Michael Robert Haupt

The growing use of glucagon-like peptide 1 (GLP-1) drugs such as Ozempic for weight loss interventions has led to increased concerns about inappropriate use, supply shortages, and high-risk sourcing. However, little is known about the influence of online marketing strategies on purchasing preferences for GLP-1 products. This study uses a survey with a Choice-Based Conjoint (CBC) exercise to assess features of social media posts that are most influential for intent to purchase GLP-1 products and identify motivations for use. Participants were recruited based on current use of GLP-1 drugs, previous use, or considering use (n = 242). Utility scores from the conjoint analysis quantified the influence of tested features. Conjoint findings showed greater preference for posts that feature branded semaglutide (e.g., Wegovy)(15.38, SD = 45.29), display products in packaging (47.49, SD = 46.72), have accredited digital pharmacies as the seller (25.16, SD = 47.31) (e.g., Amazon Pharmacy), include medical endorsements for diabetes treatment (13.16, SD = 49.04), and offer a free consultation (13.18, SD = 32.00). Analysis was conducted on survey questions measuring GLP-1 purchasing channels and motivations for use. Over half of participants (51.7%) reported that social media content influenced their perceptions of GLP-1 drugs and 45.8% (n = 65) of GLP-1 users reported purchasing product from high-risk sources (i.e., personal network or individual sellers online). Exploratory analysis shows a possible association between self-reported high-risk sourcing and reliance on GLP-1s for medical conditions. Overall, our findings identify influential attributes of GLP-1 social media ads (i.e., branding and visual display of products) and suggests greater support is needed for online moderation efforts to prevent high-risk purchasing activity and increase safe access to GLP-1s for those with medical needs.

Social media platforms enhance the propagation of online misinformation by providing large user bases with a quick means to share content. One way to disrupt the rapid dissemination of misinformation at scale is through warning tags, which label content as potentially false or misleading. However, past warning tag mitigation studies yield mixed results for diverse audiences. We hypothesize that personalizing warning tags to the individual characteristics of their diverse users may enhance mitigation effectiveness. To reach the goal of personalization, we need to understand how people differ and how those differences predict a person's attitudes and behaviors toward tags and tagged content. In this study, we leverage Amazon Mechanical Turk (n = 132) and undergraduate students (n = 112) to provide this foundational understanding. With all participants combined, we find attitudes towards warning tags and self-described behaviors are significantly influenced by factors such as Need for Cognitive Closure (NFCC), Political orientation, and Trust in Medical Scientists when controlled for covariates such as age and recruiting platform. Analyses of each sample further show that tag attitudes were influenced by Trust in Religious Leaders, and Big Five Inventory (BFI) traits for Openness and Conscientiousness. We synthesize these results into design insights and a future research agenda for more effective and personalized warning tags and misinformation mitigation strategies more generally.

As radical messaging has proliferated on social networking sites, platforms like Reddit have been used to host support groups, including support communities for the families and friends of radicalized individuals. This study examines the subreddit r/QAnonCasualties, an online forum for users whose loved ones have been radicalized by QAnon. We collected 1,665 posts and 78,171 comments posted between 7/2021 and 7/2022 and content coded top posts for prominent themes. Sentiment analysis was also conducted on all posts. We find venting, advice and validation-seeking, and pressure to refuse the COVID-19 vaccine were prominent themes. 40% (n=167) of coded posts identified the Q relation(s) of users as their parent(s) and 16.3% (n=68) as their partner. Posts with higher proportions of words related to swearing, social referents, and physical needs were positively correlated with engagement. These findings show ways that communities around QAnon adherents leverage anonymous online spaces to seek and provide social support.

Background: Explanations for why social media users propagate misinformation include failure of classical reasoning (over-reliance on intuitive heuristics), motivated reasoning (conforming to group opinion), and personality traits (e.g., narcissism). However, there is a lack of consensus on which explanation is most predictive of misinformation spread. Previous work is also limited by not distinguishing between passive (i.e., “liking”) and active (i.e., “retweeting”) propagation behaviors.Methods: To examine this issue, 858 Twitter users were recruited to engage in a Twitter simulation task in which they were shown real tweets on public health topics (e.g., COVID-19 vaccines) and given the option to “like”, “reply”, “retweet”, “quote”, or select “no engagement”. Survey assessments were then given to measure variables corresponding to explanations for: classical reasoning [cognitive reflective thinking (CRT)], motivated reasoning (religiosity, political conservatism, and trust in medical science), and personality traits (openness to new experiences, conscientiousness, empathy, narcissism).Results: Cognitive reflective thinking, conscientiousness, openness, and emotional concern empathy were all negatively associated with liking misinformation, but not significantly associated with retweeting it. Trust in medical scientists was negatively associated with retweeting misinformation, while grandiose narcissism and religiosity were positively associated. An exploratory analysis on engagement with misinformation corrections shows that conscientiousness, openness, and CRT were negatively associated with liking corrections while political liberalism, trust in medical scientists, religiosity, and grandiose narcissism were positively associated. Grandiose narcissism was the only factor positively associated with retweeting corrections.Discussion: Findings support an inhibitory role for classical reasoning in the passive spread of misinformation (e.g., “liking”), and a major role for narcissistic tendencies and motivated reasoning in active propagating behaviors (“retweeting”). Results further suggest differences in passive and active propagation, as multiple factors influence liking behavior while retweeting is primarily influenced by two factors. Implications for ecologically valid study designs are also discussed to account for greater nuance in social media behaviors in experimental research.

Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI’s ChatGPT imitates specific social roles or identities. This research examines how ChatGPT’s accuracy in detecting COVID-19–related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications. Objective: This study investigates the impact of role-playing prompts on ChatGPT’s accuracy in detecting misinformation. This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions. Methods: Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation, sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was tested with prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality, religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts with no identities and those including only political identity. Results: The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT’s explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances. Conclusions: These results indicate that ChatGPT’s ability to classify misinformation is negatively impacted when role-playing social identities, highlighting the complexity of integrating human biases and perspectives in LLMs. This points to the need for human oversight in the use of LLMs for misinformation detection. Further research is needed to understand how LLMs weigh social identities in prompt-based tasks and explore their application in different cultural contexts.

BACKGROUND During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI’s ChatGPT imitates specific social roles or identities. This research examines how ChatGPT’s accuracy in detecting COVID-19–related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications. OBJECTIVE This study investigates the impact of role-playing prompts on ChatGPT’s accuracy in detecting misinformation. This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions. METHODS Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation, sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was tested with prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality, religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts with no identities and those including only political identity. RESULTS The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT’s explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances. CONCLUSIONS These results indicate that ChatGPT’s ability to classify misinformation is negatively impacted when role-playing social identities, highlighting the complexity of integrating human biases and perspectives in LLMs. This points to the need for human oversight in the use of LLMs for misinformation detection. Further research is needed to understand how LLMs weigh social identities in prompt-based tasks and explore their application in different cultural contexts.

Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how attributes of social media posts (i.e., features) influence safety evaluations of mock posts selling illicit substances. 440 participants were recruited online for self-reporting use or purchase of controlled substances or prescription medicines recreationally. The following attributes were tested: drug packaging, drug offerings, profile photo of seller, payment info provided, and use of emojis. Results: Packaging was ranked the most important attribute (Average Importance =43.68, Offering=14.94, Profile=13.86, Payment=14.11, Emoji=13.41), with posts that displayed drugs in pill bottles assessed as the most safe. Attribute levels for advertising multiple drugs, having a blank profile photo, including payment information, and including emojis also ranked higher in perceived safety. Rankings were consistent across tested demographic factors (i.e., gender, age, and income). Survey results show that online pharmacies were most likely to be perceived as safe for purchasing drugs and medications. Additionally, those who were younger in age, had higher income, and identified as female were more likely to purchase from a greater number of platforms. Conclusions: These findings can assist in developing more precise content moderation for platforms seeking to address this ongoing threat to public safety.

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as ‘X’). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

The experience of rejection and disconnection reliably amplifies pain. Yet, little is known about the impact of enduring feelings of closeness, or social connectedness, on experiences of chronic pain. The current secondary analysis tested the hypothesis that greater social connectedness would predict lower chronic pain ratings, mediated by lower depression and anxiety. In addition, based on the social-affective effects of deeper pressure, and our previous finding that deeper pressure from a weighted blanket reduced chronic pain ratings, we examined whether deeper pressure from a weighted blanket would induce greater pain relief in socially disconnected chronic pain patients. We assessed social connectedness, anxiety, and depression at baseline and pain levels before and after a remote, 7-day randomized-controlled trial of a heavy or light (control) weighted blanket in a predominately White (86%) and female (80%) sample of 95 chronic pain patients. Results revealed that lower social connectedness was associated with higher chronic pain ratings, which was mediated by anxiety, but not depression. Pressure level (light vs. deep) moderated associations between social connectedness and pain reductions, such that deeper pressure was necessary for pain relief in the most socially disconnected participants. Our findings suggest a close relationship between social connectedness and chronic pain through a mechanistic pathway of anxiety. Furthermore, our findings demonstrate that sensory-affective interventions such as a weighted blanket may be a beneficial tool for chronic pain sufferers who are prone to social disconnection, potentially by activating embodied representations of safety and social support. (PsycInfo Database Record (c) 2023 APA, all rights reserved).