Declining trust in public health organizations has (not coincidentally) coincided with a rise in health misinformation on social media. During the COVID-19 pandemic, the World Health Organization (WHO) strategically employed corrective communications to counter common misinformation as an important aspect of its risk communication and community engagement (RCCE) efforts. Whereas past research focuses mainly on the ability of corrections to reduce misperceptions, we turn attention to the broader question of whether and how they affect perceptions of the WHO. We analyze an online survey experiment (N = 1343) with a 2 × 2 design: correction approach (preemptive vs. responsive) × source (WHO vs. user) as compared to a control condition. Results suggest that the corrective infographic significantly improved public approval of the WHO's communication efforts and its credibility, but only when it was shared by a WHO information bot in direct response to a misinformation post. Interestingly, these same benefits did not accrue to the WHO when a social media user shared the WHO's graphics. Moreover, individuals initially critical of the WHO's performance increased their ratings of the WHO's credibility after seeing the corrective infographic from the WHO. The findings inform WHO's future RCCE efforts during a crisis and also highlight the potential of AI-driven bots affiliated with expert organizations to support corrective efforts.

Research demonstrates that correction consistently reduces misperceptions, increasing the importance of understanding how often people see such corrections. To answer this, researchers often rely on self-reported measures through surveys. However, there is little consistency in these measures across studies, hindering comparability and theory development. To investigate whether different measures of self-reported correction experiences affect their estimated frequency, this study uses a preregistered online survey experiment to examine two key factors: the question wording ("corrected" vs. "told they shared misinformation") and the response options (binary vs. four vs. five-point frequency scales). Findings reveal that frequency scales produce consistently higher estimates of three correction experiences (witnessing, performing, and being corrected) compared to binary measures, although classifying those who rarely experience correction alongside those who never do reduces these differences. Moreover, using the term "correction" leads to higher self-reported instances of performing corrections than the "told misinformation" phrasing, but does not impact estimates of witnessing or being corrected. These results offer researchers clear takeaways for comparing measures across multiple datasets and reinforce the importance of moving toward consistent measures in the future to ensure we can better understand how often people experience corrections.

Fertility apps, which help users track their menstrual cycles, are growing in popularity. To better understand how these fertility apps communicate to consumers, we consider rhetorical appeals made within influencer-based marketing of a popular fertility app, Natural Cycles, on Instagram. The results of a quantitative content analysis (499 sponsored posts, 371 influencer profiles, 2019-2024) indicate that most posts (84.9%) used appeals to credibility, emotion, and logic. Further, influencers often connected appeals to nature with fears related to hormonal birth control use. These findings hold important implications for healthcare authorities, providers, and advocates with a stake in public health communications.

As chatbots have become more commonplace writing tools, a need exists to understand the breadth of research about the humanness of machine-generated text via techniques that extend beyond the traditional Turing Test, in both dialogue (e.g., conversing with a chatbot) and non-dialogue (e.g., reading a news article) scenarios. To fill this gap and support future work, we survey current literature that examines and identifies humanness features of written communication generated with the state-of-the-art generative pre-trained transformer language models, provide a working definition of humanness, propose a text-based humanness taxonomy based on linguistic properties, and identify current research gaps.

The mixing of misinformation with high-quality news and information on social media has reinvigorated interest in the value of news literacy (NL) to build audience resiliency to misinformation. Optimizing NL messages for social media environments—where they may be seen alongside misinformation—allows these messages to reach audiences when they are most likely to benefit from them. Using a 2 (NL video vs. control video) x 2 (sunscreen promotion video vs. sunscreen misinformation video) online survey experiment (N = 780), we examine whether exposure to an NL video improves perceived personal NL skills and value for news literacy, as well as enables participants to recognize and avoid engaging with misinformation. Our findings suggest that after watching the NL video, individuals valued NL more but their self-perceived news literacy did not improve. Furthermore, watching the NL video made individuals rate the second video as less credible and reduced engagement with it no matter whether the second video contained misinformation or quality information. This research has several important implications. While watching an NL video could protect individuals by discrediting and decreasing engagement with misinformation, it may do so at the expense of high-quality information. We discuss the difficulty in designing NL messages that lead people to be appropriately skeptical and able to discern between high- and low-quality health information, rather than cynically disengaging with media content altogether.

This study investigates whether source expertise (expert vs. non-expert), use of artificial intelligence (AI; AI vs. non-AI), and the placement (debunking vs. prebunking) of a correction influence its effectiveness in reducing misperceptions and intentions to consume raw milk. Results of a pre-registered two-wave online experiment (N1 = 1,785, N2 = 1,568) suggest that debunking consistently reduces misperceptions and behavioral intentions for at least 1 week, while prebunking was less effective. Expert corrections only outperform non-expert corrections in reducing misperceptions in wave 1. In general, AI cues do not significantly influence the effectiveness of a correction, offering both opportunities and challenges for organizations hoping to automate corrections.

