesitancy and refusal. Despite the influence social media has on vaccination behavior, there is a lack of studies exploring the public's exposure to misinformation, conspiracy theories, and concerns on Twitter regarding a potential COVID-19 vaccination. Objective: The study aims to identify the major thematic areas about a potential COVID-19 vaccination based on the contents of Twitter data. Method: We retrieved 1,286,659 publicly available tweets posted within the timeline of July 19, 2020, to August 19, 2020, leveraging the Twint package. Following the extraction, we used Latent Dirichlet Allocation for topic modelling and identified 20 topics discussed in the tweets. We selected 4,868 tweets with the highest probability of belonging in the specific cluster and manually labeled as positive, negative, neutral, or irrelevant. The negative tweets were further assigned to a theme and subtheme based on the contentResult: The negative tweets were further categorized into 7 major themes: "safety and effectiveness,” "misinformation,” "conspiracy theories,” "mistrust of scientists and governments,” "lack of intent to get a COVID-19 vaccine,” "freedom of choice," and "religious beliefs. Negative tweets predominantly consisted of misleading statements (n=424) that immunization against coronavirus is unnecessary as the survival rate is high. The second most prevalent theme to emerge was tweets constituting safety and effectiveness related concerns (n=276) regarding the side effects of a potential vaccine developed at an unprecedented speed. Conclusion: Our findings suggest a need to formulate a large-scale vaccine communication plan that will address the safety concerns and debunk the misinformation and conspiracy theories spreading across social media platforms, increasing the public's acceptance of a COVID-19 vaccination.

scholarly journals Social media and vaccine hesitancy

2020 ◽  
Vol 5 (10) ◽  
pp. e004206
Author(s):  
Steven Lloyd Wilson ◽  
Charles Wiysonge
Keyword(s):  
Social Media ◽  
Vaccination Coverage ◽  
Vaccine Safety ◽  
Two Dimensions ◽  
Vaccine Hesitancy ◽  
Annual Data ◽  
Data Set ◽  
Digital Society ◽  
Vaccination Rates ◽  
Society Project

BackgroundUnderstanding the threat posed by anti-vaccination efforts on social media is critically important with the forth coming need for world wide COVID-19 vaccination programs. We globally evaluate the effect of social media and online foreign disinformation campaigns on vaccination rates and attitudes towards vaccine safety.MethodsWeuse a large-n cross-country regression framework to evaluate the effect ofsocial media on vaccine hesitancy globally. To do so, we operationalize social media usage in two dimensions: the use of it by the public to organize action(using Digital Society Project indicators), and the level of negative lyoriented discourse about vaccines on social media (using a data set of all geocoded tweets in the world from 2018-2019). In addition, we measure the level of foreign-sourced coordinated disinformation operations on social media ineach country (using Digital Society Project indicators). The outcome of vaccine hesitancy is measured in two ways. First, we use polls of what proportion ofthe public per country feels vaccines are unsafe (using Wellcome Global Monitor indicators for 137 countries). Second, we use annual data of actual vaccination rates from the WHO for 166 countries.ResultsWe found the use of social media to organise offline action to be highly predictive of the belief that vaccinations are unsafe, with such beliefs mounting as more organisation occurs on social media. In addition, the prevalence of foreign disinformation is highly statistically and substantively significant in predicting a drop in mean vaccination coverage over time. A 1-point shift upwards in the 5-point disinformation scale is associated with a 2-percentage point drop in mean vaccination coverage year over year. We also found support for the connection of foreign disinformation with negative social media activity about vaccination. The substantive effect of foreign disinformation is to increase the number of negative vaccine tweets by 15% for the median country.ConclusionThere is a significant relationship between organisation on social media and public doubts of vaccine safety. In addition, there is a substantial relationship between foreign disinformation campaigns and declining vaccination coverage.

Vaccine-Related Content, Dynamics, and Conspiracy Theories in External Links Shared in Twitter COVID-19 Conversations: A Computational Analysis (Preprint)

2021 ◽  
Author(s):  
Iain Cruickshank ◽  
Tamar Ginossar ◽  
Jason Sulskis ◽  
Elena Zheleva ◽  
Tanya Berger-Wolf
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Communication Strategy ◽  
Vaccine Hesitancy ◽  
Traditional Media ◽  
Conspiracy Theories ◽  
Wide Range ◽  
Social Media Platforms ◽  
Youtube Videos

