In recent years, research into gender differences has established that individual differences in how people problem-solve often cluster by gender. Research also shows that these differences have direct implications for software that aims to support users' problem-solving activities, and that much of this software is more supportive of problem-solving processes favored (statistically) more by males than by females. However, there is almost no work considering how software practitionerssuch as User Experience (UX) professionals or software developers-can find gender-inclusiveness issues like these in their software. To address this gap, we devised the GenderMag method for evaluating problem-solving software from a genderinclusiveness perspective. The method includes a set of faceted personas that bring five facets of gender difference research to life, and embeds use of the personas into a concrete process through a gender-specialized Cognitive Walkthrough. Our empirical results show that a variety of practitioners who design software-without needing any background in gender research-were able to use the GenderMag method to find gender-inclusiveness issues in problem-solving software. Our results also show that the issues the practitioners found were real and fixable. This work is the first systematic method to find gender-inclusiveness issues in software, so that practitioners can design and produce problem-solving software that is more usable by everyone. Categories and Subject DescriptorsH.5.2. Information interfaces and presentation (e.g., HCI): User Interfaces; H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous. Additional KeywordsGender; gender HCI; diversity; problem-solving software; GenderMag Research Highlights We discuss five facets of prior gender research with ties to males' and females' usage of problem-solving software.  We present GenderMag, the first systematic method to evaluate gender-inclusiveness issues in problem-solving software.  We show how GenderMag draws upon and encapsulates these five facets.  We present three qualitative empirical studies that were used to inform and to validate various aspects of GenderMag, and show the kinds of issues that participants found and how gender of the evaluator interacted with usage of the method.

Social media connects youth to peers who share shared experiences and support; however, urban gang-involved youth navigate ‘the digital street’ following a script that may incite violence. Urban gang-involved youth use SNS to brag and insult and make threats a concept known as Internet banging. Recent research suggests Internet banging has resulted in serious injury and homicide. We argue violence may be disseminated in Chicago through social media platforms like Twitter. We examine the Twitter communications of one known female gang member, Gakirah Barnes, during a two week window in which her friend was killed and then weeks later, she was also killed. We explore how street culture is translated online through the conventions of Twitter. We find that a salient script of reciprocal violence within a local network is written online in real time. Those writing this script anticipate, direct, historicize, and mourn neighborhood violence.

This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools, in particular convolutional neural networks. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.

There is a dearth of research investigating youths’ experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. First, we conducted a close textual read on a sample of tweets by Gakirah Barnes, a gang-involved teenaged girl in Chicago, and members of her Twitter network, over a 19-day period in 2014 during which 2 significant deaths occurred: that of Raason “Lil B” Shaw and Gakirah’s own death. We leverage the grief literature to understand the way Gakirah and her peers express thoughts, feelings, and behaviors at the time of these deaths. We also present and explain the rich and complex style of online communication among gang-involved youth, one that has been overlooked in prior research. Next, we overview the natural language processing output for expressions of loss and grief in our data set based on qualitative findings and present an error analysis on its output for grief. We conclude with a call for interdisciplinary research that analyzes online and offline behaviors to help understand physical and emotional violence and other problematic behaviors prevalent among marginalized communities.

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Heart failure is a leading cause of death in the United States, with around 5 million Americans currently suffering from congestive heart failure. The WANDA B. wireless health technology leverages sensor technology and wireless communication to monitor heart failure patient activity and to provide tailored guidance. Patients who have cardiovascular system disorders can measure their weight, blood pressure, activity levels, and other vital signs in a real-time automated fashion. The system was developed in conjunction with the UCLA Nursing School and the UCLA Wireless Health Institute for use on actual patients. It is currently in use with real patients in a clinical trial.

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While the Internet and social media help keep today's youth better connected to their friends, family, and community, the same media are also the form of expression for an array of harmful social behaviors, such as cyberbullying and cyber-harassment. In this paper we present work in progress to develop intelligent interfaces to social media that use commonsense knowledge bases and automated narrative analyses of text communications between users to trigger selective interventions and prevent negative outcomes. While other approaches seek merely to classify the overall topic of the text, we try to match stories to finer-grained "scripts" that represent stereotypical events and actions. For example, many bullying stories can be matched to a "revenge" script that describes trying to harm someone who has harmed you. These tools have been implemented in an initial prototype system and tested on a database of real stories of cyberbullying collected on MTV's "A Thin Line" Web site.