Evaluating the relationship between user interaction and financial visual analysis

It has been widely accepted that interactive visualization techniques enable users to more effectively form hypotheses and identify areas for more detailed investigation. There have been numerous empirical user studies testing the effectiveness of specific visual analytical tools. However, there has been limited effort in connecting a user’s interaction with his reasoning for the purpose of extracting the relationship between the two. In this paper, we present an approach for capturing and analyzing user interactions in a financial visual analytical tool and describe an exploratory user study that examines these interaction strategies. To achieve this goal, we created two visual tools to analyze raw interaction data captured during the user session. The results of this study demonstrate one possible strategy for understanding the relationship between interaction and reasoning both operationally and strategically. Index Terms: H.5.2 [Information Interfaces And Presentatio

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Participatory Sensing for Community Building

Abstract In this research, we explore the viability of using participatory sensing as a means to enhance a sense of community. To accomplish this, we are developing and deploying a suite of participatory sensing applications, where users explicitly report on the state of their environment, such as the location of the bus. In doing so, community members become reliant on each other for valuable information about the community. By better understanding the relationship between participatory sensing and community, we inform the design and research of similar participatory sensing, or crowd-sourced sensing applications

+Your Circles: Sharing Behavior on Google+

Users are sharing and consuming enormous amounts of information through online social network interaction every day. Yet, many users struggle to control what they share to their overlapping social spheres. Google+ introduces circles, a mechanism that enables users to group friends and use these groups to control their social network feeds and posts. We present the results of a qualitative interview study on the sharing perceptions and behavior of 27 Google+ users. These results indicate that many users have a clear understanding of circles, using them to target information to those most interested in it. Yet, despite these positive perceptions, there is only moderate use of circles to control information flow. We explore reasons and risks associated with these behaviors and provide insight on the impact and open questions of this privacy mechanism

"I don\u27t own the data": End User Perceptions of Smart Home Device Data Practices and Risks

Smart homes are more connected than ever before, with a variety of commercial devices available. The use of these devices introduces new security and privacy risks in the home, and needs for helping users to understand and mitigate those risks. However, we still know little about how everyday users understand the data practices of smart home devices, and their concerns and behaviors regarding those practices. To bridge this gap, we conducted a semi-structured interview study with 23 smart home users to explore what people think about smart home device data collection, sharing, and usage practices; how that knowledge affects their perceived risks of security and privacy; and the actions they take to resolve those risks. Our results reveal that while people are uncertain about manufacturers\u27 data practices, users\u27 knowledge of their smart home does not strongly influence their threat models and protection behaviors. Instead, users\u27 perceptions and concerns are largely shaped by their experiences in other computing contexts and with organizations. Based on our findings, we provide several recommendations for policymakers, researchers and designers to contribute to users\u27 risk awareness and security and privacy practices in the smart home

The impact of social navigation on privacy policy configuration

Social navigation is a promising approach to help users make better privacy and security decisions using community knowledge and expertise. Social navigation has recently been applied to several privacy and security systems such as peer-topeer file sharing, cookie management, and firewalls. However, little empirical evaluation of social navigation cues has been performed in security or privacy systems to understand the real impact such knowledge has on user behavior and the resulting policies. In this paper, we explore the application of social navigation to access control policy configuration using an empirical between subjects study. Our results indicate that community information does impact user behavior, but only when the visual representation of the cue is sufficiently strong

Mapping user preference to privacy default settings

Copyright © 2015 ACM. Managing the privacy of online information can be a complex task often involving the configuration of a variety of settings. For example, Facebook users determine which audiences have access to their profile information and posts, how friends can interact with them through tagging, and how others can search for them-and many more privacy tasks. In most cases, the default privacy settings are permissive and appear to be designed to promote information sharing rather than privacy. Managing privacy online can be complex and often users do not change defaults or use granular privacy settings. In this article, we investigate whether default privacy settings on social network sites could be more customized to the preferences of users. We survey users\u27 privacy attitudes and sharing preferences for common SNS profile items. From these data, we explore using audience characterizations of profile items to quantify fit scores that indicate how well default privacy settings represent user privacy preferences. We then explore the fit of various schemes, including examining whether privacy attitude segmentation can be used to improve default settings. Our results suggest that using audience characterizations from community data to create default privacy settings can better match users\u27 desired privacy settings