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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.
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.
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A Comparison of Questionnaire Biases Across Sample Providers
Aaron Sedley
Victoria Sosik
American Association for Public Opinion Research, 2015 Annual Conference(2015)
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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.
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"Not some trumped up beef": Assessing Credibility of Online Restaurant Reviews
Victoria Schwanda Sosik
Human-Computer Interaction – INTERACT 2015, Springer
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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.
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Online Microsurveys for User Experience Research
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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
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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?”
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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)
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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.
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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
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)
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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.
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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.
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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)
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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.
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Seller Activity in Virtual Marketplaces
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As virtual goods continue to proliferate in online worlds, understanding their production, consumption and distribution remains exciting for scholars, technology companies and policy makers alike. We present a descriptive study of the activities of successful sellers in Second Life, a 3D virtual world that allows users to create their content and even to make money by selling it to other users. We combine user log analysis, network analysis and content analysis to examine cycles in trading volume, market segmentation and specialization, geographic concentration and the impact of social capital on economic success, revealing important insights regarding virtual markets, as well as differences between the very top sellers and those making a more modest income.
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Dimensions of Leadership and Social Influence in Online Communities
Human Communication Research, 36(4)(2010), pp. 593-617
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The purpose of this article is to examine the communication behaviors of online leaders, or those who influence other members of online communities in triggering message replies, sparking conversation, and diffusing language. It relies on 632,622 messages from 33,450 participants across 16 discussion groups from Google Groups that took place over a 2-year period. It utilizes automated text analysis, social network analysis, and hierarchical linear modeling to uncover the language and social behavior of online leaders. The findings show that online leaders influence others through high communication activity, credibility, network centrality, and the use of affective, assertive, and linguistic diversity in their online messages.
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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)
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This paper examines motivations of knowledge workers to contribute expertise to online knowledge repositories that support informal learning, and presents findings from both a survey and an experimental study. Results indicate that younger workers and those new to an organization, are more motivated by self-interest factors such as gaining name recognition and impressing management, while older workers, and those with a longer tenure, are motivated by more altruistic factors such as sharing and mentoring.
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The Language of Online Leadership: Gender and Youth Engagement on the Internet
Justine Cassell
Dona Tversky
Kim Ferriman
Developmental Psychology, 42(3)(2006), pp. 436-449
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This study examines the JUNIOR SUMMIT online community, which consisted of 3,062 adolescents representing 139 countries, varying SES, and a range of experience with computers. The online forum culminated in the election of 100 delegates. By analyzing the messages posted before results of the election were announced, we explore whether language use predicts who was elected as a leader, as well as gender differences in leadership style. Results indicate that the young online leaders do not adhere to adult leadership styles of contributing many ideas, sticking to task, and using powerful language. On the contrary, while the young people elected as delegates do contribute more, their linguistic style is likely to keep the goals and needs of the group as central--by referring to the group rather than to themselves, and by synthesizing the posts of others rather than solely contributing their own ideas. Furthermore, both boy and girl leaders follow this pattern of interpersonal language use. These results reassure us that young people can be civically engaged and community minded, while indicating that these concepts themselves may change through contact with the next generation.
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Gender, Identity and Language Use in Teenage Blogs
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This study examines issues of online identity and language use among male and female teenagers who created and maintained weblogs, personal journals made publicly accessible on the World Wide Web. Online identity and language use were examined in terms of the disclosure of personal information, sexual identity, emotive features, and semantic themes. Male and female teenagers presented themselves similarly in their blogs, often revealing personal information such as their real names, ages, and locations. Males more so than females used emoticons, employed an active and resolute style of language, and were more likely to present themselves as gay. The results suggest that teenagers stay closer to reality in their online expressions of self than has previously been suggested, and that these explorations involve issues, such as learning about their sexuality, that commonly occur during the adolescent years.
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The educated blogger: Using weblogs to promote literacy in the classroom
First Monday, 9(6)(2004)
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This paper explores the role of weblogs or "blogs" in classroom settings. Blogs, which resemble personal journals or diaries and provide an online venue where self–expression and creativity is encouraged and online communities are built, provide an excellent opportunity for educators to advance literacy through storytelling and dialogue. This paper explores the importance of literacy and storytelling in learning, and then juxtaposes these concepts with the features of blogs. The paper also reviews examples of blogs in practice.
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