Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study (Preprint)

2021 ◽  
Author(s):  
Jueman Zhang ◽  
Yi Wang ◽  
Molu Shi ◽  
Xiuli Wang
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Vaccine Efficacy ◽  
Topic Modeling ◽  
English Language ◽  
Vaccine Development ◽  
Linguistic Features ◽  
The Public ◽  
Social Media Platforms ◽  
Positive Valence

BACKGROUND COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. OBJECTIVE The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. METHODS We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. RESULTS Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people’s views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people’s views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. CONCLUSIONS This study suggests the public interest in and demand for information about vaccine development and people’s views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.

scholarly journals (Re)constructing Teacher Identity through the Contesting Narratives of ELT Associations

2021 ◽  
Vol 50 (2) ◽  
pp. 130-141
Author(s):  
Ramesh Nair ◽  
Keyword(s):  
Teacher Identity ◽  
English Language ◽  
Language Teachers ◽  
English Language Teaching ◽  
Contributing Factors ◽  
Multimodal Texts ◽  
The Public ◽  
Mainstream Media ◽  
Social Media Platforms ◽  
English Language Teachers

Discussions in the mainstream media about the declining standard of English in Malaysia have focused on a variety of contributing factors, one of the more prominent being the quality of teaching. English language teachers have been central actors in these narratives and are often easy targets for assigning blame. Left uncontested, such narratives have the capacity to shape a damaging image of Malaysian English language teachers which can have lasting implications for the ELT profession in the country. Fortunately, alternative voices emerge to challenge narratives describing Malaysian English language teachers as inept and incompetent. In this paper, I examine such narratives as they are presented through multimodal texts published and circulated in the public domain by the Malaysian English Language Teaching Association. Drawing on the frameworks of Systemic-Functional Linguistics and visual grammar, I examine a series of posters disseminated through the association’s social media platforms. The analysis unpacks the language and images used in the posters, and reveals an alternative discourse in which these teachers are presented as trained professionals with expertise in their field of ELT. The study highlights the important role of ELT associations in representing its members by challenging emerging discourses which threaten the reputation of the profession.

scholarly journals Modeling the public attitude towards organic foods: a big data and text mining approach

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Anupam Singh ◽  
Aldona Glińska-Neweś
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Food Consumption ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Food Delivery ◽  
Public Attitude ◽  
Organic Foods ◽  
The Public

AbstractThis study aims to identify the topics that users post on Twitter about organic foods and to analyze the emotion-based sentiment of those tweets. The study addresses a call for an application of big data and text mining in different fields of research, as well as proposes more objective research methods in studies on food consumption. There is a growing interest in understanding consumer choices for foods which are caused by the predominant contribution of the food industry to climate change. So far, customer attitudes towards organic food have been studied mostly with self-reported methods, such as questionnaires and interviews, which have many limitations. Therefore, in the present study, we used big data and text mining techniques as more objective methods to analyze the public attitude about organic foods. A total of 43,724 Twitter posts were extracted with streaming Application Programming Interface (API). Latent Dirichlet Allocation (LDA) algorithm was applied for topic modeling. A test of topic significance was performed to evaluate the quality of the topics. Public sentiment was analyzed based on the NRC emotion lexicon by utilizing Syuzhet package. Topic modeling results showed that people discuss on variety of themes related to organic foods such as plant-based diet, saving the planet, organic farming and standardization, authenticity, and food delivery, etc. Sentiment analysis results suggest that people view organic foods positively, though there are also people who are skeptical about the claims that organic foods are natural and free from chemicals and pesticides. The study contributes to the field of consumer behavior by implementing research methods grounded in text mining and big data. The study contributes also to the advancement of research in the field of sustainable food consumption by providing a fresh perspective on public attitude toward organic foods, filling the gaps in existing literature and research.

