Short-form video platforms have become major channels for misinformation, with their rich multimodal features making false claims highly believable. HCI research shows that providing corrections in the same modality as the misinformation can be an effective solution. However, since corrections and misinformation convey contradicting information, the order in which one is exposed to them can impact what one believes. We conducted a between-subjects mixed-methods experiment where participants (N=120) rated the credibility of misinformation statements before and after viewing misinformation videos paired with correction videos. Corrections were shown either before, during, or after misinformation. Across all three timings, corrections reduced belief in misinformation, but post-exposure corrections proved most effective and mid-exposure corrections least effective. These findings suggest that correction mechanisms should appear after misinformation exposure, while avoiding mid-exposure interruptions that reduce impact. We outline design recommendations for integrating correction videos into short-form video platforms to improve resilience against misinformation.

As multi-agent Large Language Models (LLMs) gain traction, designers must consider how to surface their internal reasoning in ways that foster appropriate trust. We present a design-led, qualitative, comparative structured observation study, exploring how users interpret and evaluate transparency in multi-agent LLMs. Participants interacted with five interface variants, each instantiating different combinations of transparency-related design dimensions, across two task types: information-seeking and logical reasoning. We surface participants’ mental models, the cues they interpret as signals of transparency and trustworthiness, and how they weigh the costs and benefits of increasing process visibility. Transparency needs were dynamic and context-sensitive, with the ideal “Goldilocks” (i.e., “just right” transparency) level shaped jointly by task demands, interface affordances, and user characteristics such as task expertise and dispositional AI trust. We highlight tensions between process visibility, information sufficiency, and cognitive effort, and synthesise these insights into design considerations for aligning transparency with user needs in future multi-agent LLM interfaces.

While short video platforms such as TikTok, YouTube Shorts, and Instagram Reels are frequently criticised for facilitating the spread of misinformation, they are also increasingly leveraged as tools for countering it through debunking content. Although video-based corrections have demonstrated effectiveness, their persuasive impact may depend on the richness of their audio-visual elements. This study examines the persuasive efficacy of three fundamental presentation styles commonly used in short-form video content: (1) videos featuring only captions, (2) captions accompanied by relevant images, and (3) captions presented alongside the creator’s visible face. Our results indicate that videos incorporating either relevant and engaging imagery or the creator’s facial presence are significantly more persuasive than those relying solely on captions. Based on these findings, we propose practical recommendations for improving the effectiveness of debunking videos, with the aim of promoting belief revision and mitigating misinformation on short video platforms.

Virtual AI-generated Influencers (VAIIs) are increasingly being used by corporations and public agencies, raising questions about how their visual design and communication strategies impact end-users’ propensity to accept the messages they deliver. We examined the impact of human-likeness (how closely a VAII resembles a human) and self-disclosure (whether the message contains personal information) on message acceptance, alongside dispositional factors like empathy and anthropomorphising tendencies. In a mixed-methods experiment, participants (N=120) watched short-form videos featuring VAIIs of varying human-likeness (High/Moderate-High/Moderate-Low/Low) and self-disclosure (present/absent). We observed the strongest message acceptance from the VAIIs with the lowest human-likeness, and message rejection for VAIIs with moderate to low human-likeness. Additionally, participants’ message acceptance was influenced by their empathy tendencies. Our qualitative analysis revealed further insights into participants’ perceptions of the human-likeness of VAIIs, their discomfort with self-disclosure, and their tendency to anthropomorphise VAIIs. These findings provide important implications for the design of VAIIs.

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases.

Social media has become a primary information source, with platforms evolving from text-based to multi-modal environments that include images and videos. While richer media modalities enhance user engagement, they also increase the spread and perceived credibility of misinformation. Most interventions to counter misinformation on social media are text-based, which may lack the persuasive power of richer modalities. This study explores whether the effectiveness of misinformation correction varies by modality, and if certain modalities of misinformation are better countered by a specific correction modality. We conducted a survey-based experiment where participants rated the credibility of misinformation tweets before and after exposure to corrections, across all combinations of text, images and video modalities. Our findings suggest that corrections are most effective when their modality richness matches that of the original misinformation. We discuss factors affecting the perceived credibility of corrections and offer strategies to optimise misinformation correction.

Humans naturally seek to identify causes behind outcomes through causal attribution, yet Human-AI research often overlooks how users perceive causality behind AI decisions. We examine how this perceived locus of causality—internal or external to the AI—influences trust, and how decision stakes and outcome favourability moderate this relationship. Participants (N=192) engaged with AI-based decision-making scenarios operationalising varying loci of causality, stakes, and favourability, evaluating their trust in each AI. We find that internal attributions foster lower trust as participants perceive the AI to have high autonomy and decision-making responsibility. Conversely, external attributions portray the AI as merely “a tool” processing data, reducing its perceived agency and distributing responsibility, thereby boosting trust. Moreover, stakes moderate this relationship—external attributions foster even more trust in lower-risk, low-stakes scenarios. Our findings establish causal attribution as a crucial yet underexplored determinant of trust in AI, highlighting the importance of accounting for it when researching trust dynamics.

