Influencing and Measuring Behaviour in Crowdsourced Activities

Crowdsourcing psychometric data is common in areas of Human-Computer Interaction (HCI) such as information visualization, text entry, and interface design. In some of the social sciences, crowdsourcing data is now considered routine, and even standard. In this chapter, we explore the collection of data in this manner, beginning by describing the variety of approaches can be used to crowdsource data. Then, we evaluate past literature that has compared the results of these approaches to more traditional data-collection techniques. From this literature, we synthesize a set of design and implementation guidelines for crowdsourcing studies. Finally, we describe how particular analytic techniques can be recruited to aid the analysis of large-scale crowdsourced data. The goal of this chapter it to clearly enumerate the difficulties of crowdsourcing psychometric data and to explore how, with careful planning and execution, these limitations can be overcome.

[1]  Katharina Reinecke,et al.  Personalized Feedback Versus Money: The Effect on Reliability of Subjective Data in Online Experimental Platforms , 2017, CSCW Companion.

[2]  Serge Egelman,et al.  The Anatomy of Smartphone Unlocking: A Field Study of Android Lock Screens , 2016, CHI.

[3]  Tanja Aitamurto,et al.  Unmasking the crowd: participants’ motivation factors, expectations, and profile in a crowdsourced law reform , 2017 .

[4]  Jennifer Preece,et al.  Citizen Science: New Research Challenges for Human–Computer Interaction , 2016, Int. J. Hum. Comput. Interact..

[5]  Alex S. Taylor,et al.  Re-Making Places: HCI, 'Community Building' and Change , 2016, CHI.

[6]  A. Acquisti,et al.  Beyond the Turk: Alternative Platforms for Crowdsourcing Behavioral Research , 2016 .

[7]  Edward Cutrell,et al.  Deterring Cheating in Online Environments , 2015, TCHI.

[8]  Duncan P. Brumby,et al.  Now Check Your Input: Brief Task Lockouts Encourage Checking, Longer Lockouts Encourage Task Switching , 2016, CHI.

[9]  Ann Blandford,et al.  Designing for dabblers and deterring drop-outs in citizen science , 2014, CHI.

[10]  Anna L. Cox,et al.  Home is Where the Lab is: A Comparison of Online and Lab Data From a Time-sensitive Study of Interruption , 2015, Hum. Comput..

[11]  Katharina Reinecke,et al.  Doodle around the world: online scheduling behavior reflects cultural differences in time perception and group decision-making , 2013, CSCW.

[12]  Andrea Wiggins,et al.  Community-based Data Validation Practices in Citizen Science , 2016, CSCW.

[13]  Aaron D. Shaw,et al.  Designing incentives for inexpert human raters , 2011, CSCW.

[14]  Qingming Huang,et al.  Robust evaluation for quality of experience in crowdsourcing , 2013, ACM Multimedia.

[15]  Anna L. Cox,et al.  Diminished Control in Crowdsourcing , 2016, ACM Trans. Comput. Hum. Interact..

[16]  Tara S. Behrend,et al.  The viability of crowdsourcing for survey research , 2011, Behavior research methods.

[17]  James D. Abbey,et al.  Attention by design: Using attention checks to detect inattentive respondents and improve data quality , 2017 .

[18]  Chris Callison-Burch,et al.  A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk , 2017, CHI.

[19]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[20]  Christopher G. Harris The Effects of Pay-to-Quit Incentives on Crowdworker Task Quality , 2015, CSCW.

[21]  Penelope M. Sanderson,et al.  The Effect of Individual Differences on How People Handle Interruptions , 2013 .

[22]  Michael S. Bernstein,et al.  Examining Crowd Work and Gig Work Through the Historical Lens of Piecework , 2017, CHI.

[23]  C. Lintott,et al.  Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies , 2009, 0907.4155.

[24]  Antti Oulasvirta,et al.  Model of visual search and selection time in linear menus , 2014, CHI.

[25]  Duncan P. Brumby,et al.  Frequency and Duration of Self-Initiated Task-Switching in an Online Investigation of Interrupted Performance , 2013, HCOMP.

[26]  Sean A. Munson,et al.  Beyond Abandonment to Next Steps: Understanding and Designing for Life after Personal Informatics Tool Use , 2016, CHI.

[27]  Andrew Howes,et al.  Strategies for Guiding Interactive Search: An Empirical Investigation Into the Consequences of Label Relevance for Assessment and Selection , 2008, Hum. Comput. Interact..

[28]  D. Dunning The Dunning–Kruger Effect , 2011 .

[29]  Mirco Musolesi,et al.  My Phone and Me: Understanding People's Receptivity to Mobile Notifications , 2016, CHI.

