The popularity and virality of political social media: hashtags, mentions, and links predict likes and retweets of 2016 U.S. presidential nominees’ tweets

Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis

BACKGROUND

The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels.

OBJECTIVE

Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti-HIV vaccine conspiracy theories through manual coding.

METHODS

We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti-HIV vaccine conspiracy theories.

RESULTS

Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19-related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti-HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events.

CONCLUSIONS

The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti-HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

Social media engagement in the maritime industry during the pandemic

The maritime sectors are suffering severe disruptions as a result of COVID-19. Maritime companies have turned to social media to interact with partners and clear up confusion. However, not every social media post receives the same engagement levels especially during a pandemic. For a more holistic analysis of social media engagement during a pandemic, the Protection Motivation Theory and classical theories such as user gratification and media richness theories were included. The research uses hierarchical regression analysis on information gathered from Facebook posts about COVID-19 made by eighteen different companies from four distinct maritime sectors. It was found that the rate of stakeholder engagement is highly influenced by informational, relational, and entertaining content as well as content that fosters self-efficacy, severity, and vulnerability. Additionally, the stakeholder engagement rate is greatly influenced by posts with greater vividness and fluency, which are characterized by providing external links. This is the first study to look into why stakeholders interact with posts linked to COVID-19 on social media. The findings will help maritime organisations use social media as a management and communication instrument.

COVID-19, climate change, and the finite pool of worry in 2019 to 2021 Twitter discussions

Climate change mitigation has been one of the world's most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past 2 y. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 to 2021. Consistent with the "finite pool of worry" hypothesis both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of "climate change" tweets. Climate change discussion on Twitter decreased, despite 1) a larger Twitter daily active usage in 2020 and 2021, 2) greater coverage of climate change in the traditional media in 2021, 3) a larger number of North Atlantic Ocean hurricanes, and 4) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of climate change tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.

Modeling the popularity of twitter hashtags with master equations

In this work we introduce a simple mathematical model, based on master equations, to describe the time evolution of the popularity of hashtags on the Twitter social network. Specifically, we model the total number of times a certain hashtag appears on user’s timelines as a function of time. Our model considers two kinds of components: those that are internal to the network (degree distribution) as well as external factors, such as the external popularity of the hashtag. From the master equation, we are able to obtain explicit solutions for the mean and variance and construct confidence regions. We propose a gamma kernel function to model the hashtag popularity, which is quite simple and yields reasonable results. We validate the plausibility of the model by contrasting it with actual Twitter data obtained through the public API. Our findings confirm that relatively simple semi-deterministic models are able to capture the essentials of this very complex phenomenon for a wide variety of cases. The model we present distinguishes from other existing models in its focus on the time evolution of the total number of times a particular hashtag has been seen by Twitter users and the consideration of both internal and external components.

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

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.

Messengers and Messages for Tweets That Used #thinspo and #fitspo Hashtags in 2016

INTRODUCTION

Twitter is widely used by young adults and is popular for seeking and sharing health information. The hashtags #thinspo and #fitspo provide a way to identify tweets designed to inspire thinness (thinspiration, thinspo) or fitness (fitspiration, fitspo). However, despite having different purposes, both terms may be associated with content that promotes eating disorders. We sought to 1) examine and compare the characteristics of senders and the content of tweets using these hashtags and 2) identify characteristics associated with engagement with a #thinspo or #fitspo tweet.

METHODS

In May 2016 we collected 1,035 tweets with #thinspo and #fitspo hashtags by using a constructed week sampling procedure. Using consensus coding, pairs of raters assessed each tweet's topic and associated images and videos. We used descriptive statistics to examine topics and user characteristics and inferential models to determine topics and characteristics associated with retweets, likes, and replies to tweets.

RESULTS

Of the 1,035 tweets, 696 (67.2%) were relevant to body image, fitness, food, dieting, or eating disorders. Fitspo tweets came from organizations or businesses, were promotional, and focused on nutrition and exercise, whereas #thinspo tweets came from individuals, focused on thinness and disordered eating behaviors, and contained images of extremely thin women. Rates of retweeting and liking were significantly higher for #thinspo than for #fitspo.

CONCLUSION

Characteristics of messages and messengers differed between #thinspo and #fitspo tweets; #thinspo tweets were used for messages about disordered eating. Public health professionals should consider using the #thinspo hashtag to reach the #thinspo group.