Political Partisanship and Antiscience Attitudes in Online Discussions About COVID-19: Twitter Content Analysis

Scenario-Based Messages on Social Media Motivate COVID-19 Information Seeking

Communicating information about health risks empowers individuals to make informed decisions. To identify effective communication strategies, we manipulated the specificity, self-relevance, and emotional framing of messages designed to motivate information seeking about COVID-19 exposure risk. In Study 1 (N=221,829), we conducted a large-scale social media field study. Using Facebook advertisements, we targeted users by age and political attitudes. Episodic specificity drove engagement: Advertisements that contextualized risk in specific scenarios produced the highest click-through rates, across all demographic groups. In Study 2, we replicated and extended our findings in an online experiment (N=4,233). Message specificity (but not self-relevance or emotional valence) drove interest in learning about COVID-19 risks. Across both studies, we found that older adults and liberals were more interested in learning about COVID-19 risks. However, message specificity increased engagement across demographic groups. Overall, evoking specific scenarios motivated information seeking about COVID-19, facilitating risk communication to a broad audience.

Modeling opinion misperception and the emergence of silence in online social system

In the last decades an increasing deal of research has investigated the phenomenon of opinion misperception in human communities and, more recently, in social media. Opinion misperception is the wrong evaluation by community's members of the real distribution of opinions or beliefs about a given topic. In this work we explore the mechanisms giving rise to opinion misperception in social media groups, which are larger than physical ones and have peculiar topological features. By means of numerical simulations, we suggest that the structure of connections of such communities plays indeed a role in distorting the perception of the agents about others' beliefs, but it is essentially an indirect effect. Moreover, we show that the main ingredient that generates misperception is a spiral of silence induced by few, well connected and charismatic agents, which rapidly drives the majority of individuals to stay silent without disclosing their true belief, leading minoritarian opinions to appear more widespread throughout the community.

How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events

Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviors that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days of their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, making unwarranted and excessive use of reply and mention features, and sharing large amounts of spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings contribute to shedding light on patterns of platform abuse and subsequent moderation during major events.

Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders

BACKGROUND

Stay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.

OBJECTIVE

This study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.

METHODS

We constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.

RESULTS

We combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.

CONCLUSIONS

This study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

Entity graphs for exploring online discourse

A vast amount of human communication occurs online. These digital traces of natural human communication along with recent advances in natural language processing technology provide for computational analysis of these discussions. In the study of social networks, the typical perspective is to view users as nodes and concepts as flowing through and among the user nodes within the social network. In the present work, we take the opposite perspective: we extract and organize massive amounts of group discussion into a concept space we call an entity graph where concepts and entities are static and human communicators move about the concept space via their conversations. Framed by this perspective, we performed several experiments and comparative analysis on large volumes of online discourse from Reddit. In quantitative experiments, we found that discourse was difficult to predict, especially as the conversation carried on. We also developed an interactive tool to visually inspect conversation trails over the entity graph; although they were difficult to predict, we found that conversations, in general, tended to diverge to a vast swath of topics initially, but then tended to converge to simple and popular concepts as the conversation progressed. An application of the spreading activation function from the field of cognitive psychology also provided compelling visual narratives from the data.

Trustworthiness Over Time on Twitter: Three Critical Periods for the Norwegian Health Authorities and Political Leadership During the COVID-19 Pandemic

Public health authorities and political leaders need to come across as trustworthy in their handling of a crisis like the COVID-19 pandemic. There is, however, little knowledge about how the affordances and dynamics of social media influence perceptions of trustworthiness, especially during a protracted crisis. In this article, we study how Twitter users were discussing the trustworthiness of the Norwegian health authorities and political leadership throughout three periods of partial lockdown during the COVID-19 pandemic. Across all the periods, there was a substantial number of positive comments, but these were outweighed by negative ones. Ability was clearly the most discussed factor for trustworthiness, and many users offered up their lay expertise. Discussions of integrity and benevolence were less frequent and mostly negative when they occurred. An increase in negative comments during the last period might be read as an expression of fatigue, and there was a noted dissatisfaction with the ability of the political leadership. Taken together, the study suggests Twitter to be an arena where users are exposed to arguments and counterarguments in negotiations over ability in particular. Such discussions can intensify as a crisis drags on and are important to grasp for health authorities and political leadership alike. Thus, the study sheds light on the contribution that a socio-technical platform like Twitter makes to the discursive formation of trustworthiness over time, which in turn might function to strengthen or erode public trust in public authorities and political leadership.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

