Bio
Michael Bernstein is an Associate Professor of Computer Science at Stanford University, where he is a Bass University Fellow and Interim Director of the Symbolic Systems program. His research focuses on designing social, societal, and interactive technologies. This research has been reported in venues such as The New York Times, Wired, Science, and Nature. Michael has been recognized with an Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the Computer History Museum's Patrick J. McGovern Tech for Humanity Prize. He holds a bachelor's degree in Symbolic Systems from Stanford University, as well as a master's degree and a Ph.D. in Computer Science from MIT.
Academic Appointments
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Associate Professor, Computer Science
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Member, Bio-X
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Honors & Awards
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Bass University Fellow, Stanford University
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Patrick J. McGovern Tech for Humanity Prize, Computer History Museum
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Lasting Impact Award, UIST
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CASBS Fellow, Center for Advanced Study in the Behavioral Sciences
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Teaching Honor Roll, Tau Beta Pi
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STMicroelectronics Faculty Scholar, Stanford University
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Sloan Research Fellowship, Sloan Foundation
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NSF CAREER award, National Science Foundation
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Best paper award, CHI 2019, CHI 2017, CSCW 2017, CHI 2016, UIST 2014, ICWSM 2011, UIST 2010, UIST 2006
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Outstanding Academic Title, "Handbook of Collective Intelligence", American Library Association, Choice
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Robert N. Noyce Family Faculty Scholar, Stanford University
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George M. Sprowls Award for best doctoral thesis in Computer Science, MIT
Program Affiliations
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Symbolic Systems Program
Professional Education
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PhD, MIT, Computer Science (2012)
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SM, MIT, Computer Science (2008)
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BS, Stanford University, Symbolic Systems (2006)
2024-25 Courses
- Computer Science Research
CS 197 (Aut) - Human-Computer Interaction Seminar
CS 547 (Aut, Win, Spr) - Human-Computer Interaction: Foundations and Frontiers
CS 347 (Win) - Social Computing
CS 278, SOC 174, SOC 274 (Spr) -
Independent Studies (17)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390C (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr, Sum) - Independent Study
SYMSYS 196 (Aut, Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr, Sum) - Master's Degree Project
SYMSYS 290 (Aut, Win, Spr, Sum) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr, Sum) - Programming Service Project
CS 192 (Aut, Win, Spr, Sum) - Senior Honors Tutorial
SYMSYS 190 (Aut, Win, Spr, Sum) - Senior Project
CS 191 (Aut, Win, Spr, Sum) - Supervised Undergraduate Research
CS 195 (Aut, Win, Spr, Sum) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Computer Science Research
CS 197 (Aut) - Human-Computer Interaction Seminar
CS 547 (Aut, Win, Spr) - Social Computing
CS 278 (Spr)
2022-23 Courses
- Computer Science Research
CS 197 (Aut) - Human-Computer Interaction Seminar
CS 547 (Aut, Win, Spr) - Human-Computer Interaction: Foundations and Frontiers
CS 347 (Win) - Social Computing
CS 278, SOC 174, SOC 274 (Spr)
- Computer Science Research
Stanford Advisees
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Postdoctoral Faculty Sponsor
Tiziano Piccardi -
Doctoral Dissertation Advisor (AC)
Michelle Lam -
Orals Evaluator
Jackie Yang -
Master's Program Advisor
Anavi Baddepudi, Carolina Borbon Miranda, Ria Calcagno, Thomas Escudero, William Fang, Nicole Garcia, Graham Griffin, Eric Lee, Matthew Lee, Amy Lo, Zayn Malhotra, Izzy Meyerson, William Newton, Eleanor Peng, Julia Rhee, Alison Rogers, Bhavya Shah, Simran Tandon, Hunter Zhang, Jessica Zhang, Xiaomiao Zhang, Yutong Zhang, Melanie Zhou, Annie Zhu -
Doctoral Dissertation Co-Advisor (AC)
Beleicia Bullock -
Doctoral (Program)
Michelle Lam, Catherine Mullings, Joon Park, Lindsay Popowski, Omar Shaikh, Jordan Troutman, Dora Zhao
All Publications
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Social and moral psychology of COVID-19 across 69 countries.
Scientific data
2023; 10 (1): 272
Abstract
The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.
View details for DOI 10.1038/s41597-023-02080-8
View details for PubMedID 37169799
View details for PubMedCentralID PMC10173241
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COVID-19 non-pharmaceutical interventions: data annotation for rapidly changing local policy information.