Manually Profiling Egos and Entities across Social Media Platforms

2017 ◽  
pp. 352-405
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Network Analysis ◽  
Analytical Techniques ◽  
Software Tools ◽  
Text Summarization ◽  
The Public ◽  
Social Media Platforms

Social media accounts on various social media platforms represent the public-facing Web presences of egos (individuals) and entities (groups). On the surface, these may be understood based on their profiles, their shared contents and postings, and their interactions with other user accounts online. A number of software tools and analytical techniques enable further analyses of these accounts through network analysis, content analysis, machine-based text summarization, and other approaches. This chapter describes some of the capabilities of “manual” or semi-automated (vs. fully automated) remote profiling of social media accounts for insights that would not generally be attainable by other means.

scholarly journals Analysis of Issues Based on Disaster Type Using Text Mining and a Semantic Network

2021 ◽  
Vol 21 (3) ◽  
pp. 49-60
Author(s):  
Tae Jin Kim ◽  
Mi Ryeong Eum ◽  
Sang Hyun Park
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Forest Fire ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
The Public ◽  
State Administration ◽  
The Government ◽  
Media Channels ◽  
Negative Sentiment

Recently, the government has been increasingly communicating with the public in response to their opinions on state administration and policy projects. To examine the practicality of the public’s suggestions, this study investigated issues by disaster type, based on information from major media channels and comment data from the news. An analysis of the frequency of appearance, text mining (TF-IDF, LDA, and sentiment analysis), and the semantic network was performed by extracting the comment data of articles on the themes of “disaster” and “evacuation,” published from January 2010 to May 2020. The analysis results showed that news articles centered on these themes increased rapidly from 2017. The main disasters in Korea were those of “fire,” “typhoon,” “forest fire,” “radioactivity,” and “earthquake,” in order of enormity. Of the total negative words pertaining to “radioactivity” disasters, 43% were negative-sentiment words, and the semantic network analysis revealed that the terms “typhoon,” “forest fire,” and “earthquake” were connected to “radioactivity” disasters. This study is meaningful as it identifies issues by type of disaster and factors of anxiety expressed by the public using news and comment data, without conducting surveys and interviews.

scholarly journals Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach (Preprint)

2021 ◽  
Author(s):  
Chen Luo ◽  
Kaiyuan Ji ◽  
Yulong Tang ◽  
Zhiyuan Du
Keyword(s):  
Text Mining ◽  
Topic Modeling ◽  
Vaccine Development ◽  
Vaccination Campaign ◽  
Vaccine Effectiveness ◽  
Social Implications ◽  
Significant Difference ◽  
Question And Answer ◽  
Question Category ◽  
Expression Characteristics

BACKGROUND COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines. OBJECTIVE This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context. METHODS We collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups. RESULTS Laypeople are more prevailing in the COVID-19 vaccine–related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the “safety of Chinese-made vaccine” topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference. CONCLUSIONS Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as “qualitatively different” in perspective rather than “quantitatively different” in scientific knowledge. Among those questions closely associated with medical expertise, the “qualitatively different” characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

scholarly journals The Electability Level of PDI-P on Social Media of Instagram

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Resky Eka Rachmandani ◽  
Eko Priyo Purnomo ◽  
Aulia Nur Kasiwi
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Network Analysis ◽  
Political Parties ◽  
Political Party ◽  
The Political ◽  
The Public ◽  
Social Media Platforms ◽  
Media Channels ◽  
Analyze Data

In this era of the 21st century, political parties have many ways to develop a campaign, including on social media as a new medium to do a campaign. Partai Demokrasi Indonesia Perjuangan (PDI-P) has its own strategy toward social media to achieve their power in society. This research aims to discover the level of electability of PDI-P through Instagram, one of social media platforms being used to make social interaction with the public. Starting from Instagram, this research has decided three criteria: basic of election mode, media channels, and technological access to political party that can be seen by public. The analysis is conducted to a variety of large and small media outlets that analyze and explore things beyond political parties. This research uses Nvivo12 Plus as network analysis tools to identify and analyze data from Instagram related to PDI-P strategy. The results of this study show that social media has a significant role in pursuing and maintaining the power of political parties to stay strong in society’s life. This research also develops a novelty, based on the results, that social media has a strong power to build a strategy and accept plurality in society’s behavior, instead of building a strong branding of the political party itself and stand out as candidates.  