[30]  Sun Young Park,et al.  Technological and Organizational Adaptation of EMR Implementation in an Emergency Department , 2015, TCHI.

[31]  Daniel J. Wigdor,et al.  Slide to X: unlocking the potential of smartphone unlocking , 2014, CHI.

[32]  Hojung Cha,et al.  Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities , 2013, SenSys '13.

[33]  Anna L. Cox,et al.  Always On(line)?: User Experience of Smartwatches and their Role within Multi-Device Ecologies , 2017, CHI.

[34]  Patrick Olivier,et al.  Digital Civics: Citizen Empowerment With and Through Technology , 2016, CHI Extended Abstracts.

[35]  Bonnie A. Nardi,et al.  Not Just in it for the Money: A Qualitative Investigation of Workers' Perceived Benefits of Micro-task Crowdsourcing , 2015, 2015 48th Hawaii International Conference on System Sciences.

[36]  Michael S. Bernstein,et al.  Break It Down: A Comparison of Macro- and Microtasks , 2015, CHI.

[37]  M. Graber,et al.  Internet-based crowdsourcing and research ethics: the case for IRB review , 2012, Journal of Medical Ethics.

[38]  Shaowen Bardzell,et al.  Social Justice and Design: Power and oppression in collaborative systems , 2017, CSCW Companion.

[39]  Katharina Reinecke,et al.  LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples , 2015, CSCW.

[40]  David J. Hauser,et al.  It’s a Trap! Instructional Manipulation Checks Prompt Systematic Thinking on “Tricky” Tasks , 2015 .

[41]  Jon Froehlich,et al.  Differences in Crowdsourced vs. Lab-based Mobile and Desktop Input Performance Data , 2017, CHI.

[42]  C. Lintott,et al.  Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey , 2008, 0804.4483.

[43]  Marta E. Cecchinato,et al.  Working 9-5?: Professional Differences in Email and Boundary Management Practices , 2015, CHI.

[44]  Rick A Adams,et al.  Crowdsourcing for Cognitive Science – The Utility of Smartphones , 2014, PloS one.

[45]  Aniket Kittur,et al.  CrowdScape: interactively visualizing user behavior and output , 2012, UIST.

[46]  Jennifer Preece,et al.  Accounting for Privacy in Citizen Science: Ethical Research in a Context of Openness , 2017, CSCW.

[47]  Kate A. Ratliff,et al.  Using Nonnaive Participants Can Reduce Effect Sizes , 2015, Psychological science.

[48]  Lydia B. Chilton,et al.  The labor economics of paid crowdsourcing , 2010, EC '10.

[49]  Peter Hoonakker,et al.  Questionnaire Survey Nonresponse: A Comparison of Postal Mail and Internet Surveys , 2009, Int. J. Hum. Comput. Interact..

[50]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[51]  Katharina Reinecke,et al.  Types of Motivation Affect Study Selection, Attention, and Dropouts in Online Experiments , 2017, Proc. ACM Hum. Comput. Interact..

[52]  Duncan J. Watts,et al.  Financial incentives and the "performance of crowds" , 2009, HCOMP '09.

[53]  C. Potter,et al.  Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions , 2014, Public understanding of science.

[54]  Adam Marcus,et al.  The Effects of Sequence and Delay on Crowd Work , 2015, CHI.

[55]  A. Cox,et al.  Motivations, learning and creativity in online citizen science , 2016 .

[56]  Denzil Ferreira,et al.  AWARE: Mobile Context Instrumentation Framework , 2015, Front. ICT.

[57]  Jesse Chandler,et al.  Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers , 2013, Behavior Research Methods.

[58]  Anna L. Cox,et al.  Media Multitasking at Home: A Video Observation Study of Concurrent TV and Mobile Device Usage , 2017, TVX.

[59]  Michael B. Twidale,et al.  Design Facets of Crowdsourcing , 2015 .

[60]  Robert E. Kraut,et al.  Why pay?: exploring how financial incentives are used for question & answer , 2010, CHI.

[61]  Martha Larson,et al.  Crowdsourcing as self-fulfilling prophecy: Influence of discarding workers in subjective assessment tasks , 2016, 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI).

[62]  Anna L. Cox,et al.  Exploring Citizen Psych-Science and the Motivations of Errordiary Volunteers , 2014, Hum. Comput..

[63]  Gary Marsden,et al.  After access: challenges facing mobile-only internet users in the developing world , 2010, CHI.

[64]  Stefan Dietze,et al.  Using Worker Self-Assessments for Competence-Based Pre-Selection in Crowdsourcing Microtasks , 2017, ACM Trans. Comput. Hum. Interact..

[65]  M. Six Silberman,et al.  Ethics and tactics of professional crowdwork , 2010, XRDS.

