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David Huffaker

David Huffaker

David Huffaker is the Director of UX Research for Google Maps.

His academic research focuses on understanding communication and social behavior to inform the design of HCI. He holds a Ph.D. in Media, Technology and Society from Northwestern University.
Authored Publications
Google Publications
Other Publications
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    "Not some trumped up beef": Assessing Credibility of Online Restaurant Reviews
    Victoria Schwanda Sosik
    Human-Computer Interaction – INTERACT 2015, Springer
    Preview abstract Online reviews, or electronic word of mouth (eWOM), are an essential source of information for people making decisions about products and services, however they are also susceptible to abuses such as spamming and defamation. Therefore when making decisions, readers must determine if reviews are credible. Yet relatively little research has investigated how people make credibility judgments of online reviews. This paper presents quantitative and qualitative results from a survey of 1,979 respondents, showing that attributes of the reviewer and review content influence credibility ratings. Especially important for judging credibility is the level of detail in the review, whether or not it is balanced in sentiment, and whether the reviewer demonstrates expertise. Our findings contribute to the understanding of how people judge eWOM credibility, and we suggest how eWOM platforms can be designed to coach reviewers to write better reviews and present reviews in a manner that facilitates credibility judgments. View details
    A Comparison of Questionnaire Biases Across Sample Providers
    Aaron Sedley
    Victoria Sosik
    American Association for Public Opinion Research, 2015 Annual Conference (2015)
    Preview abstract Survey research, like all methods, is fraught with potential sources of error that can significantly affect the validity and reliability of results. There are four major types of error common to surveys as a data collection method: (1) coverage error arising from certain segments of a target population being excluded, (2) nonresponse error where not all those selected for a sample respond, (3) sampling error which results from the fact that surveys only collect data from a subset of the population being measured, and (4) measurement error. Measurement error can arise from the wording and design of survey questions (i.e., instrument error), as well as the variability in respondent ability and motivation (i.e., respondent error) [17]. This paper focuses primarily on measurement error as a source of bias in surveys. It is well established that instrument error [34, 40] and respondent error (e.g., [21]) can yield meaningful differences in results. For example, variations in response order, response scales, descriptive text, or images used in a survey can lead to instrument error which can result in skewed response distributions. Certain types of questions can trigger other instrument error biases, such as the tendency to agree with statements presented in an agree/disagree format (acquiescence bias) or the hesitancy to admit undesirable behaviors or overreport desirable behaviors (social desirability bias). Respondent error is largely related to the amount of cognitive effort required to answer a survey and arises when respondents are either unable or unwilling to exert the required effort [21]. Such measurement error has been compared across survey modes, such as face-to-face, telephone, and Internet (e.g., [9, 18]), but little work has compared different Internet samples, such as crowdsourcing task platforms (e.g., Amazon’s Mechanical Turk), paywall surveys (e.g., Google Consumer Surveys), opt-in panels (e.g., Survey Sampling International), and probability based panels (e.g., the Gfk KnowledgePanel). Because these samples differ in recruiting, context, and incentives, respondents may be more or less motivated to effortfully respond to questions, leading to different degrees of bias in different samples. The specific instruments deployed to respondents in these different modes can also exacerbate the situation by requiring more or less cognitive effort to answer satisfactorily. The present study has two goals: Investigate the impact of question wording on response distributions in order to measure the strength of common survey biases arising from instrument and respondent error Compare the variance in the degree of these biases across Internet survey samples with differing characteristics in order to determine whether certain types of samples are more susceptible to certain biases than others. View details
    Online Microsurveys for User Experience Research
    Victoria Schwanda Sosik
    Gueorgi Kossinets
    Kerwell Liao
    Paul McDonald
    Aaron Sedley
    CHI '14 Extended Abstracts on Human Factors in Computing Systems (2014)
    Instant Foodie: Predicting Expert Ratings From Grassroots
    Chenhao Tan
    Gueorgi Kossinets
    Alex J. Smola
    CIKM’13, Oct. 27–Nov. 1, 2013, San Francisco, CA, USA, ACM
    Preview abstract Consumer review sites and recommender systems typically rely on a large volume of user-contributed ratings, which makes rating acquisition an essential component in the design of such systems. User ratings are then summarized to provide an aggregate score representing a popular evaluation of an item. An inherent problem in such summarization is potential bias due to raters’ self-selection and heterogeneity in terms of experiences, tastes and rating scale interpretations. There are two major approaches to collecting ratings, which have different advantages and disadvantages. One is to allow a large number of volunteers to choose and rate items directly (a method employed by e.g. Yelp and Google Places). Alternatively, a panel of raters may be maintained and invited to rate a predefined set of items at regular intervals (such as in Zagat Survey). The latter approach arguably results in more consistent reviews and reduced selection bias, however, at the expense of much smaller coverage (fewer rated items). In this paper, we examine the two different approaches to collecting user ratings of restaurants and explore the question of whether it is possible to reconcile them. Specifically, we study the problem of inferring the more calibrated Zagat Survey ratings (which we dub “expert ratings”) from the user-contributed ratings (“grassroots”) in Google Places. To achieve this, we employ latent factor models and provide a probabilistic treatment of the ordinal ratings. We can predict Zagat Survey ratings accurately from ad hoc user-generated ratings by employing joint optimization. Furthermore, the resulting model show that users become more discerning as they submit more ratings. We also describe an approach towards cross-city recommendations, answering questions such as “What is the equivalent of the Per Se restaurant in Chicago?” View details