Menstruation in China is plagued by persistent misconceptions and stigmatization. Although social media serve as a crucial information source that shapes public views and challenges misunderstandings or reinforces existing stigma, research has not yet considered how this takes place. This study compares the prevalence of menstrual stigma in menstrual product advertisements on two Chinese social media platforms, Sina Weibo and Little Red Book (Xiaohongshu), through a content analysis of 600 posts. Our findings reveal that menstrual stigma is perpetuated through various themes, textual and visual elements, celebrity endorsements, and user engagement patterns, highlighting its ongoing presence in social media discourse.

As vaccination misinformation proliferates online and hesitancy increases in the United States, more research is needed to study how to best correct such myths. This study addresses two issues: exploring corrections from bot versus human actors and testing complicated social media interactions with two misinformation claims about human papillomavirus (HPV) vaccines. In our preregistered survey experiment (N = 1576) of parents, we found that when considering simple corrections to a single misinformation claim, a bot correction increased belief accuracy compared with a control (absent misinformation), whereas a user correction did not. However, as soon as a second, related false claim about the HPV vaccine was raised, audience beliefs about both false claims, as well as attitudes toward the vaccine, were stable, no matter the content and source of the corrections—from a repetitive bot, a responsive bot, or a social media user. We highlight the potential challenges of correction efforts in online environments.

In 2021, the Texas state legislature passed a bill (SB8) prohibiting abortion after six weeks and facilitating lawsuits against providers and aiders of abortions. In order to understand the nature of media coverage around this legislative event, we content analyzed 500 headlines related to the bill from 48 media outlets. Headlines mostly discussed political aspects of the bill or issues related to abortion access, whereas almost none mentioned race, religion, or health aspects. The headlines were overwhelmingly negative (59%), were negative for nearly all categories, and headlines from left-leaning outlets were more negative than those from right-leaning outlets.

Of the many solutions to address political misinformation spreading on social media, user correction holds special promise for connective democracy given its emphasis on prioritizing user autonomy and fostering communication and connections across lines of disagreement. But for the connective democratic benefits to be realized, these user corrections should ideally come from those who express strong support for democratic norms. Using a nationally representative survey of Americans immediately after the 2020 U.S. presidential election, we find the opposite is true: self-reported correctors also tended to support political violence to achieve their goals. Rather than treating self-reported correction as a clear positive force for democracy, researchers and practitioners should consider the potential drawbacks and limitations of self-reported correction, particularly when coming from those with less supportive attitudes toward connective democracy.

The highly polarized North American experience with COVID-19 often intertwined with sports, meaning that messages were filtered through differing social identities, political partisan and sports fan. This experimental study finds political identities more salient than sports fandom when hearing athlete's views on vaccination; only self-identified political independents saw any attitudinal change athletes' perspectives. An athlete's statements on vaccination had no appreciable effect on their image and fans are significantly more tolerant of others' views on vaccination than non-sports fans. This has implications for the use of athletes or other celebrities in public health campaigns, reaching people with information as the health media space becomes increasingly polarized, and further understanding the pro-social components of sports fandom.

In recent years, short-form social media videos have emerged as an important source of health-related advice. In this study, we investigate whether experts or ordinary users in such videos are more effective in debunking the common misperception that talking about suicide should be avoided. We also explore a new trend on TikTok and other platforms, in which users attempt to back up their arguments by displaying scientific articles in the background of their videos. To test the effect of source type (expert vs. ordinary user) and scientific references (present or absent), we conducted a 2 × 2 between-subject plus control group experiment (n = 956). In each condition, participants were shown a TikTok video that was approximately 30 seconds long. Our findings show that in all four treatment groups, participants reduced their misperceptions on the topic. The expert was rated as being more authoritative on the topic compared to the ordinary user. However, the expert was also rated as being less credible compared to the ordinary user. The inclusion of a scientific reference did not make a difference. Thus, both experts and ordinary users may be similarly persuasive in a short-form video environment.

