Cancer misinformation on social media.

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks-such as their extensive use and the relative ease it allows to share information quickly-facilitate the spread of misinformation. Research shows that inaccurate and misleading health-related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health-related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence-based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

The effects of Facebook and Instagram on the 2020 election: A deactivation experiment.

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Social media influences National Park visitation.

Visitation to National Parks in the United States increased by more than 25% since 2010, rising from roughly 70 to 90 million annual visitors. Anecdotes suggest that this increase was driven by the advent of social media in the early-to-mid 2010s, generating a new form of exposure for parks, and has led to concerns about overcrowding and degradation of environmental quality. However, there is little empirical evidence on the role of social media in influencing recreation decisions. Here, I construct a dataset on social media exposure (SME) for each National Park and relate that exposure to changes in visitation over the last two decades. High SME parks see visitation increase by 16 to 22% relative to parks with less exposure, which comes with a concomitant increase in revenue. Low SME parks have no, or negative, changes in visitation. These estimates account for unobserved park heterogeneity and are based on an instrumental variables strategy that predicts exposure with a park's online popularity prior to the social media era. Additional analysis suggests that recent social media posts that include media attachments increase visitation, while posts with negative sentiment reduce visitation. These results provide insight for the National Park Service-which faces more than $22 billion in deferred maintenance costs and is considering policy options to manage demand-as well as for management of recreation on other public lands.

Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.

January 6 arrests and media coverage do not remobilize conservatives on social media.

Social media's pivotal role in catalyzing social movements is widely acknowledged across scientific disciplines. Past research has predominantly explored social media's ability to instigate initial mobilization while leaving the question of its capacity to sustain these movements relatively uncharted. This study investigates the persistence of movement activity on Twitter and Gab following a substantial on-the-ground mobilization event catalyzed by social media-the StoptheSteal movement culminating in the January 6th Capitol attack. Our findings indicate that the online communities active in the January 6 mobilization did not display substantial remobilization in the subsequent year. These results highlight the fact that further exploration is needed to understand the factors shaping how and when movements are sustained by social media. In this regard, our study provides valuable insights for scientists across diverse disciplines, on how certain social media platforms may contribute to the evolving dynamics of collective action.

People who share encounters with racism are silenced online by humans and machines, but a guideline-reframing intervention holds promise.

Are members of marginalized communities silenced on social media when they share personal experiences of racism? Here, we investigate the role of algorithms, humans, and platform guidelines in suppressing disclosures of racial discrimination. In a field study of actual posts from a neighborhood-based social media platform, we find that when users talk about their experiences as targets of racism, their posts are disproportionately flagged for removal as toxic by five widely used moderation algorithms from major online platforms, including the most recent large language models. We show that human users disproportionately flag these disclosures for removal as well. Next, in a follow-up experiment, we demonstrate that merely witnessing such suppression negatively influences how Black Americans view the community and their place in it. Finally, to address these challenges to equity and inclusion in online spaces, we introduce a mitigation strategy: a guideline-reframing intervention that is effective at reducing silencing behavior across the political spectrum.

On the rise of fear speech in online social media.

Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fear speech. Fear speech, as the name suggests, attempts to incite fear about a target community. Although subtle, it might be highly effective, often pushing communities toward a physical conflict. Therefore, understanding their prevalence in social media is of paramount importance. This article presents a large-scale study to understand the prevalence of 400K fear speech and over 700K hate speech posts collected from Gab.com. Remarkably, users posting a large number of fear speech accrue more followers and occupy more central positions in social networks than users posting a large number of hate speech. They can also reach out to benign users more effectively than hate speech users through replies, reposts, and mentions. This connects to the fact that, unlike hate speech, fear speech has almost zero toxic content, making it look plausible. Moreover, while fear speech topics mostly portray a community as a perpetrator using a (fake) chain of argumentation, hate speech topics hurl direct multitarget insults, thus pointing to why general users could be more gullible to fear speech. Our findings transcend even to other platforms (Twitter and Facebook) and thus necessitate using sophisticated moderation policies and mass awareness to combat fear speech.

Greed communication predicts the approval and reach of US senators' tweets.

