Purpose: Social media present opportunities to intervene on harmful rape myth beliefs among adolescents and young adults, such as through digital bystander intervention. Methods: We conducted a digital experiment to examine young peoples' willingness to intervene on rape myth comments in a simulated social media environment. Participants were adolescents and young adults (n = 712) aged 18-25 years (M = 22.14, SD = 1.92). Participants randomly viewed social media posts with the following: (1) rape myth comments, (2) rape myth comments with bystander intervention comments, or (3) control condition with only neutral comments. Participants then reported willingness to intervene, gender stereotype agreement, alcohol rape myth acceptance, and perceived normativity of bystander behavior. Results: Rape myth comments (with or without bystander comments) led to greater willingness to intervene among participants. Alcohol rape myth acceptance, perceived normativity of bystander behavior, and gender (women vs. men) were all significant moderators of this relationship. Participants susceptible to alcohol rape myths and those who were men were less willing to intervene in the rape myth and rape myth & thorn; bystander conditions. Participants who perceived bystander behavior to be less normal were more willing to intervene in the rape myth-only condition. Discussion: This study explored attitudes of young people exposed to harmful rape myth comments on social media. Future studies should continue this work, especially pursuing ways to reduce undesirable moderation effects of alcohol rape myths and gender. (c) 2025 Society for Adolescent Health and Medicine. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Understanding adolescent online behavior is critical for designing effective online safety interventions; yet, studying such behavior ethically and realistically remains a challenge. To address this, we present SocialSim, a social media simulation tool that builds on an open-source platform designed to enable in situ experimentation in a safe, controlled environment. SocialSim replicates key features of social platforms, including feed, profiles, friending, and private messaging while offering a flexible architecture for researchers to study user interactions and deploy behavioral interventions, such as real-time online safety nudges within direct messaging, which are unsupported in other platforms. We used this tool with a Wizard-of-Oz approach, combined with automated, as well as scripted interactions which allows researchers to simulate lifelike social media while maintaining a safe environment for youth. This demonstration presents the design, technical implementation, and future research implications for this simulation as a sandbox for evaluating online safety interventions, and as a generalizable platform for ethically studying human behavior in an ecologically valid social media environment.

PURPOSE:Positive vaping portrayals and marketing from influencers receive billions of views on visual-based social media. Strategies are needed to counter these appeals and encourage sharing about health harms. METHODS:We recruited 712 US young adults (ages 18-25 years) through Cloud Research for our between-persons experiment. Participants were randomized to view four social media messages in one of the following three conditions: health harms, health harms with a social impact, or control. Participants first viewed and interacted with their condition messages on a simulated social media feed. Participants were shown messages again and reported sharing intentions, perceived message effectiveness, and relevance of each message. Last, participants reported vaping knowledge and beliefs. RESULTS:Health harm messages lead to greater sharing intentions, perceived message effectiveness (discouraged vaping), relevance, knowledge, and beliefs compare to control messages, p = .03-p < .001. Health harms paired with social impact messages discouraged vaping more than control messages but otherwise were rated similarly to the health harm messages. Participants would share antivaping messages mostly with friends (73%-76%) and through private (direct message, 55%) or ephemeral channels (social media stories, 46%-49%). Overall, Black and Hispanic young adults had higher sharing intentions, greater message relevance, and lower vaping knowledge compared to non-Hispanic White participants. Overall, Black young adults were more discouraged from vaping by the messages. DISCUSSION:Communicating novel information about vaping health harms on social media is a promising strategy to reach young adults where they are online and encourage sharing among friends.

This study addresses two currently open questions about how behaviors of online community members influence others’ responses to misinformation. First, in contrast to prior work, it directly measures norm perception to address whether (1) norm perception actually acts as a mediator, (2) others’ behaviors directly influence individuals’ responses to misinformation, (3) both direct and mediated effects occur. Second, it investigates norm perceptions about a behavior that is not readily observable in online communities, but is prone to misinformation, specifically, vaccination. To do so, it experimentally manipulates the prevalence of communicating about vaccination (an unobservable behavior) within an online community. The results demonstrate no evidence of a direct effect—the causal relationship between prevalence of communicating a behavior and intentions to respond to misinformation only occurs via norm perception as a mediator. The paper highlights implications of these findings for designing community-centered interventions to influence perceived norms, thereby mitigating misinformation spread and impacts.

HCI research has been at the forefront of designing interventions for protecting teens online; yet, how can we test and evaluate these solutions without endangering the youth we aim to protect? Towards this goal, we conducted focus groups with 20 teens to inform the design of a social media simulation platform and study for evaluating online safety nudges co-designed with teens. Participants evaluated risk scenarios, personas, platform features, and our research design to provide insight regarding the ecological validity of these artifacts. Teens expected risk scenarios to be subtle and tricky, while also higher in risk to be believable. The teens iterated on the nudges to prioritize risk prevention without reducing autonomy, risk coping, and community accountability. For the simulation, teens recommended using transparency with some deceit to balance realism and respect for participants. Our meta-level research provides a teen-centered action plan to evaluate online safety interventions safely and effectively.

