Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

In this paper, we use a critical informatics approach to investigate institutional disinformation around 2022-23 labor organizing at three higher education institutions: Rutgers University, Temple University, and the University of California. Our contribution to the study of disinformation is the application of critical informatics perspectives that attend to structural power dynamics of disinformation within an institutional context. Understanding the political economic dynamics of disinformation and how these dynamics function can help more solidly contextualize and clarify how and why disinformation exists across different information systems, so that solutions to this social and institutional problem of disinformation can be more appropriately addressed and understood. The study describes disinformation tactics employed by institutional leaders during higher education labor organizing including: non-performative commitments to “community”, legal threats, misleading victories, and elite capture.

Worldwide events, including the war in Ukraine and the conflict in Gaza, have highlighted the effective use of social media to voice concerns about certain issues and create awareness. However, the negative effect of media is largely seen as a fertile ground for spreading misinformation. Misinformation is not a new concept. However, the spread by which information is spread and magnified has exponentially increased with the widespread use of media. Efforts to regulate media and control the widespread spread of misinformation are still lacking, and the final decisions are left to social media companies to self-regulate and supervise content.Given complexity associated with identifying misinformation using automated methods due to the subjective nature of information, it is more practical to focus on social noise as factor in spreading and magnifying misinformation on media.In this paper, we investigate methods of quantifying social noise using entropy and topic modeling. Results have shown a direct relationship between social noise and social entropy as a measure of uncertainty. Results have shown that social noise and uncertainty decrease with the use of URLs and rich content. This approach does not eliminate social noise and misinformation.identification and reduction of social noise can minimize the negative effect misinformation.

This paper discusses the ethical implications of citing retracted biomedical literature, particularly in the context of spreading misinformation within scholarly discourse. It examines the responsibility of scientists to combat disinformation and uphold ethical standards in their research practices. To guide our discussion, we studied citations of the most often cited retracted works containing disinformation in Web of Science. Our findings confirm prior research and demonstrate that most citations to retracted papers reference them to bolster arguments or use methodologies without acknowledging their status. We conclude by interpreting our findings through a framework of moral obligation and argue that scientists have a special responsibility to combat disinformation, which may harm others. Cognitive authorities, namely scientists, citing invalid publications may perpetuate false beliefs and erode trust in scientific integrity.

ChatGPT has shown promise in assisting qualitative researchers with coding. Previous efforts have primarily focused on datasets derived from interviews and observations, leaving document analysis, another crucial data source, relatively unexplored. In this project, we address the rapidly emerging topic of disinformation regulatory policy as a pilot to investigate ChatGPT's potential for document analysis. We adapt our existing qualitative research framework, which identifies five key components of disinformation policy: context, actors, issue, instrument, and channel, to sketch out policy documents. We then designed a two-stage experiment employing a multi-layer workflow using a dataset with highly relevant policy documents from US federal government departments. Through iteratively developing and refining six different prompt strategies, we identified an effective few-shot learning strategy that achieved 72.0% accuracy and a 70.8% F-score with the optimal prompt. Our experimental process and outcomes explore the feasibility of using ChatGPT to support manual coding for policy documents and suggest a coding approach for conducting explicit document analysis through an interactive process between researchers and ChatGPT. Furthermore, our results initiate a wider debate on how to integrate human logic with ChatGPT logic, along with the evolving relationship between researchers and AI tools.