Selective and deceptive citation in the construction of dueling consensuses.

The COVID-19 pandemic provides a unique opportunity to study science communication and, in particular, the transmission of consensus. In this study, we show how "science communicators," writ large to include both mainstream science journalists and practiced conspiracy theorists, transform scientific evidence into two dueling consensuses using the effectiveness of masks as a case study. We do this by compiling one of the largest, hand-coded citation datasets of cross-medium science communication, derived from 5 million Twitter posts of people discussing masks. We find that science communicators selectively uplift certain published works while denigrating others to create bodies of evidence that support and oppose masks, respectively. Anti-mask communicators in particular often use selective and deceptive quotation of scientific work and criticize opposing science more than pro-mask communicators. Our findings have implications for scientists, science communicators, and scientific publishers, whose systems of sharing (and correcting) knowledge are highly vulnerable to what we term adversarial science communication.

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election.

The 2020 United States (US) presidential election was - and has continued to be - the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.

Temporal trends in health worker social media communication during the COVID-19 pandemic.

During the COVID-19 pandemic, healthcare professionals are exposed to extreme hazards and workplace stressors. Social media postings by physicians and nurses related to COVID-19 from January 21 to June 1, 2020 were obtained from the Reddit website. Topic modeling via Latent Dirichlet Allocation (LDA) using a machine-learning approach was performed on 1723 documents, each posted in a unique Reddit discussion. We selected the optimal number of topics using a heuristic approach based on examination of the rate of perplexity change (RPC) across LDA models. A two-step multiple linear regression was done to identify differences across time and between nurses versus physicians. Prevalent topics included excessive workload, positive emotional expression and collegial support, anger and frustration, testing positive for COVID-19 and treatment, use of personal protective equipment, impacts on healthcare jobs, disruption of medical procedures, and general healthcare issues. Nurses' posts initially reflected concern about workload, personal danger, safety precautions, and emotional support to their colleagues. Physicians posted initially more often than nurses about technical aspects of the coronavirus disease, medical equipment, and treatment. Differences narrowed over time: nurses increasingly made technical posts, while physicians' posts increasingly were in the personal domain, suggesting a convergence of the professions over time.

Combining interventions to reduce the spread of viral misinformation.

Misinformation online poses a range of threats, from subverting democratic processes to undermining public health measures. Proposed solutions range from encouraging more selective sharing by individuals to removing false content and accounts that create or promote it. Here we provide a framework to evaluate interventions aimed at reducing viral misinformation online both in isolation and when used in combination. We begin by deriving a generative model of viral misinformation spread, inspired by research on infectious disease. By applying this model to a large corpus (10.5 million tweets) of misinformation events that occurred during the 2020 US election, we reveal that commonly proposed interventions are unlikely to be effective in isolation. However, our framework demonstrates that a combined approach can achieve a substantial reduction in the prevalence of misinformation. Our results highlight a practical path forward as misinformation online continues to threaten vaccination efforts, equity and democratic processes around the globe.

How do you solve a problem like misinformation?

Understanding key distinctions between misinformation/disinformation, speech/action, and mistaken belief/conviction provides an opportunity to expand research and policy toward more constructive online communication.

#drunktwitter: Examining the relations between alcohol-related Twitter content and alcohol willingness and use among underage young adults.

PURPOSE: Despite the importance of social networking sites on young adult alcohol use, few studies have examined Twitter as a conduit for sharing drinking behavior. However, this work generally uses random samples of tweets and thus cannot determine the extent to which Tweets correspond with self-reported drinking cognitions or behaviors. The primary aims of the present study were to (1) document basic patterns of alcohol-related Twitter activity in a subsample of young adult drinkers, and (2) examine whether willingness to drink, alcohol use, and negative consequences are associated with alcohol-related tweeting behavior. METHODS: 186 young adults age 18-20 completed an online survey and provided Twitter handle information. From these participants, a random sample of 5000 Tweets was coded by a trained team to determine whether tweets were related to alcohol use or not. Ordinary least squares regression analyses were conducted to determine whether the proportion of alcohol-related Tweets is associated with self-reported alcohol use willingness, behaviors, and negative consequences. RESULTS: Results indicated that not only are alcohol-related tweets common among young adults, but that the proportion of one's overall tweets that are related to alcohol is significantly associated with willingness to drink, alcohol use, and negative consequences. CONCLUSIONS: The results of this study are an important step to understanding how digital behavior (e.g., posting about alcohol on Twitter) is related to an individual's self-reported drinking cognitions, alcohol use, and negative consequences and has implications for the way Twitter data can be used for public health surveillance and interventions.

Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing.

Despite recent and growing interest in using Twitter to examine human behavior and attitudes, there is still significant room for growth regarding the ability to leverage Twitter data for social science research. In particular, gleaning demographic information about Twitter users-a key component of much social science research-remains a challenge. This article develops an accurate and reliable data processing approach for social science researchers interested in using Twitter data to examine behaviors and attitudes, as well as the demographic characteristics of the populations expressing or engaging in them. Using information gathered from Twitter users who state an intention to not vote in the 2012 presidential election, we describe and evaluate a method for processing data to retrieve demographic information reported by users that is not encoded as text (e.g., details of images) and evaluate the reliability of these techniques. We end by assessing the challenges of this data collection strategy and discussing how large-scale social media data may benefit demographic researchers.

