Identifying Vaccine Hesitant Communities on Twitter and their Geolocations: A Network Approach

Vaccine misinformation online may contribute to the increase of anti-vaccine sentiment and vaccine-hesitant behaviors. Social network data was used to identify Twitter vaccine influencers, their online twitter communities, and their geolocations to determine pro-vaccine and vaccine-hesitant online communities. We explored 139,433 tweets and identified 420 vaccine Twitter influencers—opinion leaders and assessed 13,487 of their tweets and 7,731 of their connections. Semantic network analysis was employed to determine twitter conversation themes. Results suggest that locating social media influencers is an efficient way to identify and target vaccine-hesitant communities online. We discuss the implications of using this process for public health education and disease management

What Drives Sentiments on Social Media? An Exploratory Study on the 2021 Canadian Federal Election

Social media is used for online political discourse. Voter opinions have different sentiments associated with them. Understanding the factors behind these sentiments can help policymakers to take actions that align with voter needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments while also investigating the relationship between these keywords and how fast the related message (the tweet) spreads. Sentiment Analysis (SA) of 779,169 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. The results suggest some keywords common in opposite sentiment types (positive and negative), which shows polarization in Twitter while some keywords unique to a sentiment type suggest concepts to invest in or mitigate. Chi-Square tests suggest a significant relationship between keywords and the number of retweets for extremely negative tweets

The Unwanted Dissemination of Science: The Usage of Academic Articles as Ammunition in Contested Discursive Arenas on Twitter

Twitter is a common site of offensive language. Prior literature has shown that the emotional content of tweets can heavily impact their diffusion when discussing political topics. We extend prior work to look at offensive tweets that link to academic articles. Using a mixed methods approach, we identify three findings: firstly, offensive language is common in tweets that refer to academic articles, and vary widely by subject matter. Secondly, discourse analysis reveals that offensive tweets commonly use academic articles to promote or attack political ideologies. Lastly, we show that offensive tweets reach a smaller audience than their non-offensive counterparts. Our analysis of these offensive tweets reveal how academic articles are being shared on Twitter not for the sake of disseminating new knowledge, but rather to as argumentative tools in controversial and combative discourses.Comment: 16 pages, 8 tables, submitted to CSCW '2

Covid-19-related misinformation on social media: a systematic review

Source at https://www.who.int/publications/journals/bulletin/. Objective - To review misinformation related to coronavirus disease 2019 (COVID-19) on social media during the first phase of the pandemic and to discuss ways of countering misinformation. Methods - We searched PubMed®, Scopus, Embase®, PsycInfo and Google Scholar databases on 5 May 2020 and 1 June 2020 for publications related to COVID-19 and social media which dealt with misinformation and which were primary empirical studies. We followed the preferred reporting items for systematic reviews and meta-analyses and the guidelines for using a measurement tool to assess systematic reviews. Evidence quality and the risk of bias of included studies were classified using the grading of recommendations assessment, development and evaluation approach. The review is registered in the international prospective register of systematic reviews (PROSPERO; CRD42020182154). Findings - We identified 22 studies for inclusion in the qualitative synthesis. The proportion of COVID-19 misinformation on social media ranged from 0.2% (413/212 846) to 28.8% (194/673) of posts. Of the 22 studies, 11 did not categorize the type of COVID-19-related misinformation, nine described specific misinformation myths and two reported sarcasm or humour related to COVID-19. Only four studies addressed the possible consequences of COVID-19-related misinformation: all reported that it led to fear or panic. Conclusion Social media play an increasingly important role in spreading both accurate information and misinformation. The findings of this review may help health-care organizations prepare their responses to subsequent phases in the COVID–19 infodemic and to future infodemics in general

On the relation between message sentiment and its virality on social media

We investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message diffusion on social media have been studied previously, we focus on message sentiment and reveal how the polarity of message sentiment affects its virality. The virality of a message is characterized by the number of message repostings (retweets) and the time elapsed from the original posting of a message to its Nth reposting (N-retweet time). Through extensive analysis using the 4.1 million tweets and their retweets in 1 week, we discover that negative messages are likely to be reposted more rapidly and frequently than positive and neutral messages. Specifically, the reposting volume of negative messages is 20–60% higher than that of positive and neutral messages, and negative messages spread 25% faster than positive and neutral messages when the diffusion volume is quite high. We also perform longitudinal analysis of message diffusion observed over 1 year and find that recurrent diffusion of negative messages is less frequent than that of positive and neutral messages. Moreover, we present a simple message diffusion model that can reproduce the characteristics of message diffusion observed in this paper

Are generics and negativity about social groups common on social media? A comparative analysis of Twitter (X) data

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter

Lost in Translation? Exploring the journey from press releases to news articles during volcanic crises, and its impact on perceptions

The world in which we communicate has changed rapidly in recent years; information from official bodies can be posted, shared, translated, re-interpreted and disseminated rapidly via online news outlets and social media. The increased use of the internet means that science communication can be spread more than ever, from online news to press releases being shared on social media. The modern drive of the press industry can cause news about events to be sensationalised to create interesting stories, therefore potentially impacting the public perceptions of a volcanic crisis. This study aims to better understand how the ‘translation’ of press releases by the mainstream media impacts the behaviours and perceptions of the global community during a volcanic crisis. Here we show that translation from press releases to news articles is not always linear and different media companies translate press releases differently. This therefore impacts public perceptions of volcanic events as the news articles do not always portray the events accurately and the public’s perceptions can be impacted by many extraneous factors. It is thought that this research will be the starting point into more research into press release translation and that the results of this study can show the true impact of sensationalist language within the press industry

The Effect of Affect: A Close Reading of LeBron James’ Athlete Activism

The purpose of this thesis is to argue that LeBron James’ expressions of activism are, in many ways, perceived as insincere, hypocritical or disingenuous to his followers on the social media platform Twitter. Using the theoretical framework of Affect Theory and Plutchik’s wheel of emotions, a close reading was conducted in order to find an interpretive answer to the argument presented. The results of this research found that, through the affect category of contempt and the notion of whataboutism, the perception of hypocrisy is confirmed by followers through the responses of LeBron James’ online activism campaigns. The rhetorical implications of these findings suggest there may be a bandwagon effect regarding how LeBron James’ expressions of activism are received by followers on social media platforms such as Twitter.Keywords: LeBron James, Athlete Activism, Affect Theory, Twitter Responses, Whataboutism, Bandwagon Effec