Anti-intellectualism amid the COVID-19 pandemic: The discursive elements and sources of anti-Fauci tweets

Anti-intellectualism (resentment, hostility, and mistrust of experts) has become a growing concern during the pandemic. Using topic modeling and supervised machine learning, this study examines the elements and sources of anti-Fauci tweets as a case of anti-intellectual discourse on social media. Based on the theoretical framework of science-related populism, we identified three anti-intellectual discursive elements in anti-Fauci tweets: people-scientist antagonism, delegitimizing the motivation of scientists, and delegitimizing the knowledge of scientists. Delegitimizing the motivation of scientists appeared the most in anti-Fauci tweets. Politicians, conservative news media, and non-institutional actors (e.g. individuals and grassroots advocacy organizations) co-constructed the production and circulation of anti-intellectual discourses on Twitter. Anti-intellectual discourses resurged even under Twitter's content moderation mechanism. We discuss theoretical and practical implications for building public trust in scientists, effective science communication, and content moderation policies on social media.

Using geospatial social media data for infectious disease studies: a systematic review

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

A systematic review of worldwide causal and correlational evidence on digital media and democracy

One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

Who polarizes Twitter? Ideological polarization, partisan groups and strategic networked campaigning on Twitter during the 2017 and 2021 German Federal elections 'Bundestagswahlen'

Political campaign activities are increasingly digital. A crucial part of digital campaigning is communication efforts on social media platforms. As a forum for political discourse and political communication, parties and candidates on Twitter share public messages and aim to attract media attention and persuade voters. Party or prominent candidate hashtags are a central element of the campaign communication strategy since journalists and citizens search for these hashtags to follow the current debate concerning the hashed party or political candidate. Political elites and partisans use social media strategically, e.g., to link their messages to a broader debate, increase the visibility of messages, criticize other parties, or take over their hashtags (hashjacking). This study investigates the cases of the most recent 2017 and 2021 German federal elections called 'Bundestagswahlen'. The investigation (1) identifies communities of partisans in retweet networks in order to analyze the polarization of the most prominent hashtags of parties, 2) assesses the political behavior by partisan groups that amplify messages by political elites in these party networks, and 3) examines the polarization and strategic behavior of the identified partisan groups in the broader election hashtag debates using #BTW17 and #BTW21 as the prominent hashtags of the 2017 and 2021 elections. While in 2017, the far-right party 'Alternative für Deutschland' (AfD) and its partisans are in an isolated community, in 2021, they are part of the same community as the official party accounts of established conservative and liberal parties. This broader polarization may indicate changes in the political ideology of these actors. While the overall activity of political elites and partisans increased between 2017 and 2021, AfD politicians and partisans are more likely to use other party hashtags, which resulted in the polarization of the observed parts of the German political twitter sphere. While in 2017, the AfD polarized German Twitter, 2021 shows a broader division along the classical left–right divide.

A Social Cognitive Theory Approach to Understanding Parental Attitudes and Intentions to Vaccinate Children during the COVID-19 Pandemic

The distribution of the COVID-19 vaccine represents a path towards global health after a worldwide pandemic. Yet, the U.S. response to the vaccination rollout has been politically polarized. The aim of this paper is to contribute to the understanding of the contextual factors that influence parents' attitudes towards health officials and their intention to vaccinate children, focusing on communication behaviors, personal factors, and geographic locations. We use Bandura's triadic reciprocal determinism (TRD) model which posits reciprocal influence between personal factors, environmental factors, and behaviors. We found that personal factors (having younger children and identifying as Republican partisans), and the behavioral factor of conservative news use were significantly related to more negative attitudes towards health officials and lower vaccination intentions. Conversely, Democrats and liberal news use were significantly related to warmer attitudes and greater vaccination intentions. The environmental factor of geographic location across four states with different partisan dynamics was not significantly related to attitudes and behavioral intentions. Results from a post-hoc analysis show that news media use and partisanship were the strongest correlates of parents' attitudes towards health officials. This evidence points to the politicization of the COVID-19 vaccine being a key consideration regarding vaccine uptake.

