Sarah Rajtmajer’s research while affiliated with Pennsylvania State University and other places

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this work presents the findings from a university-level competition which aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. While significant advances have been made in misinformation detection, the focus remains largely on monolingual high-resource contexts, with low-resource languages often overlooked. This survey aims to bridge that gap by providing a comprehensive overview of the current research on low-resource language misinformation detection in both monolingual and multilingual settings. We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts. We also examine emerging approaches, such as language-agnostic models and multi-modal techniques, while emphasizing the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.

Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space.

Undergraduate curricula in informatics programs typically include several courses in mathematics. In my time teaching mathematics in IST, I have noticed continual and increasing calls from colleagues and administrators to prove to students the relevance of these courses through application-driven lesson plans and preferential treatment of topics with well-understood connection to today’s technologies. This chapter offers a counterpoint to these calls. I suggest that the treatment of foundational mathematical concepts as a means to well-defined and highly circumscribed ends perpetuates math anxiety and undermines flexibility of mathematical reasoning critical to highly dynamic IST careers. The examples I provide center around a course on discrete mathematics. However, the message is general.

Following change in Twitter's ownership and subsequent changes to content moderation policies, many in academia looked to move their discourse elsewhere and migration to Mastodon was pursued by some. Our study looks at the dynamics of this migration. Utilizing publicly available user account data, we track the posting activity of academics on Mastodon over a one year period. Our analyses reveal significant challenges sustaining user engagement on Mastodon due to its decentralized structure as well as competition from other platforms such as Bluesky and Threads. The movement lost momentum after an initial surge of enthusiasm as most users did not maintain their activity levels, and those who did faced lower levels of engagement compared to Twitter. Our findings highlight the challenges involved in transitioning professional communities to decentralized platforms, emphasizing the need for focusing on migrating social connections for long-term user engagement.

As social media has become a predominant mode of communication globally, the rise of abusive content threatens to undermine civil discourse. Recognizing the critical nature of this issue, a significant body of research has been dedicated to developing language models that can detect various types of online abuse, e.g., hate speech, cyberbullying. However, there exists a notable disconnect between platform policies, which often consider the author's intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent. This paper examines the role of intent in content moderation systems. We review state of the art detection models and benchmark training datasets for online abuse to assess their awareness and ability to capture intent. We propose strategic changes to the design and development of automated detection and moderation systems to improve alignment with ethical and policy conceptualizations of abuse.

Generative Artificial intelligence (GenAI) such as ChatGPT has elicited strong reactions from almost all stakeholders across the education system. Education-oriented and academic social media communities provide an important venue for these stakeholders to share experiences and exchange ideas about GenAI, which is constructive for developing human-centered policies. This study examines early user reactions to GenAI, consisting of 725 Reddit threads between 06/2022 and 05/2023. Through natural language processing (NLP) and content analysis, we observe an increasingly negative sentiment in the discussion and identify six main categories of student and faculty experiences of GenAI in education. These experiences reflect concerns about academic integrity and AI's negative impact on the value of traditional education. Our analysis also highlights an additional workload imposed by new technologies. Our findings suggest that dialogue between stakeholders in the education community is critical and can mitigate sources of tension between students and faculty.