BACKGROUND The onset of the COVID-19 pandemic and the consequent “infodemic” that ensued highlighted the role that social media play in increasing vaccine hesitancy. Despite the efforts to curtail the spread of misinformation, the anti-vaccination movement continues to use Twitter and other social media platforms to advance its messages. Although users typically engage with different social media platforms, research on vaccination discourse typically focused on single platforms. Understanding the content and dynamics of external content shared on vaccine-related conversations on Twitter during the COVID-19 pandemic can shed light on the use of different sources, including traditional media and social media by the anti-vaccination movement. In particular, examining how YouTube videos are shared within vaccination-related tweets is important in understanding the spread of anti-vaccination narratives. OBJECTIVE informed by agenda-setting theory, this study aimed to use machine-learning to understand the content and dynamics of external websites shared in vaccines-related tweets posted in COVID-19 conversations on Twitter. METHODS We screened around 5 million tweets posted to COVID-19 related conversations to include tweets that discussed vaccination. We then identified external content, including the most tweeted web domains and URLs within these tweets and the number of days they were shared. The topics and dynamics of tweeted YouTube videos were further analyzed by using Latent Dirichlet Allocation to topic-model the transcripts of the YouTube videos, and by independent coders. RESULTS of 841,896 vaccination-related tweets identified, 128,408 (22.1%) included external content. A wide range of external websites were shared. The 20 most tweeted websites constituted 10.9% of the shared websites and were typically shared for only 2-3 days within a one-month period. Traditional media constituted the majority of these 20 most tweeted URLs. Content of YouTube links shared had both the greatest number of unique URLs for any given URL domain and was the most tweeted domain over time. The majority (n=15) of the 20 most tweeted videos opposed vaccinations and featured conspiracy theories. Analysis of the transcripts of 1,280 YouTube videos shared indicated high frequency of conspiracy theories. CONCLUSIONS Our study reveals that sharing URLs over Twitter is a common communication strategy. Whereas shared URLs overall demonstrated a strong presence of legacy media organizations, YouTube videos were used to spread anti-vaccination messages. Produced by individuals or by foreign governments, these videos emerged as a major driver for sharing vaccine-related conspiracy theories. Future interventions should take into account cross-platform use to counteract this misinformation.

scholarly journals Leveraging Natural Language Processing to Understand Public Outlook Towards the Influenza Vaccination

2021 ◽  
Author(s):  
Ankita Agarwal ◽  
William Romine ◽  
Tanvi Banerjee
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Temporal Trend ◽  
Healthcare Management ◽  
Data Set ◽  
Vaccine Preventable Diseases ◽  
The Public ◽  
Social Media Platforms ◽  
Flu Vaccine

<div>Understanding public outlook in healthcare management is important in the study of the various diseases. With respect to vaccinations, which play a major role in combating vaccine-preventable diseases, the study on their acceptance or rejection by the public becomes useful. In particular to the</div><div>influenza vaccine, studies on the public opinion and views is ongoing. Social media platforms like Twitter help us to leverage thoughts and attitudes related to the flu vaccine. The data set used for our analysis contained tweets related to vaccines which were collected using vaccine-related keywords over a period of twelve months from February, 2018 to January, 2019. Out of these tweets, we filtered out the tweets specific to the flu vaccine and generated our corpus for further study. By using Latent Dirichlet Allocation (LDA), we identified eighteen topics comprising six major themes which best represented our corpus. In this paper, we discuss these six themes and subsequently analyze the trend observed in these themes over a period of twelve months. The themes identified covered various aspects related to the flu vaccine. Among the six major themes, four showed a distinctive temporal trend with respect to the annual flu season.</div><div><br></div>

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

Factor Analysis of Students’ Exposure to Social Media for Food and Beverage

2021 ◽  
Vol 8 (2) ◽  
pp. 113-118
Author(s):  
Noora Shrestha
Keyword(s):  
Social Media ◽  
Factor Analysis ◽  
Data Set ◽  
Factors Associated ◽  
Rotation Method ◽  
The Social ◽  
Food And Beverage ◽  
Social Media Platforms ◽  
Strongly Agree ◽  
Self Administered Questionnaire

Food and beverage marketing on social media is a powerful factor to influence students’ exposure to social media and application for food and beverage. It is a well-known fact that most of the food and beverage business target young people on the social media. The objective of the study is to identify the factors associated to the students’ exposure in the social media platforms for food and beverage. The young students between the ages 20 to 26 years completed a self-administered questionnaire survey on their media use for food and beverages. The questionnaire was prepared using Likert scale with five options from strongly agree to strongly disagree. The data set was described with descriptive statistics such as mean and standard deviation. The exploratory factor analysis with varimax rotation method was used to extract the factors. The most popular social media among the respondents were Facebook, Instagram, and You Tube. 73.3% of the students were exposed to food and beverage application in their mobile device and 76% of them followed the popular food and beverage pages in social media. The result revealed that social media posts, promotional offer, and hygienic concept have positively influenced majority of the students’ exposure to social media for food and beverage. Keywords: Factor analysis, Social Media, Food and Beverage, Student, Promotional Offer.

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