Teens often encounter cyberbullying on social media. One promising way to reduce cyberbullying is through empowering teens to stand up for their peers and cultivating prosocial norms online. While there is no shortage of bystander interventions that have shown potential, little research has explored designing chatbots with users to provide a contextualized and embedded “learning at the moment” experience for bystanders. This study involved teens and educators in two design sessions: an in-depth interview to identify the barriers that prevent upstanding behaviors, and interaction with the “social media co-pilot'' chatbot prototype to identify design guidelines to empower teens to overcome these barriers. Qualitative analysis on the conversations from the two design sessions revealed three factors that curb teens' upstanding behaviors: a) inadequate knowledge about social norms, appropriate language, and consequences, b) inhibitive emotions such as fear of retaliation and confrontation; c) lack of empathy toward their peers. Key parameters were also identified to shape chatbot responses to encourage upstanding behaviors, such as a) adopting voices representing multiple roles, b) empathetic, friendly and encouraging tone, c) reflective, specific and relatable language and d) appropriate length. These insights inform the design of personalized and scalable education programs and moderation tools to combat cyberbullying.

Social norms play a significant role in how conspiratorial content and related misinformation impact online communities. However, less is understood about the mechanisms by which particular aspects of a community may drive perceptions of social norms in the community. Using anti-vaccine conspiracies as a testbed, this paper experimentally examines three such features and their relationships : prevalence of conspiratorial content, community response, and explicit community rules. Results show that prevalence of content has a significant effect on norm perceptions, while the results did not support the effects of explicit rule on norm perceptions. However, these effects can be mitigated by the way a community responds to such content. Furthermore, perceived norms also influence other expectations about the community, from escalated behaviors to belief in other conspiracy theories. The paper concludes by highlighting the implications of these findings for online platform design, for community governance, and for future research about the relationships among conspiratorial content and norm perceptions.

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible.

BACKGROUND:Cancer treatment misinformation, or false claims about alternative cures, often spreads faster and farther than true information on social media. Cancer treatment misinformation can harm the psychosocial and physical health of individuals with cancer and their cancer care networks by causing distress and encouraging people to abandon support, potentially leading to deviations from evidence-based care. There is a pressing need to understand how cancer treatment misinformation is shared and uncover ways to reduce misinformation.OBJECTIVE:We aimed to better understand exposure and reactions to cancer treatment misinformation, including the willingness of study participants to prosocially intervene and their intentions to share Instagram posts with cancer treatment misinformation.METHODS:We conducted a survey on cancer treatment misinformation among US adults in December 2021. Participants reported their exposure and reactions to cancer treatment misinformation generally (saw or heard, source, type of advice, and curiosity) and specifically on social media (platform, believability). Participants were then randomly assigned to view 1 of 3 cancer treatment misinformation posts or an information post and asked to report their willingness to prosocially intervene and their intentions to share.RESULTS:Among US adult participants (N=603; mean age 46, SD 18.83 years), including those with cancer and cancer caregivers, almost 1 in 4 (142/603, 23.5%) received advice about alternative ways to treat or cure cancer. Advice was primarily shared through family (39.4%) and friends (37.3%) for digestive (30.3%) and natural (14.1%) alternative cancer treatments, which generated curiosity among most recipients (106/142, 74.6%). More than half of participants (337/603, 55.9%) saw any cancer treatment misinformation on social media, with significantly higher exposure for those with cancer (53/109, 70.6%) than for those without cancer (89/494, 52.6%; P<.001). Participants saw cancer misinformation on Facebook (39.8%), YouTube (27%), Instagram (22.1%), and TikTok (14.1%), among other platforms. Participants (429/603, 71.1%) thought cancer treatment misinformation was true, at least sometimes, on social media. More than half (357/603, 59.2%) were likely to share any cancer misinformation posts shown. Many participants (412/603, 68.3%) were willing to prosocially intervene for any cancer misinformation posts, including flagging the cancer treatment misinformation posts as false (49.7%-51.4%) or reporting them to the platform (48.1%-51.4%). Among the participants, individuals with cancer and those who identified as Black or Hispanic reported greater willingness to intervene to reduce cancer misinformation but also higher intentions to share misinformation.CONCLUSIONS:Cancer treatment misinformation reaches US adults through social media, including on widely used platforms for support. Many believe that social media posts about alternative cancer treatment are true at least some of the time. The willingness of US adults, including those with cancer and members of susceptible populations, to prosocially intervene could initiate the necessary community action to reduce cancer treatment misinformation if coupled with strategies to help individuals discern false claims.