Partisan asymmetries in exposure to misinformation

Online misinformation is believed to have contributed to vaccine hesitancy during the Covid-19 pandemic, highlighting concerns about social media’s destabilizing role in public life. Previous research identified a link between political conservatism and sharing misinformation; however, it is not clear how partisanship affects how much misinformation people see online. As a result, we do not know whether partisanship drives exposure to misinformation or people selectively share misinformation despite being exposed to factual content. To address this question, we study Twitter discussions about the Covid-19 pandemic, classifying users along the political and factual spectrum based on the information sources they share. In addition, we quantify exposure through retweet interactions. We uncover partisan asymmetries in the exposure to misinformation: conservatives are more likely to see and share misinformation, and while users’ connections expose them to ideologically congruent content, the interactions between political and factual dimensions create conditions for the highly polarized users—hardline conservatives and liberals—to amplify misinformation. Overall, however, misinformation receives less attention than factual content and political moderates, the bulk of users in our sample, help filter out misinformation. Identifying the extent of polarization and how political ideology exacerbates misinformation can help public health experts and policy makers improve their messaging.

Perceived risk, political polarization, and the willingness to follow COVID-19 mitigation guidelines

OBJECTIVE

Risk assessment and response is important for understanding human behavior. The divisive context surrounding the coronavirus pandemic inspires our exploration of risk perceptions and the polarization of mitigation practices (i.e., the degree to which the behaviors of people on the political "Left" diverge from those on the "Right"). Specifically, we investigate the extent to which the political polarization of willingness to comply with mitigation behaviors changes with risk perceptions.

METHOD

Analyses use data from two sources: an original dataset of Twitter posts and a nationally-representative survey. In the Twitter data, negative binomial regression models are used to predict mitigation intent measured using tweet counts. In the survey data, logit models predict self-reported mitigation behavior (vaccination, masking, and social distancing).

RESULTS

Findings converged across both datasets, supporting the idea that the links between political orientation and willingness to follow mitigation guidelines depend on perceived risk. People on the Left are more inclined than their Right-oriented colleagues to follow guidelines, but this polarization tends to decrease as the perceived risk of COVID-19 intensifies. Additionally, we find evidence that exposure to COVID-19 infections sends ambiguous signals about the risk of the virus while COVID-19 related deaths have a more consistent impact on mitigation behaviors.

CONCLUSIONS

Pandemic-related risks can create opportunities for perceived "common ground," between the political "Right" and "Left." Risk perceptions and politics interact in their links to intended COVID-19 mitigation behavior (as measured both on Twitter and in a national survey). Our results invite a more complex interpretation of political polarization than those stemming from simplistic analyses of partisanship and ideology.

Scaling up the discovery of hesitancy profiles by identifying the framing of beliefs towards vaccine confidence in Twitter discourse

Our study focused on the discovery of how vaccine hesitancy is framed in Twitter discourse, allowing us to recognize at-scale all tweets that evoke any of the hesitancy framings as well as the stance of the tweet authors towards the frame. By categorizing the hesitancy framings that propagate misinformation, address issues of trust in vaccines, or highlight moral issues or civil rights, we were able to empirically recognize their ontological commitments. Ontological commitments of vaccine hesitancy framings couples with the stance of tweet authors allowed us to identify hesitancy profiles for two most controversial yet effective and underutilized vaccines for which there remains substantial reluctance among the public: the Human Papillomavirus and the COVID-19 vaccines. The discovered hesitancy profiles inform public health messaging approaches to effectively reach Twitter users with promise to shift or bolster vaccine attitudes.

Political attitudes and efficacy of health expert communication on the support for COVID-19 vaccination program: Findings from a survey in Hong Kong

Despite evidence suggesting that vaccines offer protection against COVID-19, the uptake rates of COVID-19 vaccines have been low in some high-income regions. Support for vaccination program is important to fight the pandemic. This study aimed at exploring two research questions: first, to what extent political attitudes are associated with support for COVID-19 vaccination program; and second, whether health expert communication is effective in increasing the support. An online survey was undertaken by 1079 Hong Kong residents aged 18–77 years from May 26 to June 3, 2021. The survey found higher support in pro-government respondents, and lower support in political opposition. A strategy of positive communication by health experts could increase support in the opposition and politically attentive respondents. Other variables that were positively related to program support were quarantine experience, trust in government, preference for pandemic control over freedom, political attentiveness, and disagreement with China’s influence on Hong Kong’s COVID-19 policymaking. This study contributes to understanding the relationship between political attitudes and support for vaccination program and provides empirical evidence of the efficacy of health expert communication strategy in improving support for vaccination program for people with certain political attitudes.