Scientific data
2023; 10 (1): 126
Abstract
Understanding the scope, prevalence, and impact of the COVID-19 pandemic response will be a rich ground for research for many years. Key to the response to COVID-19was the non-pharmaceutical intervention (NPI) measures, such as mask mandates or stay-in-place orders. For future pandemic preparedness, it is critical to understand the impact and scope of these interventions. Given the ongoing nature of the pandemic, existing NPI studies covering only the initial portion provide only a narrow view of the impact of NPI measures. This paper describes a dataset of NPI measures taken by counties in the U.S. state of Virginia that include measures taken over the first two years of the pandemic beginningin March 2020. This data enables analyses of NPI measures over a long time period that can produce impact analyses on both the individual NPI effectiveness in slowing the pandemic spread,and the impact of various NPI measures on the behavior and conditions of the different counties and state.
View details for DOI 10.1038/s41597-023-01979-6
View details for PubMedID 36894597
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Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research
ASSOC COMPUTING MACHINERY. 2023
View details for DOI 10.1145/3544548.3581108
View details for Web of Science ID 001037809507029
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Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design
ASSOC COMPUTING MACHINERY. 2023
View details for DOI 10.1145/3544548.3581290
View details for Web of Science ID 001048393802055
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Architecting Novel Interactions With Generative AI Models
ASSOC COMPUTING MACHINERY. 2023
View details for DOI 10.1145/3586182.3617431
View details for Web of Science ID 001125107000106
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On Second Thought, Let′s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 4454-4470
View details for Web of Science ID 001181086803015
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Socially situated artificial intelligence enables learning from human interaction.
Proceedings of the National Academy of Sciences of the United States of America
2022; 119 (39): e2115730119
Abstract
Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people-socially situated learning-is a core faculty of human development. Unfortunately, socially situated learning remains an open challenge for artificial intelligence agents because they must learn how to interact with people to seek out the information that they lack. In this article, we formalize the task of socially situated artificial intelligence-agents that seek out new information through social interactions with people-as a reinforcement learning problem where the agent learns to identify meaningful and informative questions via rewards observed through social interaction. We manifest our framework as an interactive agent that learns how to ask natural language questions about photos as it broadens its visual intelligence on a large photo-sharing social network. Unlike active-learning methods, which implicitly assume that humans are oracles willing to answer any question, our agent adapts its behavior based on observed norms of which questions people are or are not interested to answer. Through an 8-mo deployment where our agent interacted with 236,000 social media users, our agent improved its performance at recognizing new visual information by 112%. A controlled field experiment confirmed that our agent outperformed an active-learning baseline by 25.6%. This work advances opportunities for continuously improving artificial intelligence (AI) agents that better respect norms in open social environments.
View details for DOI 10.1073/pnas.2115730119
View details for PubMedID 36122244
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Social Simulacra: Creating Populated Prototypes for Social Computing Systems
ASSOC COMPUTING MACHINERY. 2022
View details for DOI 10.1145/3526113.3545616
View details for Web of Science ID 001046841800005
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Jury Learning: Integrating Dissenting Voices into Machine Learning Models
ASSOC COMPUTING MACHINERY. 2022
View details for DOI 10.1145/3491102.3502004
View details for Web of Science ID 000890212503009
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Ethics and society review: Ethics reflection as a precondition to research funding.
Proceedings of the National Academy of Sciences of the United States of America
1800; 118 (52)
Abstract
Researchers in areas as diverse as computer science and political science must increasingly navigate the possible risks of their research to society. However, the history of medical experiments on vulnerable individuals influenced many research ethics reviews to focus exclusively on risks to human subjects rather than risks to human society. We describe an Ethics and Society Review board (ESR), which fills this moral gap by facilitating ethical and societal reflection as a requirement to access grant funding: Researchers cannot receive grant funding from participating programs until the researchers complete the ESR process for their proposal. Researchers author an initial statement describing their proposed research's risks to society, subgroups within society, and globally and commit to mitigation strategies for these risks. An interdisciplinary faculty panel iterates with the researchers to refine these risks and mitigation strategies. We describe a mixed-method evaluation of the ESR over 1 y, in partnership with a large artificial intelligence grant program at our university. Surveys and interviews of researchers who interacted with the ESR found 100% (95% CI: 87 to 100%) were willing to continue submitting future projects to the ESR, and 58% (95% CI: 37 to 77%) felt that it had influenced the design of their research project. The ESR panel most commonly identified issues of harms to minority groups, inclusion of diverse stakeholders in the research plan, dual use, and representation in datasets. These principles, paired with possible mitigation strategies, offer scaffolding for future research designs.
