A New Social Order: Mechanisms for Social Network Site Boundary Regulation

Social Network Site (SNS) use has become ubiquitous, with hundreds of millions of users sharing and interacting online. Yet,constant, unbounded sharing and interacting with others can cause social crowding and emotional harm (Altman 1975). Weexplore interpersonal boundary regulation on Social Network Sites to understand these tradeoffs and examine how toimprove the social experiences of users. In this paper, we present a taxonomy of five categories of interpersonal boundarymechanisms relevant to SNSs and the specific interface controls that sites provide for managing these boundaries. Wequalitatively research how SNS users employ these mechanisms and the boundary issues that arise while interacting onlinewith others. These results present a first step towards a model of SNS interpersonal boundary regulation

Technology Overload: Gender-based Perceptions of Knowledge Worker Performance

Gender studies show numerous differences between genders in regard to technology, and emphasize that women areunderrepresented in IT-related academic programs and careers. Because technology is so prevalent in our workforce,it is important to study how technology usage affects white-collared working women. We explore the relationshipbetween three dimensions of technology overload and knowledge worker job performance (stratified by gender)through a quantitative analysis. Our results show that female knowledge workers perceive a more significant andnegative relationship than men between technology overload and job performance even when they do not relyheavily on technology in the workplace. Addressing technology overload may thus positively impact women’scareer development

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

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