Justin Cranshaw | Carnegie Mellon University (original) (raw)

Papers by Justin Cranshaw

Proceedings of the 2nd Acm Sigkdd International Workshop, Aug 11, 2013

In this work we explore the use of incidentally generated social network data for the folksonomic... more In this work we explore the use of incidentally generated social network data for the folksonomic characterization of cities by the types of amenities located within them. Using data collected about venue categories in various cities, we examine the effect of different granularities of spatial aggregation and data normalization when representing a city as a collection of its venues. We introduce three vector-based representations of a city, where aggregations of the venue categories are done within a grid structure, within the city's municipal neighborhoods, and across the city as a whole. We apply our methods to a novel dataset consisting of Foursquare venue data from 17 cities across the United States, totaling over 1 million venues. Our preliminary investigation demonstrates that different assumptions in the urban perception could lead to qualitative, yet distinctive, variations in the induced city description and categorization.

Proceedings of the 2013 Acm International Joint Conference on Pervasive and Ubiquitous Computing, Sep 8, 2013

ABSTRACT Location sharing is a popular feature of online social networks, but challenges remain i... more ABSTRACT Location sharing is a popular feature of online social networks, but challenges remain in the effective presentation of privacy choices to users, whose location sharing preferences are complex and diverse. One proposed approach for capturing these nuances builds on the observation that key attributes of users' location sharing preferences can be represented by a small number of privacy profiles, which can provide a basis for configuring individual preferences. However, the impact of this approach on how users view their privacy is relatively unknown. We present a study evaluating the impact of this approach on users' location sharing preferences and their satisfaction with the decisions made by their resulting settings. The results suggest that this approach can influence users to share significantly more without a substantial difference in comfort. This further suggests that the provision of profiles for privacy settings must be carefully considered, as they can substantially alter sharing behavior.

Online Behavioral Advertising (OBA) is the practice of tailoring ads based on an individual's onl... more Online Behavioral Advertising (OBA) is the practice of tailoring ads based on an individual's online activities. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures while performing an online task. We tested icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options; these were based on prior research or drawn from those currently in use. The icons, taglines, and landing pages fell short both in terms of notifying participants about OBA and clearly informing participants about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. The majority of participants mistakenly believed that ads would pop up if they clicked on disclosure icons and taglines, and more participants incorrectly thought that clicking the disclosures would let them purchase their own advertisements than correctly understood that they could then opt out of OBA. "AdChoices," the tagline most commonly used by online advertisers, was particularly ineffective at communicating notice and choice. 45% of participants who saw "AdChoices" believed that it was intended to sell advertising space, while only 27% believed it was an avenue to stop tailored ads. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We discuss challenges in crafting disclosures, and we provide suggestions for improvement.

With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applica... more With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.

Studying the social dynamics of a city on a large scale has traditionally been a challenging ende... more Studying the social dynamics of a city on a large scale has traditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a partial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these areas. To address both difficulties, we introduce a clustering model and research methodology for studying the structure and composition of a city based on the social media its residents generate. We use data from approximately 18 million check-ins collected from users of a location-based online social network. The resulting clusters, which we call Livehoods, are representations of the dynamic urban areas that comprise the city. We take an interdisciplinary approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perce...

Locaccino is a location sharing application designed to empower users to effectively control thei... more Locaccino is a location sharing application designed to empower users to effectively control their privacy. It has been piloted by close to 2000 users and has been used by researchers as an experimental platform for conducting research on location-based social networks. Featured technologies include expressive privacy rule creation, detailed feedback mechanisms that help users understand their privacy, algorithms for analyzing privacy preferences, and clients for mobile computers and smartphone devices. In addition, variations of Locaccino are also being piloted as part of research on user-controllable policy learning, learning usable privacy personas and reconciling expressiveness and user burden. The purpose of this demo is to introduce participants to the features of Locaccino, so that they can try out the Locaccino smartphone and laptop applications on their own devices, locate their friends and colleagues, and set rich privacy policies for sharing their location.

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing - UrbComp '13, 2013

Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11, 2011

Although science is becoming increasingly collaborative, there are remarkably few success stories... more Although science is becoming increasingly collaborative, there are remarkably few success stories of online collaborations between professional scientists that actually result in real discoveries. A notable exception is the Polymath Project, a group of mathematicians who collaborate online to solve open mathematics problems. We provide an in-depth descriptive history of Polymath, using data analysis and visualization to elucidate the principles that led to its success, and the difficulties that must be addressed before the project can be scaled up. We find that although a small percentage of users created most of the content, almost all users nevertheless contributed some content that was highly influential to the task at hand. We also find that leadership played an important role in the success of the project. Based on our analysis, we present a set of design suggestions for how future collaborative mathematics sites can encourage and foster newcomer participation.

Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing - UrbComp '13, 2013

ABSTRACT

Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI '14, 2014

Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11, 2011

There have been many location sharing systems developed over the past two decades, and only recen... more There have been many location sharing systems developed over the past two decades, and only recently have they started to be adopted by consumers. In this paper, we present the results of three studies focusing on the foursquare check-in system. We conducted interviews and two surveys to understand, both qualitatively and quantitatively, how and why people use location sharing applications, as well as how they manage their privacy. We also document surprising uses of foursquare, and discuss implications for design of mobile social services.

Proceedings of the 2012 ACM workshop on Privacy in the electronic society - WPES '12, 2012

Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's onlin... more Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices," the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We discuss challenges in crafting disclosures and provide suggestions for improvement.

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing - UbiComp '13, 2013

Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Ubicomp '10, 2010

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI '13, 2013

We present the results of an online survey of 1,221 Twitter users, comparing messages individuals... more We present the results of an online survey of 1,221 Twitter users, comparing messages individuals regretted either saying during in-person conversations or posting on Twitter. Participants generally reported similar types of regrets in person and on Twitter. In particular, they often regretted messages that were critical of others. However, regretted messages that were cathartic/expressive or revealed too much information were reported at a higher rate for Twitter. Regretted messages on Twitter also reached broader audiences. In addition, we found that participants who posted on Twitter became aware of, and tried to repair, regret more slowly than those reporting in-person regrets. From this comparison of Twitter and in-person regrets, we provide preliminary ideas for tools to help Twitter users avoid and cope with regret.

Proceedings of the 12th ACM international conference on Ubiquitous computing - Ubicomp '10, 2010

The rapid adoption of location tracking and mobile social networking technologies raises signific... more The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of people's location sharing privacy preferences remains very limited, including how these preferences are impacted by the type of location tracking device or the nature of the locations visited. To address this gap, we deployed Locaccino, a mobile location sharing system, in a four week long field study, where we examined the behavior of study participants (n=28) who shared their location with their acquaintances (n = 373.) Our results show that users appear more comfortable sharing their presence at locations visited by a large and diverse set of people. Our study also indicates that people who visit a wider number of places tend to also be the subject of a greater number of requests for their locations. Over time these same people tend to also evolve more sophisticated privacy preferences, reflected by an increase in time-and location-based restrictions. We conclude by discussing the implications our findings.

... geographic regions. To meet this need, we gathered data from foursquare, a popularlocation-ba... more ... geographic regions. To meet this need, we gathered data from foursquare, a popularlocation-based social network that allows users share their location with their friends by checking-in to the places that they visit. When users ...

Association for the Advancement of Artificial Intelligence, May 20, 2012