    Understanding the Meta-Experience of Casual Games
    Carolyn Wei
    Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’12). Workshop on Games User Research, ACM (2012)
    Preview abstract In this position paper, we argue that casual gamers can be segmented by “meta-experiences” into a typology that could inform game platform design. These meta- experiences include out-of-game immersion, social layering, and game discovery. We discuss the interviews and video diaries that have helped shape the typology. View details
    Are privacy concerns a turn-off? Engagement and privacy in social networks
    Jessica Staddon
    Larkin Brown
    Aaron Sedley
    Symposium on Usable Privacy and Security (SOUPS), ACM (2012) (to appear)
    Preview abstract We describe the survey results from a representative sample of 1,075 U.S. social network users who use Facebook as their primary network. Our results show a strong association between low engagement and privacy concern. Specifically, users who report concerns around sharing control, comprehension of sharing practices or general Facebook privacy concern, also report consistently less time spent as well as less (self-reported) posting, commenting and “Like”ing of content. The limited evidence of other significant differences between engaged users and others suggests that privacy-related concerns may be an important gate to engagement. Indeed, privacy concern and network size are the only malleable attributes that we find to have significant association with engagement. We manually categorize the privacy concerns finding that many are nonspecific and not associated with negative personal experiences. Finally, we identify some education and utility issues associated with low social network activity, suggesting avenues for increasing engagement amongst current users. View details
    Talking in Circles: Selective Sharing in Google+
    Sanjay Kairam
    Michael J. Brzozowski
    Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’12), ACM, New York, NY (2012), pp. 1065-1074
    Preview abstract Online social networks have become indispensable tools for information sharing, but existing ‘all-or-nothing’ models for sharing have made it difficult for users to target information to specific parts of their networks. In this paper, we study Google+, which enables users to selectively share content with specific ‘Circles’ of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that these users frequently engaged in selective sharing, creating circles to manage content across particular life facets, ties of varying strength, and interest-based groups. Motivations to share spanned personal and informational reasons, and users frequently weighed ‘limiting’ factors (e.g. privacy, relevance, and social norms) against the desire to reach a large audience. Our work identifies implications for the design of selective sharing mechanisms in social networks. View details
    Game Your Campaign
    Carolyn Wei
    Think Quarterly, Google, Inc. (2012)
    Preview abstract In this article Carolyn Wei and David Huffaker, Google User Experience researchers, explore how understanding gaming sociability could help marketers communicate with a growing audience in new ways. From heightening personalization with "virtual goods", to avoiding the pitfalls of "noisy" game notifications, today's marketers can create a gaming niche that is both relevant and meaningful to a highly engaged user base. View details
    Around the Water Cooler: Shared Discussion Topics and Contact Closeness in Social Search
    Saranga Komanduri
    Lujun Fang
    Jessica Staddon
    Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM-12), ACM (2012)
    Preview abstract Search engines are now augmenting search results with social annotations, i.e., endorsements from users’ social network contacts. However, there is currently a dearth of published research on the effects of these annotations on user choice. This work investigates two research questions associated with annotations: 1) do some contacts affect user choice more than others, and 2) are annotations relevant across various information needs. We conduct a controlled experiment with 355 participants, using hypothetical searches and annotations, and elicit users’ choices. We find that domain contacts are preferred to close contacts, and this preference persists across a variety of information needs. Further, these contacts need not be experts and might be identified easily from conversation data. View details
    Seller Activity in Virtual Marketplaces
    Matthew Simmons
    Eytan Bakshy
    Lada Adamic
    First Monday, vol. 15(7) (2010)
    Dimensions of Leadership and Social Influence in Online Communities
    Human Communication Research, vol. 36(4) (2010), pp. 593-617
    The social behaviors of experts in massive multiplayer online role-playing games
    Jing Wang
    Jeffrey Treem
    Muhammad Ahmad
    Lindsay Fullerton
    Marshall Scott Poole
    Noshir Contractor
    . International Conference on Computational Science and Engineering (CSE '09) (2009), pp. 326 - 331
    Motivating Online Expertise-Sharing for Informal Learning: The Influence of Age and Tenure in Knowledge Organization
    Jennifer Lai
    7th IEEE International Conference on Advanced Learning Technologies (ICALT07), 1600 Amphitheatre Pkwy (2007)
    The Language of Online Leadership: Gender and Youth Engagement on the Internet
    Justine Cassell
    Dona Tversky
    Kim Ferriman
    Developmental Psychology, vol. 42(3) (2006), pp. 436-449
    Gender, Identity and Language Use in Teenage Blogs
    Sandra Calvert
    Journal of Computer-Mediated Communication, vol. 10(2) (2005)
    The educated blogger: Using weblogs to promote literacy in the classroom
    First Monday, vol. 9(6) (2004)