Social media are at the forefront of modern political campaigning. They allow politicians to communicate directly with constituents and constituents to endorse politicians' messages and share them with their networks. Analyzing every tweet of all US senators holding office from 2013 to 2021 (861,104 tweets from 140 senators), we identify a psycholinguistic factor, greed communication, that robustly predicts increased approval (favorites) and reach (retweets). These effects persist when tested against diverse established psycholinguistic predictors of political content dissemination on social media and various other psycholinguistic variables. We further find that greed communication in the tweets of Democratic senators is associated with greater approval and retweeting compared to greed communication in the tweets of Republican senators, especially when those tweets also mention political outgroups.

Sharing of misinformation is habitual, not just lazy or biased.

Why do people share misinformation on social media? In this research (N = 2,476), we show that the structure of online sharing built into social platforms is more important than individual deficits in critical reasoning and partisan bias-commonly cited drivers of misinformation. Due to the reward-based learning systems on social media, users form habits of sharing information that attracts others' attention. Once habits form, information sharing is automatically activated by cues on the platform without users considering response outcomes such as spreading misinformation. As a result of user habits, 30 to 40% of the false news shared in our research was due to the 15% most habitual news sharers. Suggesting that sharing of false news is part of a broader response pattern established by social media platforms, habitual users also shared information that challenged their own political beliefs. Finally, we show that sharing of false news is not an inevitable consequence of user habits: Social media sites could be restructured to build habits to share accurate information.

Social Media and Morality.

Nearly five billion people around the world now use social media, and this number continues to grow. One of the primary goals of social media platforms is to capture and monetize human attention. One means by which individuals and groups can capture attention and drive engagement on these platforms is by sharing morally and emotionally evocative content. We review a growing body of research on the interrelationship of social media and morality as well its consequences for individuals and society. Moral content often goes viral on social media, and social media makes moral behavior (such as punishment) less costly. Thus, social media often acts as an accelerant for existing moral dynamics, amplifying outrage, status seeking, and intergroup conflict while also potentially amplifying more constructive facets of morality, such as social support, prosociality, and collective action. We discuss trends, heated debates, and future directions in this emerging literature.

Police agencies on Facebook overreport on Black suspects.

A large and growing share of the American public turns to Facebook for news. On this platform, reports about crime increasingly come directly from law enforcement agencies, raising questions about content curation. We gathered all posts from almost 14,000 Facebook pages maintained by US law enforcement agencies, focusing on reporting about crime and race. We found that Facebook users are exposed to posts that overrepresent Black suspects by 25 percentage points relative to local arrest rates. This overexposure occurs across crime types and geographic regions and increases with the proportion of both Republican voters and non-Black residents. Widespread exposure to overreporting risks reinforcing racial stereotypes about crime and exacerbating punitive preferences among the polity more generally.

Algorithmic amplification of politics on Twitter.

Content on Twitter's home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There's been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.

Determining containment policy impacts on public sentiment during the pandemic using social media data.

Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people's emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from −1 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.

The virtual disengagement hypothesis: A neurophysiological framework for reduced empathy on social media.

Social media is a hotbed of interpersonal conflict and aggression. Platforms such as Twitter and Instagram are used by more than 62% of the global population, facilitating billions of user interactions every day. However, many of these exchanges involve hostile, insensitive, and antisocial behaviors. This raises the question: is empathy blunted on social media? Substantial evidence demonstrates that humans tend to behave more rudely in virtual settings, but considering the scarcity of physiological data collected under these circumstances, it remains unclear how the neural systems guiding social cognition and empathy may function differently in online interactions. We propose the "Virtual Disengagement Hypothesis," a conceptual framework to explain the prevalence of hostility online. It posits that interactions occurring on social media omit social cues that facilitate the assessment of a social partner's affective state, such as facial expressions and vocal tone, and thus fail to sufficiently recruit brain circuitry involved in empathy, such as the anterior cingulate cortex, insula, and prefrontal cortex. Additionally, interactions on social media occur asynchronously and in a "replayed" context, which may further limit recruitment of empathy systems. As a result of this diminished sensitivity to others' states, users may be predisposed to inconsiderate or outright antisocial behaviors. Given the massive and growing base of users on these platforms, we urge researchers to expand efforts that focus on neuroimaging in virtual settings with a particular emphasis on developing social media-relevant behavioral designs.