The start of the 2020s ushered in a new era of AI through the rise of Generative AI Large Language Models (LLMs) such as ChatGPT. These AI chatbots offer a form of interactive agency by enabling users to ask questions and query for more information. However, prior research only considers if LLMs have a political bias or agenda, and not how a biased LLM can impact a user’s opinion and trust. Our study bridges this gap by investigating a scenario where users read online news articles and then engage with an interactive AI chatbot, where both the news and the AI are biased to hold a particular stance on a news topic. Interestingly, participants were far more likely to adopt the narrative of a biased chatbot over news articles with an opposing stance. Participants were also substantially more inclined to adopt the chatbot’s narrative if its stance aligned with the news—all compared to a control news-article only group. Our findings suggest that the very interactive agency offered by an AI chatbot significantly enhances its perceived trust and persuasive ability compared to the ‘ static ’ articles from established news outlets, raising concerns about the potential for AI-driven indoctrination. We outline the reasons behind this phenomenon and conclude with the implications of biased LLMs for HCI research, as well as the risks of Generative AI undermining democratic integrity through AI-driven Information Warfare.

Robots are embodied agents that act under several sources of uncertainty. When assisting humans in a collaborative task, robots need to communicate their uncertainty to help inform decisions. In this study, we examine the use of visualising a robot’s uncertainty in a high-stakes assisted decision-making task. In particular, we explore how different modalities of uncertainty visualisations (graphical display vs. the robot’s embodied behaviour) and confidence levels (low, high, 100%) conveyed by a robot affect the human decision-making and perception during a collaborative task. Our results show that these visualisations significantly impact how participants arrive to their decision as well as how they perceive the robot’s transparency across the different confidence levels. We highlight potential trade-offs and offer implications for robot-assisted decision-making. Our work contributes empirical insights on how humans make use of uncertainty visualisations conveyed by a robot in a critical robot-assisted decision-making scenario.

Generative AI systems have been heralded as tools for augmenting human creativity and inspiring divergent thinking, though with little empirical evidence for these claims. This paper explores the effects of exposure to AI-generated images on measures of design fixation and divergent thinking in a visual ideation task. Through a between-participants experiment (N=60), we found that support from an AI image generator during ideation leads to higher fixation on an initial example. Participants who used AI produced fewer ideas, with less variety and lower originality compared to a baseline. Our qualitative analysis suggests that the effectiveness of co-ideation with AI rests on participants’ chosen approach to prompt creation and on the strategies used by participants to generate ideas in response to the AI’s suggestions. We discuss opportunities for designing generative AI systems for ideation support and incorporating these AI tools into ideation workflows.

Misinformation is rampant on social media, and existing platform-supplied interventions offer limited effectiveness. In this study, we examine the effectiveness of credibility labels that dispute the accuracy of information when they are supplied by one’s peers at different levels of relationship closeness and political agreement. We investigate four variants of these labels using a 2 (strong vs. weak tie strength) x 2 (high vs. low political agreement) between-subjects factorial design. We find that credibility disputes raised by one’s co-partisans (peers with similar political beliefs) significantly reduced belief in misinformation, irrespective of one’s relationship closeness with the peer. Our findings also reveal that in contrast to prior literature, a peer’s knowledgeability may be more potent than trustworthiness in causing belief change, and that trust can sometimes manifest even in the credibility judgement of distant peers, when perceived to have expertise or a fact-checking tendency. We further highlight the dual nature of these credibility labels, discussing scenarios in which disputes by hyper-partisan members of the opposite party can enforce belief in misinformation. We conclude by discussing how peer-supplied credibility disputes can benefit social media, especially echo chambers with high political homophily, where disputes by a co-partisan may be met with less resistance and persuade significantly reduced belief in fake news.

As misinformation increasingly proliferates on social media platforms, it has become crucial to explore how to best convey automated news credibility assessments to end-users, and foster trust in fact-checking AIs. In this paper, we investigate how model-agnostic, natural language explanations influence trust and reliance on a fact-checking AI. We construct explanations from four Conceptualisation Validations (CVs) - namely consensual, expert, internal (logical), and empirical - which are foundational units of evidence that humans utilise to validate and accept new information. Our results show that providing explanations significantly enhances trust in AI, even in a fact-checking context where influencing pre-existing beliefs is often challenging, with different CVs causing varying degrees of reliance. We find consensual explanations to be the least influential, with expert, internal, and empirical explanations exerting twice as much influence. However, we also find that users could not discern whether the AI directed them towards the truth, highlighting the dual nature of explanations to both guide and potentially mislead. Further, we uncover the presence of automation bias and aversion during collaborative fact-checking, indicating how users' previously established trust in AI can moderate their reliance on AI judgements. We also observe the manifestation of a 'boomerang'/backfire effect often seen in traditional corrections to misinformation, with individuals who perceive AI as biased or untrustworthy doubling down and reinforcing their existing (in)correct beliefs when challenged by the AI. We conclude by presenting nuanced insights into the dynamics of user behaviour during AI-based fact-checking, offering important lessons for social media platforms.