Prior work has identified a variety of factors that drive the way people identify and respond to misinformation. Such factors include confirmation bias, perceived credibility of the information source, individual media literacy, social norms, and others. This paper reviews the interventions designed to address misinformation and examines how various underlying mechanisms of response to misinformation are operationalized and implemented in the reviewed interventions. Key findings show that most prior work to address misinformation heavily focuses on individual pieces of misinformation and the actions individuals take in response to those individual pieces. These individualistic approaches, we argue, overlook the other drivers of responses to misinformation, such as individuals' prior beliefs and the social contexts in which misinformation is encountered. Additionally, the analysis shows that an individualistic focus on misinformation draws attention away from the systemic nature and consequences of misinformation. This paper argues that to overcome the limitation of individualistic approaches to addressing misinformation, future interventions need to expand their scope beyond individualistic approaches. As one way to do so, it discusses leveraging the impacts of community factors that impact the spread and impacts of misinformation. The paper concludes by using social norms as an example to illustrate how a focus on community factors might work in practice.

Rapid advancements in structural bioinformatics result in short software lifespans due to issues with scalability, portability and maintainability. In many cases, researchers aim to distribute scientific software as part of a research project but lack the development resources to maintain a robust web application server. Here, we introduce a web application framework and example that takes advantage of containers and the virtualization capabilities associated with them, and present an example application as a template. We contain the full web application in a container, which packages all the code and dependencies required for the software. The application itself is built specifically for structural bioinformatics, with a front end GUI and molecular viewer as well as a back end that can be altered to run arbitrary software tools. The container, which is a snapshot of the software, limits the effort required for porting and maintaining the software. At the same time, the architecture we introduce streamlines the process of starting a web server for programmers that are not web developers. Finally, for computationally intensive work, the container transfers computing costs to users in a pay-as-you-use model. An example of the web application implementation built in a container can be found in section 5.

There are many factors that account for disclosure of private information on social network sites, but a potentially powerful determinant that remains understudied is social norms, which refer to perceptions of what other people do, approve of, and expect us to do on social media. To address this gap, we conducted an in-depth analysis of descriptive, injunctive, and subjective norms for verbal and visual disclosure on Facebook and Instagram, using a preregistered survey study with 863 participants. We further analyzed whether critical media literacy and media-related self-reflection could buffer against uncritical adoption of these norms. The findings revealed that all three types of norms positively and independently predicted self-disclosure, regardless of the platform or type of self-disclosure (visual vs. verbal), while controlling for other common predictors of self-disclosure, including perceived benefits and risks of self-disclosure. Self-reflection and critical media literacy neither directly predicted disclosure, nor accounted for differences in norm-behavior relationships.

Problematic content on social media can be countered through objections raised by other community members. While intended to deter offenses, objections can influence the surrounding audience observing the interaction, leading to their collective approval or disapproval. The results of an experiment manipulating seven types of objections against common types of offenses indicate audiences' support for objections that implore via appeals and disapproval of objections that threaten the offender, as they view the former as more moral, appropriate, and effective compared to the latter. Furthermore, audiences tend to prefer more benign and less threatening objections regardless of the offense severity (following the principle of "taking the high road") instead of objections proportionate to the offense ("an eye for an eye"). Taken together, these results show how objections to offensive behaviors may impact collective perceptions on social media, paving the way for interventions to foster effective objection strategies in social media discussions. Problematic content that involves misleading information or contains offensive language is prevalent on social media. Other users can confront it by objecting to this content, but the effectiveness of different objection strategies is not well understood. We conducted a randomized controlled experiment in a simulated social media environment to investigate how audiences interpret (e.g., as appropriate, justified, and persuasive) and react to them (e.g., intentions to upvote and downvote). The results indicate that audiences approve objections that appeal to consciousness, as they are viewed as more appropriate, justified, and effective; conversely, objections that undermine the offender's reputation or imply physical threats are most likely to be downvoted because they are judged as morally questionable. Furthermore, audiences prefer more benign and less threatening objections, even when encountering severe offenses. These findings shed light on what objections-even as all of them call out offensive content-find favor in audiences' eyes, which can be leveraged for interventions and trainings in how to counter offenses on social media.