Influences of HPV disease perceptions, vaccine accessibility, and information exposure on social media on HPV vaccination uptake among 11,678 mothers with daughters aged 9-17 years in China: a cross-sectional study.

BACKGROUND: Mothers play a crucial role in influencing their daughters' HPV vaccination decisions. Addressing barriers to receiving HPV vaccination among mothers of girls may achieve two goals in one strike: increasing vaccination coverage among both mothers and their daughters. This study aims to examine the HPV vaccination uptake and its determinants among mothers of girls in China at both the individual and interpersonal levels. METHODS: From July to October 2023, a cross-sectional online study was conducted to investigate HPV vaccine refusal for daughters aged 9-17 years among 11,678 mothers in Shenzhen, China. A randomized selection method was employed, targeting 11 primary schools and 13 secondary schools in Shenzhen. The research team invited mothers of girls to participate in an anonymous online survey. Multilevel logistic regression models (level 1: schools; level 2: individual participants) were employed to analyze the data. RESULTS: Among 11,678 mothers, 41.1% self-reported receiving at least one dose of HPV vaccination. Through multilevel logistic regression analysis, eight items measuring illness representations of HPV, which refers to how people think about HPV, were associated with higher HPV vaccination uptake (AOR: 1.02-1.14). These items included identity (identifying symptoms of HPV), timeline (whether HPV is acute/chronic), negative consequences, personal and treatment control (whether HPV is under volitional control), concern, negative emotions, and coherence (overall understanding of HPV). In addition, participants refusing HPV vaccines for the index daughters (AOR: 0.82, 95%CI: 0.76, 0.89) had lower vaccine uptake. Perceived more difficulties in accessing the 9-valent vaccines (AOR: 1.06, 95%CI: 1.04, 1.08) and more satisfaction with vaccine-related promotional materials (AOR: 1.50, 95%CI: 1.46, 1.54) at the individual level were associated with higher vaccine uptake. At the interpersonal factors, higher frequency of exposure to testimonials given by others about HPV vaccination on social media (AOR: 1.19, 95%CI: 1.14, 1.25) and thoughtful consideration of the veracity of the information (AOR: 1.11, 95%CI: 1.07, 1.16) were correlated with higher HPV vaccination uptake. CONCLUSIONS: These findings offer essential implications for modifying HPV disease perceptions, addressing difficulties in accessing the 9-valent HPV vaccines, and enhancing health communication needs to improve HPV vaccine uptake among mothers of girls.

Cancer fatalism, social media informational awareness, and education.

OBJECTIVE: Understand if cancer fatalism among adult social media users in the United States is linked to social media informational awareness and if the relationship varies by education level. METHODS: Cross-sectional data from the 2022 Health Information National Trends Survey (n = 3,948) were analyzed using multivariable linear probability models. The study population was defined as social media users active within the past year. The outcome variable was cancer fatalism and the predictor variables were social media informational awareness and education level. RESULTS: Participants with low social media informational awareness were 9% (95% CI = 3, 15), 6% (95% CI = 1, 11), and 21% (95% CI = 14, 27) percentage points more likely to agree that it seems like everything causes cancer, you cannot lower your chances of getting cancer, and there are too many cancer prevention recommendations to follow, respectively. Participants with a college degree or higher level of education and who reported high social media informational awareness were the least likely to agree that everything causes cancer (60%; 95% CI = 54, 66), you cannot lower your chances of getting cancer (14%; 95% CI = 10, 19), and there are too many cancer prevention recommendations to follow (52%; 95% CI = 46, 59). CONCLUSION: Social media informational awareness was associated with lower levels of cancer fatalism among adult social media users. College graduates with high social media informational awareness were the least likely to report cancer fatalism.

Determinants for Mediterranean diet adherence beyond the boundaries: a cross-sectional study from Sharjah, the United Arab Emirates.