Anti-vaccine sentiment during the COVID-19 pandemic grew at an alarming rate, leaving much to understand about the relationship between people’s vaccination status and the information they were exposed to. This study investigated the relationship between vaccine behavior, decision rationales, and information exposure on social media over time. Using a cohort study that consisted of a nationally representative survey of American adults, three subpopulations (early adopters, late adopters, and nonadopters) were analyzed through a combination of statistical analysis, network analysis, and semi-supervised topic modeling. The main reasons Americans reported choosing to get vaccinated were safety and health. However, work requirements and travel were more important for late adopters than early adopters (95% CI on OR of [0.121, 0.453]). While late adopters’ and nonadopters’ primary reason for not getting vaccinated was it being too early, late adopters also mentioned safety issues more often and nonadopters mentioned government distrust (95% CI on OR of [0.125, 0.763]). Among those who shared Twitter/X accounts, early adopters and nonadopters followed a larger fraction of highly partisan political accounts compared to late adopters, and late adopters were exposed to more neutral and pro-vaccine messaging than nonadopters. Together, these findings suggest that the decision-making process and the information environments of these subpopulations have notable differences, and any online vaccination campaigns need to consider these differences when attempting to provide accurate vaccine information to all three subpopulations.

Observed corrections of misinformation on social media can encourage more accurate beliefs, but for these benefits to occur, corrections must happen. By exploring people’s perceptions of witnessing and performing corrections on social media, we find that many people say they observe and perform corrections across the United States, the United Kingdom, Canada, and France. We find higher levels of self-reported correction experiences in the United States but few differences between who reports these experiences across countries. Specifically, younger and more educated adults, as well as those who see misinformation more frequently online, are more likely to report observing and performing corrections across contexts.

Citizens often attempt to interact with government through online modes of communication such as email and social media. Using an audit study, we examine when and how American municipalities with populations of over 50,000 respond to online requests for information. We develop baselines for municipal responsiveness, including the average rate, time, and quality of responses, and examine whether these response attributes vary based on the mode of communication or the tone of the request. Overall, municipalities responded to 54% of email requests and 38% of Twitter requests. A majority of responses were received on the same business day. Responses are slightly faster on Twitter, but of higher quality on email. Governments are more likely to respond to frustrated constituents on email, but respond faster to frustrated queries on Twitter, though with lower quality responses. These findings contribute to our understanding of local government responsiveness and have significant implications for democratic accountability and resident compliance with and the effectiveness of local government policies. Furthermore, our scholarly understanding of local government communications with residents, and particularly the promise of social media as a tool of two-way communication, may be underdeveloped.

Gen Zers around the world have grown up and come of age in a period rife with the implications of climate change, heightened right-wing extremism, threats to democracy, and rising inflation. Gen Z has also been characterized by an enhanced awareness of mental health care and body positivity, LGBTQIA+ and the spectrum of gender identity, as well as racial justice, diversity, equity and inclusion. Our previous chapter (Chapter 11) on youth political engagement focused on digital media’s role in shaping engagement modes. In this chapter, we first explore shifts in the basis for civic identity for many young people beginning with the role of socio-economic status and affinity groups. Then we draw attention to skills, online practices, and the definition of political engagement. Lastly, we discuss what these changes imply for the study of political socialization and the practice of civic education.

This paper reviews the existing literature on user correction to consider its value for combating misinformation on social media. We discuss the effectiveness of user correction in reducing misperceptions, and synthesize best practices, highlighting the dual audiences for public correction on social media. We outline how often user correction occurs across contexts, countries, and social media platforms. We pay special attention to the methodological constraints in existing research, emphasizing the need for using diverse and interdisciplinary methods, including longitudinal surveys and experiments, computational methods, realistic simulated environments, and qualitative methods. We call for a more comprehensive understanding of user correction in terms of its long-term and downstream effects on social media platforms.

Political campaigns often feature jarring revelations against candidates. This study examines how audiences come to understand major campaign events, the extent to which they shape evaluations of candidates, and how their impact is filtered through an increasingly partisan news media environment. Using national rolling cross-sectional survey data collected over the 2016 U.S. presidential election period, we show partisan asymmetries in the way major campaign events influenced candidate appraisals. Event effects during the 2016 campaign were dependent on various media use patterns and concentrated among Independents. In particular, the reopening of the investigation into Clinton’s email server by James Comey reduced her favorability, especially when paired with liberal and conservative partisan media use. By providing a nuanced picture of partisan selective exposure and campaign effects, our findings reinforce that the role of campaigns in candidate appraisals should be understood at the intersection of media use, partisanship, and specific events during a contentious race.

In the US and much of the developed world, Gen Z faces significant challenges such as climate change, racial inequality, and economic precarity. Though this emerging generation faces significant challenges, digital media is invaluable for engaging with public life. In this chapter, we will focus on changing civic identities of citizens in industrialized democracies and digital media’s role in shaping engagement modes. First, we touch on news consumption, followed by the practices and patterns emerging as formal political campaigns attempt to reach young people through digital media. Second, we will highlight how content creation and interaction in digital media enables, for some young citizens, a form of cultural engagement that pushes the boundaries of the political. This chapter provides a base for additional discussion on Gen Z and civic engagement, the role of socio-economic status and social identity, online practices, socialization, and civic education in Chapter 12.