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Nitesh Goyal

Nitesh Goyal

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    Preview abstract Online harassment is a major societal challenge that impacts multiple communities. Some members of community, like female journalists and activists, bear significantly higher impacts since their profession requires easy accessibility, transparency about their identity, and involves highlighting stories of injustice. Through a multi-phased qualitative research study involving a focus group and interviews with 27 female journalists and activists, we mapped the journey of a target who goes through harassment. We introduce PMCR framework, as a way to focus on needs for Prevention, Monitoring, Crisis and Recovery. We focused on Crisis and Recovery, and designed a tool to satisfy a target’s needs related to documenting evidence of harassment during the crisis and creating reports that could be shared with support networks for recovery. Finally, we discuss users’ feedback to this tool, highlighting needs for targets as they face the burden and offer recommendations to future designers and scholars on how to develop tools that can help targets manage their harassment. View details
    Preview abstract Machine learning models are commonly used to detect toxicity in online conversations. These models are trained on datasets annotated by human raters. We explore how raters' self-described identities impact how they annotate toxicity in online comments. We first define the concept of specialized rater pools: rater pools formed based on raters' self-described identities, rather than at random. We formed three such rater pools for this study--specialized rater pools of raters from the U.S. who identify as African American, LGBTQ, and those who identify as neither. Each of these rater pools annotated the same set of comments, which contains many references to these identity groups. We found that rater identity is a statistically significant factor in how raters will annotate toxicity for identity-related annotations. Using preliminary content analysis, we examined the comments with the most disagreement between rater pools and found nuanced differences in the toxicity annotations. Next, we trained models on the annotations from each of the different rater pools, and compared the scores of these models on comments from several test sets. Finally, we discuss how using raters that self-identify with the subjects of comments can create more inclusive machine learning models, and provide more nuanced ratings than those by random raters. View details
    Capturing Covertly Toxic Speech via Crowdsourcing
    Alyssa Whitlock Lees
    Daniel Borkan
    Ian Kivlichan
    Jorge M Nario
    HCI, https://sites.google.com/corp/view/hciandnlp/home (2021) (to appear)
    Preview abstract We study the task of extracting covert or veiled toxicity labels from user comments. Prior research has highlighted the difficulty in creating language models that recognize nuanced toxicity such as microaggressions. Our investigations further underscore the difficulty in parsing such labels reliably from raters via crowdsourcing. We introduce an initial dataset, COVERTTOXICITY, which aims to identify such comments from a refined rater template, with rater associated categories. Finally, we fine-tune a comment-domain BERT model to classify covertly offensive comments and compare against existing baselines. View details
    Designing for Mobile Experience Beyond the Native Ad Click: Exploring Landing Page Presentation Style & Media Usage
    Marc Bron
    Mounia Lalmas
    Andrew Haines
    Henriette Cramer
    Journal of the Association for Information Science and Technology (2018)
    Preview abstract Many free mobile applications are supported by advertising. Ads can greatly affect user perceptions and behavior. In mobile apps, ads often follow a “native” format: they are designed to conform in both format and style to the actual content and context of the application. Clicking on the ad leads users to a second destination, outside of the hosting app, where the unified experience provided by native ads within the app is not necessarily reflected by the landing page the user arrives at. Little is known about whether and how this type of mobile ads is impacting user experience. In this paper, we use both quantitative and qualitative methods to study the impact of two design decisions for the landing page of a native ad on the user experience: (i) native ad style (following the style of the application) versus a non-native ad style; and (ii) pages with multimedia versus static pages. We found consider-able variability in terms of user experience with mobile ad landing pages when varying presentation style and multimedia usage, especially interaction between presence of video and ad style (native or non-native). W e also discuss insights and recommendations for improving the user experience with mobile native ads. View details
    Intelligent Interruption Management using Electro Dermal Activity based Physiological Sensor for Collaborative Sensemaking
    Susan R. Fussell
    Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 1 (2017), 52:1-52:21
    Effects of Sensemaking Translucence on Distributed Collaborative Analysis
    Susan R. Fussell
    Effects of Sensemaking Translucence on Distributed Collaborative Analysis (2016)
    RAMPARTS: Supporting Sensemaking with Spatially-Aware Mobile Interactions
    Paweł Wozniak
    Przemysław Kucharski
    Lars Lischke
    Sven Mayer
    Morten Fjeld
    Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
    Designing for Collaborative Sensemaking: Leveraging Human Cognition For Experts & non-Experts
    AAAI HCOMP (2015)
    Designing for Collaborative Sensemaking: Leveraging Human Cognition For Complex Tasks
    Susan R. Fussell
    ArXiv (2015)
    Effects of implicit sharing in collaborative analysis
    Gilly Leshed
    Dan Cosley
    Susan R. Fussell
    Effects of Implicit Sharing in Collaborative Analysis, ACM (2014)
    Leveraging partner's insights for distributed collaborative sensemaking
    Gilly Leshed
    Dan Cosley
    Leveraging Partner's Insights for Distributed Collaborative Sensemaking (2013)
    Effects of visualization and note-taking on sensemaking and analysis
    Gilly leshed
    Dan Cosley
    Effects of Visualization and Note-taking on Sensemaking and Analysis (2013)
    Cultural differences across governmental website design
    William Miner
    Nikhil Nawathe
    Cultural Differences Across Governmental Website Design, ACM (2012)
    Massively distributed authorship of academic papers
    Bill Tomlinson
    Joel Ross
    Paul Andre
    Eric Baumer
    Donald Patterson
    Joseph Corneli
    Martin Mahaux
    Syavash Nobarany
    Marco Lazzari
    Birgit Penzenstadler
    Andrew Torrance
    David Callele
    Gary Olson
    Marcus Stünder
    Fabio Romancini Palamedi
    Albert Ali Salah
    Eric Morrill
    Xavier Franch
    Florian Floyd Mueller
    Joseph'Jofish' Kaye
    Rebecca W Black
    Marisa L Cohn
    Patrick C Shih
    Johanna Brewer
    Pirjo Näkki
    Jeff Huang
    Nilufar Baghaei
    Craig Saper
    Massively distributed authorship of academic papers, ACM (2012)
    SPRING: speech and pronunciation improvement through games, for Hispanic children
    Anuj Tewari
    Matthew K. Chan
    Tina Yau
    John Canny
    Ulrik Schroeder
    SPRING: Speech and Pronunciation Improvement Through Games, for Hispanic Children (2010)