Digital storytelling has been widely explored in mathematics education to situate abstract problems within meaningful narrative contexts. However, most existing systems present stories through static media such as text and images, offering limited opportunities for active engagement. In this paper, we present MathsTale, a storytelling web application we developed for teaching children abstract mathematical problems through interactive multimodal stories. The system adopts a multi-agent architecture and leverages generative artificial intelligence to produce narrative text, images, and executable code for interactive hands-on activities. To explore its potential, we conducted a preliminary user study involving children and teachers using semi-structured interviews. Findings suggest that interactive multimodal stories can enhance children’s understanding, enjoyment, and engagement in learning mathematical problems.

While memes enhance social interaction on social media, they can raise privacy and security concerns. Despite research on overtly toxic or unsafe memes, little attention has been given to users’ experiences with seemingly safe memes and how contextual factors trigger privacy concerns. This study explores users’ comfort levels, influencing factors, underlying reasons for discomfort, and unmet needs when engaging with such memes. We first collected and analyzed 2,317 Reddit posts describing real-world meme experiences, then conducted an online survey with 324 participants to evaluate comfort across curated scenarios. Our findings reveal that perceived-safe memes can cause harm when shared inappropriately, with comfort shaped by content and context. Privacy concerns intensify with deeper involvement, strangers, and sensitive meme topics. We identified users’ desire for consent and control in meme interactions. Based on our study, we make recommendations for users, developers of social media platforms and policymakers to address meme-related privacy and contextual concerns.

When taking photos in a crowd, unintended individuals, such as bystanders, are often captured alongside the main subject(s). In an effort to protect bystanders' privacy, existing methods have been developed to automatically detect bystanders. However, inconsistent definitions of who qualifies as a bystander limit their effectiveness. To better understand bystanders' perceptions, we conducted an online survey with 486 participants, analyzing their responses to 864 image-based scenarios and their comfort with sharing these images online. Our results revealed no significant correlation between comfort with public photo sharing and bystander status. We identified limitations in current bystander detection methodologies, as they often fail to recognize bystanders who are not clearly in the background, hence missing individuals with privacy concerns. Moreover, comfort with public sharing varied significantly depending on the image context. Our findings highlight the importance of considering the context of captured images to address privacy concerns in image sharing.

Online users often post facial images of themselves and other people on online social networks (OSNs) and other Web 2.0 platforms, which can lead to potential privacy leakage of people whose faces are included in such images. There is limited research on understanding face privacy in social media while considering user behavior. It is crucial to consider privacy of subjects and bystanders separately. This calls for the development of privacy-aware face detection classifiers that can distinguish between subjects and bystanders automatically. This paper introduces such a classifier trained on face-based features, which outperforms the two state-of-the-art methods with a significant margin (by 13.1% and 3.1% for OSN images, and by 17.9% and 5.9% for non-OSN images). We developed a semi-automated framework for conducting a large-scale analysis of the face privacy problem by using our novel bystander-subject classifier. We collected 27,800 images, each including at least one face, shared by 6,423 Twitter users. We then applied our framework to analyze this dataset thoroughly. Our analysis reveals eight key findings of different aspects of Twitter users' real-world behaviors on face privacy, and we provide quantitative and qualitative results to better explain these findings. We share the practical implications of our study to empower online platforms and users in addressing the face privacy problem efficiently.

Social media users generate vast amounts of content that may contain sensitive information, posing threats to online and real-world privacy and security. Existing automated classifiers focus primarily on preserving privacy in original posts while neglecting the potential privacy risks in comments. This article addresses this gap by presenting a comprehensive study on privacy leaks in textual comments. We curate a real-world dataset of 1250 tweet-comment interactions from Twitter and also introduce features to detect privacy leaks in comments. Using these features, we train various classifiers that achieve an average F-score of 0.86 on the Twitter dataset. We then use our methods to detect privacy leaks on another social media platform, namely Reddit, and we get an average F-score of 0.90, which demonstrates the adaptability of our methods. This research shows the significance of addressing privacy leaks in comments. We also show how our approach could work across two different social media platforms.