[66]  Jim Foster,et al.  Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus) , 2015 .

[67]  Per Ola Kristensson,et al.  Improving two-thumb text entry on touchscreen devices , 2013, CHI.

[68]  Martin Pielot,et al.  An in-situ study of mobile phone notifications , 2014, MobileHCI '14.

[69]  Katharina Reinecke,et al.  Crowdsourcing performance evaluations of user interfaces , 2013, CHI.

[70]  Peng Dai,et al.  And Now for Something Completely Different: Improving Crowdsourcing Workflows with Micro-Diversions , 2015, CSCW.

[71]  Wayne D. Gray Game-XP: Action Games as Experimental Paradigms for Cognitive Science , 2017, Top. Cogn. Sci..

[72]  H. G. D. Zúñiga,et al.  Influence of social media use on discussion network heterogeneity and civic engagement: The moderating role of personality traits , 2013 .

[73]  Anne M. Land-Zandstra,et al.  Citizen science on a smartphone: Participants’ motivations and learning , 2016, Public understanding of science.

[74]  Duncan P. Brumby,et al.  Task Lockouts Induce Crowdworkers to Switch to Other Activities , 2015, CHI Extended Abstracts.

[75]  Peng Dai,et al.  Inserting Micro-Breaks into Crowdsourcing Workflows , 2013, HCOMP.

[76]  Daniel M. Oppenheimer,et al.  Instructional Manipulation Checks: Detecting Satisficing to Increase Statistical Power , 2009 .

[77]  Kevin B. Wright,et al.  Researching Internet-Based Populations: Advantages and Disadvantages of Online Survey Research, Online Questionnaire Authoring Software Packages, and Web Survey Services , 2006, J. Comput. Mediat. Commun..

[78]  David G. Rand,et al.  The promise of Mechanical Turk: how online labor markets can help theorists run behavioral experiments. , 2012, Journal of theoretical biology.

[79]  J. Suls,et al.  Flawed Self-Assessment , 2004, Psychological science in the public interest : a journal of the American Psychological Society.

[80]  Mark D. Dunlop,et al.  Multidimensional pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking , 2012, CHI.

[81]  Caroline Jay,et al.  To Sign Up, or not to Sign Up?: Maximizing Citizen Science Contribution Rates through Optional Registration , 2016, CHI.

[82]  Susanne Bødker,et al.  Third-wave HCI, 10 years later---participation and sharing , 2015, Interactions.

[83]  Duncan P. Brumby,et al.  How does knowing what you are looking for change visual search behavior? , 2014, CHI.

[84]  Duncan P. Brumby,et al.  Visual Grouping in Menu Interfaces , 2015, CHI.

[85]  Derek Ruths,et al.  How One Microtask Affects Another , 2016, CHI.

[86]  Aniket Kittur,et al.  Instrumenting the crowd: using implicit behavioral measures to predict task performance , 2011, UIST.

[87]  Johannes Schöning,et al.  The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement , 2017, CHI.

[88]  Brian L. Sullivan,et al.  eBird: A citizen-based bird observation network in the biological sciences , 2009 .

[89]  Olivier Festor,et al.  CrowdOut: A mobile crowdsourcing service for road safety in digital cities , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[90]  Ann Blandford,et al.  Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation , 2015, CHI.

[91]  J. Silvertown A new dawn for citizen science. , 2009, Trends in ecology & evolution.

[92]  Kevin C. Elliott,et al.  A framework for addressing ethical issues in citizen science , 2015 .

[93]  Elena Paslaru Bontas Simperl,et al.  From Crowd to Community: A Survey of Online Community Features in Citizen Science Projects , 2017, CSCW.

[94]  Michael S. Bernstein,et al.  We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers , 2015, CHI.

[95]  Anna L. Cox,et al.  Exploring the effects of non-monetary reimbursement for participants in HCI research , 2017, Hum. Comput..

[96]  Michael S. Bernstein,et al.  Twitch crowdsourcing: crowd contributions in short bursts of time , 2014, CHI.

[97]  Dana Chandler,et al.  Preventing Satisficing in Online Surveys: A "Kapcha" to Ensure Higher Quality Data , 2010 .

[98]  K. Nakayama,et al.  Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments , 2012, Psychonomic Bulletin & Review.

[99]  M. Six Silberman,et al.  Turkopticon: interrupting worker invisibility in amazon mechanical turk , 2013, CHI.

[100]  Jesse J. Chandler,et al.  Crowdsourcing Samples in Cognitive Science , 2017, Trends in Cognitive Sciences.

[101]  Eben M. Haber,et al.  Creek watch: pairing usefulness and usability for successful citizen science , 2011, CHI.

[102]  Michael S. Bernstein,et al.  The future of crowd work , 2013, CSCW.