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

This study examines how visibility of a content moderator and ambiguity of moderated content influence perception of the moderation system in a social media environment. In the course of a two-day pre-registered experiment conducted in a realistic social media simulation, participants encountered moderated comments that were either unequivocally harsh or ambiguously worded, and the source of moderation was either unidentified, or attributed to other users or an automated system (AI). The results show that when comments were moderated by an AI versus other users, users perceived less accountability in the moderation system and had less trust in the moderation decision, especially for ambiguously worded harassments, as opposed to clear harassment cases. However, no differences emerged in the perceived moderation fairness, objectivity, and participants confidence in their understanding of the moderation process. Overall, our study demonstrates that users tend to question the moderation decision and system more when an AI moderator is visible, which highlights the complexity of effectively managing the visibility of automatic content moderation in the social media environment.

This study evaluates whether increasing information visibility around the identity of a moderator influences bystanders’ likelihood to flag subsequent unmoderated harassing comments. In a 2-day preregistered experiment conducted in a realistic social media simulation, participants encountered ambiguous or unambiguous harassment comments, which were ostensibly flagged by either other users, an automated system (AI), or an unidentified moderation source. The results reveal that visibility of a content moderation source inhibited participants’ flagging of a subsequent unmoderated harassment comment, presumably because their efforts were seen as dispensable, compared to when the moderation source was unknown. On the contrary, there was an indirect effect of other users versus AI as moderation source on subsequent flagging through changes in perceived social norms. Overall, this research shows that the effects of moderation transparency are complex, as increasing visibility of a content moderator may inadvertently inhibit bystander intervention. This study examines the effects of flagging unmoderated offensive posts on social media, and how this changes the users’ subsequent behavior. We examined users’ reactions to the flagging of these posts by other users, an automated system, or an unspecified process to determine whether this affects the users’ ensuing behavior. A 2-day experiment on a simulated social media site showed that the visibility of the “flagger” impacts how users perceive social norms and think about the accountability for their own online actions. The results showed that the visibility of the person/system that flagged the material generally deterred subsequent flagging. The analysis also shows that the effect was stronger when the users thought that it was other users, and not an automated system, that had flagged the online harassment.

Social norms are powerful determinants of human behaviors in offline and online social worlds. While previous research established a correlational link between norm perceptions and self-reported disclosure on social network sites (SNS), questions remain about downstream effects of prevalent behaviors on perceived norms and actual disclosure on SNS. We conducted two preregistered studies using a realistic social media simulation. We further analyzed buffering effects of critical media literacy and privacy nudging. The results demonstrate a disclosure behavior contagion, whereby a critical mass of posts with visual disclosures shifted norm perceptions, which, in turn, affected perceivers’ own visual disclosure behavior. Critical media literacy was negatively related and moderated the effect of norms on visual disclosure behavioral intentions. Neither critical media literacy nor privacy nudge affected actual disclosure behaviors, however. These results provide insights into how behaviors may spread on SNS through triggering changes in perceived social norms and subsequent disclosure behaviors.

Conversational agents are increasingly becoming integrated into everyday technologies and can collect large amounts of data about users. As these agents mimic interpersonal interactions, we draw on communication privacy management theory to explore people's privacy expectations with conversational agents. We conducted a 3x3 factorial experiment in which we manipulated agents' social interactivity and data sharing practices to understand how these factors influence people's judgments about potential privacy violations and their evaluations of agents. Participants perceived agents that shared response data with advertisers more negatively compared to agents that shared such data with only their companies; perceptions of privacy violations did not differ between agents that shared data with their companies and agents that did not share information at all. Participants also perceived the socially interactive agent's sharing practices less negatively than those of the other agents, highlighting a potential privacy vulnerability that users are exposed to in interactions with socially interactive conversational agents.

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should allocate resources among group members and about what the consequences of such allocation are for a group’s social dynamics and outcomes. Methodological advances are needed to answer these questions allow researchers to collect data about a robot’s impact not only on interactions with the robot but also on interactions of people with each other. This paper presents Robot Assisted Tower Construction, a novel task that allows researchers to examine the impact of a robot’s allocation behavior on the dynamics of a group or team collaborating on a task. By focusing on the question of whether and how a robot’s allocation of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, a case is provided of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves a group of people. We highlight the kinds of insights the task can yield and how it can be adapted to various human robot collaboration contexts.

Social media sites are where life happens for many of today's young people, so it is important to teach them to use these sites safely and effectively. Many youth receive classroom education on digital literacy topics, but have few chances to build actual skills. Social Media TestDrive, an interactive social media simulation, fills a gap in digital literacy education by combining experiential learning in a realistic and safe social media environment with educator-facilitated classroom lessons. The tool was piloted with 12 educators and over 200 students, and formative evaluation data suggest that TestDrive achieved high levels of engagement with both groups. Students reported the modules enhanced their understanding of digital citizenship issues, and educators noted that students were engaging in meaningful classroom conversations. Finally, we discuss the importance of involving multiple stakeholder groups (e.g., researchers, youth, educators, curriculum developers) in designing educational technology.