Conference Agenda (All times are shown in Greenwich Mean Time (GMT) unless otherwise noted)

Negativity bias is the tendency to pay more attention and give more weight to negative information than positive information. This study explored how negativity bias affects information search, processing, and sensemaking when reading news articles on controversial topics. We conducted an eye-tracking experiment with 43 participants who sought and read positive and negative articles about the three-child policy debate. We measured their eye movements, cognitive load, attitude change, and sensemaking outcomes. We found that: (1) negativity bias occurs in both information search and information processing, and the outcomes of sensemaking also tend to show negative changes; (2) reading positive articles increase cognitive load more than reading negative articles; (3) gender and prior attitude have an influence on negativity bias; (4) people use different cognitive strategies when making sense of positive and negative information. This paper contributes to a better understanding of negativity bias in information seeking, processing, and sensemaking, which can help design news systems that adapt to readers' needs, and suggests people view information objectively.

Reaction video, a new form of online video that records users' instant reactions to a particular thing, has emerged on social media in recent years. Its unique content composition and hedonic and emotional characteristics make the information cues that influence the affective generation in danmaku comments quite different from those in traditional videos. To explore the information cues of danmaku comments to stimulate users’ affective generation in reaction videos, we conduct thematic coding using the content analysis method by selecting the danmaku resources, video content, and reactors’ responses from 11 popular videos in different categories as samples to identify information cues that influence user affects in danmaku comments. The preliminary findings show that there are three main types of information cues in the reaction videos: the content of the original video, reactors’ reaction and danmaku comments, which could trigger danmaku users’ affect in reaction videos from the perspective of orientation type, parasocial interaction, and peer influence, respectively.

This paper applies the concept of ‘lived experiences’ to understand people’s subjective and everyday encounters with automated systems. We reflect on how qualitative longitudinal research methods are useful for capturing the affective and emotional dimensions of these experiences; these flexible methods also allow for iterative changes that can react to new findings and participant feedback. Using our empirical study on Universal Credit (UC), the UK’s largest social security payment, we demonstrate how studying lived experiences via qualitative longitudinal research helps us reflect on both the topic of the research and our position as researchers in relation to study participants. We argue that the lived experience framework is extremely valuable for understanding the consequences of automated decisions for users of these systems and to redress the uneven power dynamics of representing the voices of those sharing these encounters.

Geospatial technologies collect, analyze, and produce information about earth, humans, and objects through a convergence of geographic information systems, remote sensors, and global positioning systems. A microanalysis of Google’s U.S. Patent 9,420,426 Inferring a current location based on a user location history (Duleba et al, 2016) reveals how geospatial innovation employs artificial intelligence (AI) to train computer-vision models, infer, and impute geospatial data. The technical disclosures in patents offer a view within black-boxed digital technologies to examine potential privacy implications of datafied citizens in a networked society. In patented geospatial innovation, user agency is subverted through AI and anonymous knowledge production.

Presently, the Fourth Amendment does not adequately protect citizens in a networked society. Data privacy legal cases are interpreted through a lens of inescapability (Tokson, 2020), which assumes perpetual agency to consent to sharing data. In short, agency-centered privacy models are insufficient where AI can anonymously produce knowledge about an individual. Privacy implications are exemplified in geofence warrants—an investigative technique that searches location history to identify suspects in a geofenced region in the absence of evidence. This analysis demonstrates that digital privacy rights must expand to datafication models (Mai, 2016) centered on knowledge production.

As part of a larger project, this paper reports on preliminary findings of a study exploring use cases of ChatGPT and associated behaviors and experiences among users of an online forum. Posts on a ChatGPT-related forum on Reddit (n = 452) were analyzed using qualitative content analysis. This paper reports on themes relevant to this study, including the types of tasks for which users used ChatGPT, user experiences, and perceived impacts of ChatGPT. ChatGPT was often used to facilitate various writing tasks (e.g., writing an essay), academic tasks (e.g., finding scientific references for a research paper), everyday tasks (e.g., creating a meal plan), and conversational purposes (e.g., having a simulated conversation about a past event). Users expressed positive (e.g., excited, amazed) and negative (e.g., fooled, concerned) feelings toward the technology. They raised various issues and problems with ChatGPT at the content (e.g., inaccuracy, incompletes) and system (e.g., unavailability, instability) levels. Users discussed the perceived impacts of ChatGPT on individuals (e.g., unemployment) and society (e.g., AI divide). Study findings can inform the design of policies and guidelines for mitigating AI problems and promoting the effective and ethical use of emerging AI technologies.

The recognition of data literacy as an important learning outcome in higher education has led to a call for assessment tools to measure students’ data literacy. Although there has been a growing interest in the conceptualization of data literacy, the literature lacks a measuring instrument to operationalize data literacy. This study developed and validated a three-factor, 24-item data literacy assessment tool using a sample of 573 students from four community colleges in the United States. The data literacy scale developed in this study has respectable reliability and construct validity, supported by a concept analysis of data literacy, a comparative analysis of data literacy competency frameworks, an expert panel review, exploratory factor analysis, and confirmatory factor analysis.