Shifting Stakes: Understanding the Dynamic Roles of Individuals and Organizations in Social Media Protests.

In this paper we examine two protests characterized by substantial social media presence and distributed participation frameworks via two core questions: what roles did organizations and individuals play, and how did participants' social interactions change over the course of the protests? To answer these questions, we analyzed a large Twitter activity dataset for the #YoSoy132 student uprising in Mexico and Brazil's "bus rebellion." Results indicate that individuals initially took prominence at the protests but faded in importance as the movements dwindled and organizations took over. Regarding the dynamics and structure of the interactions, we found that key time points with unique social structures often map to exogenous events such as coordinated protests in physical locations. Our results have important consequences for the visibility of such social movements and their ability to attract continued participation by individuals and organizations.

Spatio-temporal filtering techniques for the detection of disaster-related communication.

Individuals predominantly exchange information with one another through informal, interpersonal channels. During disasters and other disrupted settings, information spread through informal channels regularly outpaces official information provided by public officials and the press. Social scientists have long examined this kind of informal communication in the rumoring literature, but studying rumoring in disrupted settings has posed numerous methodological challenges. Measuring features of informal communication-timing, content, location-with any degree of precision has historically been extremely challenging in small studies and infeasible at large scales. We address this challenge by using online, informal communication from a popular microblogging website and for which we have precise spatial and temporal metadata. While the online environment provides a new means for observing rumoring, the abundance of data poses challenges for parsing hazard-related rumoring from countless other topics in numerous streams of communication. Rumoring about disaster events is typically temporally and spatially constrained to places where that event is salient. Accordingly, we use spatio and temporal subsampling to increase the resolution of our detection techniques. By filtering out data from known sources of error (per rumor theories), we greatly enhance the signal of disaster-related rumoring activity. We use these spatio-temporal filtering techniques to detect rumoring during a variety of disaster events, from high-casualty events in major population centers to minimally destructive events in remote areas. We consistently find three phases of response: anticipatory excitation where warnings and alerts are issued ahead of an event, primary excitation in and around the impacted area, and secondary excitation which frequently brings a convergence of attention from distant locales onto locations impacted by the event. Our results demonstrate the promise of spatio-temporal filtering techniques for "tuning" measurement of hazard-related rumoring to enable observation of rumoring at scales that have long been infeasible.

Thumbs up for privacy?: Differences in online self-disclosure behavior across national cultures.

This study investigates relationships between national-level culture and online self-disclosure behavior. We operationalize culture through the GLOBE dimensions, a set of nine variables measuring cultural practices and another nine measuring values. Our observations of self-disclosure come from the privacy settings of approximately 200,000 randomly sampled Facebook users who designated a geographical network in 2009. We model privacy awareness as a function of one or more GLOBE variables with demographic covariates, evaluating the relative influence of each factor. In the top-performing models, we find that the majority of the cultural dimensions are significantly related to privacy awareness behavior. We also find that the hypothesized directions of several of these relationships, based largely on cultural attitudes towards threat mitigation, are confirmed.

A cross-hazard analysis of terse message retransmission on Twitter.

For decades, public warning messages have been relayed via broadcast information channels, including radio and television; more recently, risk communication channels have expanded to include social media sites, where messages can be easily amplified by user retransmission. This research examines the factors that predict the extent of retransmission for official hazard communications disseminated via Twitter. Using data from events involving five different hazards, we identity three types of attributes--local network properties, message content, and message style--that jointly amplify and/or attenuate the retransmission of official communications under imminent threat. We find that the use of an agreed-upon hashtag and the number of users following an official account positively influence message retransmission, as does message content describing hazard impacts or emphasizing cohesion among users. By contrast, messages directed at individuals, expressing gratitude, or including a URL were less widely disseminated than similar messages without these features. Our findings suggest that some measures commonly taken to convey additional information to the public (e.g., URL inclusion) may come at a cost in terms of message amplification; on the other hand, some types of content not traditionally emphasized in guidance on hazard communication may enhance retransmission rates.

What it Takes to Get Passed On: Message Content, Style, and Structure as Predictors of Retransmission in the Boston Marathon Bombing Response.

Message retransmission is a central aspect of information diffusion. In a disaster context, the passing on of official warning messages by members of the public also serves as a behavioral indicator of message salience, suggesting that particular messages are (or are not) perceived by the public to be both noteworthy and valuable enough to share with others. This study provides the first examination of terse message retransmission of official warning messages in response to a domestic terrorist attack, the Boston Marathon Bombing in 2013. Using messages posted from public officials' Twitter accounts that were active during the period of the Boston Marathon bombing and manhunt, we examine the features of messages that are associated with their retransmission. We focus on message content, style, and structure, as well as the networked relationships of message senders to answer the question: what characteristics of a terse message sent under conditions of imminent threat predict its retransmission among members of the public? We employ a negative binomial model to examine how message characteristics affect message retransmission. We find that, rather than any single effect dominating the process, retransmission of official Tweets during the Boston bombing response was jointly influenced by various message content, style, and sender characteristics. These findings suggest the need for more work that investigates impact of multiple factors on the allocation of attention and on message retransmission during hazard events.