scholarly journals #Feminism hashtag feminism and the logic of connective action

2021 ◽  
Author(s):  
Jacqueline Stevens
Keyword(s):  
Network Analysis ◽  
Public Sphere ◽  
Dominant Role ◽  
Integral Form ◽  
Major Research ◽  
The Public ◽  
Social Media Platforms ◽  
Research Questions ◽  
Connective Action ◽  
The Public Sphere

Cyberactivism describes the ways in which the internet and social media platforms enable users to become activists. Hashtag feminism has become an integral form of cyberactivism which promotes gender equality and fights against today’s most prominent women’s issues. Focusing on the hashtags #Feminist, #Feminism, and #Genderequality, this major research project uses Social Network Analysis (SNA) to examine hashtag feminism networks on Twitter in relation to the logic of connective action. The research questions of this paper explore the density, centralization, modularity and cluster characteristics of hashtag feminism networks, and question whether feminist hashtags are implemented by members of countermovements. In addition, this paper references Habermas’s theory of the public sphere to ground the analysis of hashtag feminism. The results of the network analysis suggests that Twitter may serve as a counterpublic in which both feminists and anti-feminists can produce and participate in discourses that represent their interests and identities. Additionally, the analysis found that organizations tend to hold the most dominant role in hashtag feminism Twitter networks.

scholarly journals #Feminism hashtag feminism and the logic of connective action

2021 ◽  
Author(s):  
Jacqueline Stevens
Keyword(s):  
Network Analysis ◽  
Public Sphere ◽  
Dominant Role ◽  
Integral Form ◽  
Major Research ◽  
The Public ◽  
Social Media Platforms ◽  
Research Questions ◽  
Connective Action ◽  
The Public Sphere

Cyberactivism describes the ways in which the internet and social media platforms enable users to become activists. Hashtag feminism has become an integral form of cyberactivism which promotes gender equality and fights against today’s most prominent women’s issues. Focusing on the hashtags #Feminist, #Feminism, and #Genderequality, this major research project uses Social Network Analysis (SNA) to examine hashtag feminism networks on Twitter in relation to the logic of connective action. The research questions of this paper explore the density, centralization, modularity and cluster characteristics of hashtag feminism networks, and question whether feminist hashtags are implemented by members of countermovements. In addition, this paper references Habermas’s theory of the public sphere to ground the analysis of hashtag feminism. The results of the network analysis suggests that Twitter may serve as a counterpublic in which both feminists and anti-feminists can produce and participate in discourses that represent their interests and identities. Additionally, the analysis found that organizations tend to hold the most dominant role in hashtag feminism Twitter networks.

scholarly journals Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110214
Author(s):  
Martin Schweinberger ◽  
Michael Haugh ◽  
Sam Hames
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Corpus Linguistics ◽  
Public Discourse ◽  
Sampling Methods ◽  
The Public ◽  
Corpus Linguistic ◽  
Social Media Platforms ◽  
Linguistic Methods ◽  
Over Time

Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which phases characterize its development. They also confound structured behavioural data (i.e., tagging) with unstructured user-generated data (i.e., content of tweets) in their sampling methods. The present study aims to demonstrate how one might go about addressing both of these sets of challenges by combining corpus linguistic methods with a data-driven text-mining approach to gain a better understanding of how the public discourse around COVID-19 developed over time and what topics combine to form this discourse in the Australian Twittersphere over a period of nearly four months. By combining text mining and corpus linguistics, this study exemplifies how both approaches can complement each other productively.

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