BACKGROUND: Substantial evidence embraced the nutrition competence of the Mediterranean diet (MD) as a healthy model for decreasing the risk of chronic diseases and increasing longevity, with the bonus of ensuring environmental sustainability. Measuring adherence to this diet is marginally investigated in the Arabian Gulf region, an area away from the Mediterranean region. The current study aimed to assess the MD adherence among adults in Sharjah/the United Arab Emirates (UAE), and to identify the most influential predictors for MD adherence among the study participants. METHODS: A cross-sectional study design was employed using a self-reported, web-based electronic questionnaire that questioned sociodemographics, lifestyle factors, and familiarity with the MD. The MD adherence was assessed by the Mediterranean Diet Adherence Screener validated questionnaire. The adherence level was classified as low for a total score of [0-5], medium [score 6-7], and high (8-13). RESULTS: The study included 1314 participants (age 25-52 years) comprised 822 (62.6%) females and 492 (37.4%) males. There was a moderate adherence score (5.9 ± 1.9) among the study participants. The food constituent expressed the lowest contribution to the MD was fish (9.3%), followed by fruits (12.3%), and legumes (18.3%). The multivariable linear regression analysis showed an overall significant linear trend for the association between the MD adherence score and physical activity, while nutrition information from dietitians and social media were the most two strongly related predictors for the higher adherence (ß = 0.747; 95% CI 0.51-0.98, and ß 0.60; 95% CI 0.269-0.93; p < 0.001, respectively). On the other side, being a smoker and from a non-Mediterranean country was associated with lower adherence scores (ß = 0.538; 95% CI 0.252-0.82, p < 0.001). CONCLUSION: The findings of the current study showed a moderate adherence, low proportion for high adherence, and a gap in the familiarity with the diet name. Being married, physically active, non-smoker, and getting nutrition information from dietitians and social media were the strongest predictors for higher adherence. It is warranted that public health and nutrition specialists/dietitians to tailor new modern approaches for promoting healthy dietary behaviours consistent with the MD.

Generative AI Platform for Automating Social Media Posts From Urology Journal Articles: A Cross-Sectional Study and Randomized Assessment.

PURPOSE: This cross-sectional study assessed a generative-AI platform to automate the creation of accurate, appropriate, and compelling social-media (SoMe) posts from urological journal articles. MATERIALS AND METHODS: One hundred SoMe-posts from the top 3 journals in urology X (Twitter) profiles were collected from Aug-2022 to Oct-2023 A freeware GPT-tool was developed to auto-generate SoMe posts, which included title-summarization, key findings, pertinent emojis, hashtags, and DOI links to the article. Three physicians independently evaluated GPT-generated posts for achieving tetrafecta of accuracy and appropriateness criteria. Fifteen scenarios were created from 5 randomly selected posts from each journal. Each scenario contained both the original and the GPT-generated post for the same article. Five questions were formulated to investigate the posts' likability, shareability, engagement, understandability, and comprehensiveness. The paired posts were then randomized and presented to blinded academic authors and general public through Amazon Mechanical Turk (AMT) responders for preference evaluation. RESULTS: Median (IQR) time for post auto-generation was 10.2 seconds (8.5-12.5). Of the 150 rated GPT-generated posts, 115 (76.6%) met the correctness tetrafecta: 144 (96%) accurately summarized the title, 147 (98%) accurately presented the articles' main findings, 131 (87.3%) appropriately used emojis and hashtags 138 (92%). A total of 258 academic urologists and 493 AMT responders answered the surveys, wherein the GPT-generated posts consistently outperformed the original journals' posts for both academicians and AMT responders (P < .05). CONCLUSIONS: Generative-AI can automate the creation of SoMe posts from urology journal abstracts that are both accurate and preferable by the academic community and general public.

The Top Ten Annals of Surgical Oncology Original Articles on Twitter/X: 2020-2023.

Social media has become omnipresent in society, especially given that it enables the rapid and widespread communication of news, events, and information. Social media platforms have become increasingly used by numerous surgical societies to promote meetings and surgical journals to increase the visibility of published content. In September 2020, Annals of Surgical Oncology (ASO) established its Social Media Committee (SMC), which has worked to steadily increase the visibility of published content on social media platforms, namely X (formerly known as Twitter). The purpose of this review is to highlight the 10 ASO original articles with the most engagement on X, based on total number of mentions, since the founding of the SMC. These articles encompass a wide variety of topics from various oncologic disciplines including hepatopancreatobiliary, breast, and gynecologic surgery.