A darkening spring: How preexisting distrust shaped COVID-19 skepticism

Despite widespread communication of the health risks associated with the COVID-19 virus, many Americans underestimated its risks and were antagonistic regarding preventative measures. Political partisanship has been linked to diverging attitudes towards the virus, but the cognitive processes underlying this divergence remain unclear. Bayesian models fit to data gathered through two preregistered online surveys, administered before (March 13, 2020, N = 850) and during the first wave (April-May, 2020, N = 1610) of cases in the United States, reveal two preexisting forms of distrust--distrust in Democratic politicians and in medical scientists--that drove initial skepticism about the virus. During the first wave of cases, additional factors came into play, suggesting that skeptical attitudes became more deeply embedded within a complex network of auxiliary beliefs. These findings highlight how mechanisms that enhance cognitive coherence can drive anti-science attitudes.

Advancing Infodemiology in a Digital Intensive Era

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Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic with Deep Learning

Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, UK, Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT nad BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%.

Disinformed social movements: A large-scale mapping of conspiracy narratives as online harms during the COVID-19 pandemic

The COVID-19 pandemic caused high uncertainty regarding appropriate treatments and public policy reactions. This uncertainty provided a perfect breeding ground for spreading conspiratorial anti-science narratives based on disinformation. Disinformation on public health may alter the population’s hesitance to vaccinations, counted among the ten most severe threats to global public health by the United Nations. We understand conspiracy narratives as a combination of disinformation, misinformation, and rumour that are especially effective in drawing people to believe in post-factual claims and form disinformed social movements. Conspiracy narratives provide a pseudo-epistemic background for disinformed social movements that allow for self-identification and cognitive certainty in a rapidly changing information environment. This study monitors two established conspiracy narratives and their communities on Twitter, the anti-vaccination and anti-5G communities, before and during the first UK lockdown. The study finds that, despite content moderation efforts by Twitter, conspiracy groups were able to proliferate their networks and influence broader public discourses on Twitter, such as #Lockdown in the United Kingdom.

Understanding and navigating the repercussions of the politically polarized climate in mental health

The world is experiencing a moment of political polarization between liberal and conservative ideas, which has aggravated since the arrival of the Covid-19. Many countries (Brazil included) have been experiencing the generalized occurrence of people fighting over politics, in contexts including family, workplace, friendships, and romantic relationships. Over the past 2 years, it has been possible to observe an unexpected and overwhelming effect of the political climate on psychotherapy patients, some of whom have started to actively look for therapists who share their convictions. Brazil is experiencing a moment of severe sanitary, economic, social, and political crisis, which is directly affecting our patients. Nevertheless, the impact of the political climate on our population has not been systematically investigated. However, as the political environment is an inherent part of the social component of the psychosocial model, it is important that mental health professionals be prepared to have this conversation with their patients. This highlights the need to address these difficulties in supervision, rounds, and clinical discussions.

Socioeconomic Correlates of Anti-Science Attitudes in the US

Successful responses to societal challenges require sustained behavioral change. However, as responses to the COVID-19 pandemic in the US showed, political partisanship and mistrust of science can reduce public willingness to adopt recommended behaviors such as wearing a mask or receiving a vaccination. To better understand this phenomenon, we explored attitudes toward science using social media posts (tweets) that were linked to counties in the US through their locations. The data allowed us to study how attitudes towards science relate to the socioeconomic characteristics of communities in places from which people tweet. Our analysis revealed three types of communities with distinct behaviors: those in large metro centers, smaller urban places, and rural areas. While partisanship and race are strongly associated with the share of anti-science users across all communities, income was negatively and positively associated with anti-science attitudes in suburban and rural areas, respectively. We observed that emotions in tweets, specifically negative high arousal emotions, are expressed among suburban and rural communities by many anti-science users, but not in communities in large urban places. These trends were not apparent when pooled across all counties. In addition, we found that anti-science attitudes expressed five years earlier were significantly associated with lower COVID-19 vaccination rates. Our analysis demonstrates the feasibility of using spatially resolved social media data to monitor public attitudes on issues of social importance.