Alireza Karduni

University of North Carolina at Charlotte | UNC Charlotte · Department of Computer Science

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critic...

Images are an indispensable part of the news we consume. Highly emotional images from mainstream and misinformation sources can greatly influence our trust in the news. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact o...

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and att...

The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reas...

There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar w...

For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term los...

There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar w...

For many households, investing for retirement is one of the most significant decisions and is fraught with uncertainty. In a classic study in behavioral economics, Benartzi and Thaler (1999) found evidence using bar charts that investors exhibit myopic loss aversion in retirement decisions: Investors overly focus on the potential for short-term los...

Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and...

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication when judging bivariate visual...

Cognitive biases are systematic errors in judgment. Researchers in data visualizations have explored whether cognitive biases transfer to decision-making tasks with interactive data visualizations. At the same time, cognitive scientists have reinterpreted cognitive biases as the product of resource-rational strategies under finite time and computat...

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication when judging bivariate visual...

This paper presents an interactive AI system to enable academic advisors and program leadership to understand the patterns of behavior related to student success and risk using data collected from institutional databases. We have worked closely with advisors in our development of an innovative temporal model of student data, unsupervised k-means al...

This paper presents an interactive AI system to enable academic advisors and program leadership to understand the patterns of behavior related to student success and risk using data collected from institutional databases. We have worked closely with advisors in our development of an innovative temporal model of student data, unsupervised k-means al...

Humans quite frequently interact with conversational agents. The rapid advancement in generative language modeling through neural networks has helped advance the creation of intelligent conversational agents. Researchers typically evaluate the output of their models through crowdsourced judgments, but there are no established best practices for con...

Black Lives Matter, like many modern movements in the age of information, makes significant use of social media as well as public space to demand justice. In this article, we study the protests in response to the shooting of Keith Lamont Scott by police in Charlotte, North Carolina, on September 2016. Our goal is to measure the significance of urba...

We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped ba...

Cognitive biases are systematic errors in judgment due to an over‐reliance on rule‐of‐thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision‐m...

Social Media usage is becoming more and more interwoven with activities in urban space. Understanding the flows of information in cities can open new doors for us to understand how urban space relates to human behavior. In this chapter, we introduce a method to extrapolate flow of geolocated social media data for a street network. We then apply thi...

We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation...

The prevalence of new technologies and social media has amplified the effects of misinformation on our societies. Thus, it is necessary to create computational tools to mitigate their effects effectively. This study aims to provide a critical overview of computational approaches concerned with combating misinformation. To this aim, I offer an overv...

Architects and designers have recently become interested in the use of “big data”. The most common paradigm guiding this work is the optimization of a limited number of factors, e.g. façade designs maximizing light distribution. For most design problems, however, such optimization is oversimplified and reductive; the goal of design is the discovery...

We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. On the one hand, social media platforms empower individuals and organizations by democratizing the sharing of information. On the other hand, even well-informed and experienced social media users are vulnerable to misinformation. To address...

We describe a novel study of decision-making processes around misinformation on social media. Using a custom-built visual analytic system, we presented users with news content from social media accounts from a variety of news outlets, including outlets engaged in distributing misinformation. We conducted controlled experiments to study decision-mak...

Cognitive biases have been shown to lead to faulty decision-making. Recent research has demonstrated that the effect of cognitive biases, anchoring bias in particular, transfers to information visualization and visual analytics. However, it is still unclear how users of visual interfaces can be anchored and the impact of anchoring on user performan...

Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that...

Urban Activity Explorer is a new prototype for a planning support system that uses visual analytics to understand mobile social media data. Mobile social media data are growing at an astounding rate and have been studied from a variety of perspectives. Our system consists of linked visualizations that include temporal , spatial and topical data, an...

Understanding people’s behavior is fundamental to many planning professions (including transportation, community development, economic development, and urban design) that rely on data about frequently traveled routes, places, and social and cultural practices. Based on the results of a practitioner survey, the authors designed Urban Space Explorer,...

The study of geographical systems as graphs, and networks has gained significant momentum in the academic literature as these systems possess measurable and relevant network properties. Crowd-based sources of data such as OpenStreetMaps (OSM) have created a wealth of worldwide geographic information including on transportation systems (e.g., road n...

The objective of this study is to investigate the resilience of roads networks to extreme events using a GIS and network science approach. Using the specific case study of Chicago, three extreme event scenarios were simulated: (1) extreme flooding, (2) random zonal disturbance, and (3) central targeted disturbance. To measure their impacts and as a...