View details for DOI 10.1073/pnas.2117261118
View details for PubMedID 34934006
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Understanding the Representation and Representativeness of Age in AI Data Sets
ASSOC COMPUTING MACHINERY. 2021: 834-842
View details for DOI 10.1145/3461702.3462590
View details for Web of Science ID 000767973400093
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Scene Graph Prediction with Limited Labels.
Proceedings. IEEE International Conference on Computer Vision
2020; 2019: 2580–90
Abstract
Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few' labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by 5.16 recall@ 100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.
View details for DOI 10.1109/iccv.2019.00267
View details for PubMedID 32218709
View details for PubMedCentralID PMC7098690
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Information Maximizing Visual Question Generation
IEEE COMPUTER SOC. 2019: 2008–18
View details for DOI 10.1109/CVPR.2019.00211
View details for Web of Science ID 000529484002018
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Eevee: Transforming Images by Bridging High-level Goals and Low-level Edit Operations
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290607.3312929
View details for Web of Science ID 000482042102024
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Scene Graph Prediction with Limited Labels
IEEE COMPUTER SOC. 2019: 1772–82
View details for DOI 10.1109/ICCVW.2019.00220
View details for Web of Science ID 000554591601101
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Conservation of Procrastination: Do Productivity Interventions Save Time or Just Redistribute It?
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300560
View details for Web of Science ID 000474467904022
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Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300760
View details for Web of Science ID 000474467906066
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HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000534424303044
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Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction
IEEE COMPUTER SOC. 2019: 1730–39
View details for DOI 10.1109/ICCVW.2019.00214
View details for Web of Science ID 000554591601096
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Ink: Increasing Worker Agency to Reduce Friction in Hiring Crowd Workers
ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION
2018; 25 (2)
View details for DOI 10.1145/3177882
View details for Web of Science ID 000431849700003
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Mechanical Novel: Crowdsourcing Complex Work Through Reflection and Revision
DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION
2018: 79–104
View details for DOI 10.1007/978-3-319-60967-6_5
View details for Web of Science ID 000432741300006
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In Search of the Dream Team: Temporally Constrained Multi-Armed Bandits for Identifying Effective Team Structures
ASSOC COMPUTING MACHINERY. 2018
View details for DOI 10.1145/3173574.3173682
View details for Web of Science ID 000509673101033
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Shared Autonomy for an Interactive AI System
ASSOC COMPUTING MACHINERY. 2018: 20–22
View details for DOI 10.1145/3266037.3266088
View details for Web of Science ID 000494261200007
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Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress
DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION
2018: 105–29
View details for DOI 10.1007/978-3-319-60967-6_6
View details for Web of Science ID 000432741300007
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Iris: A Conversational Agent for Complex Tasks
ASSOC COMPUTING MACHINERY. 2018
View details for DOI 10.1145/3173574.3174047
View details for Web of Science ID 000509673105074
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Referring Relationships
IEEE. 2018: 6867–76
View details for DOI 10.1109/CVPR.2018.00718
View details for Web of Science ID 000457843607003
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Engagement Learning: Expanding Visual Knowledge by Engaging Online Participants
ASSOC COMPUTING MACHINERY. 2018: 87–89
View details for DOI 10.1145/3266037.3266110
View details for Web of Science ID 000494261200029
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Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
INTERNATIONAL JOURNAL OF COMPUTER VISION
2017; 123 (1): 32-73
View details for DOI 10.1007/s11263-016-0981-7
View details for Web of Science ID 000400276400003
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Anyone Can Become a Troll
AMERICAN SCIENTIST
2017; 105 (3): 152–55
View details for DOI 10.1511/2017.126.152
View details for Web of Science ID 000399270400012
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Examining Crowd Work and Gig Work Through The Historical Lens of Piecework
ASSOC COMPUTING MACHINERY. 2017: 4599–4616
View details for DOI 10.1145/3025453.3025974
View details for Web of Science ID 000426970504043
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Huddler: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability
ASSOC COMPUTING MACHINERY. 2017: 1700-1713
View details for DOI 10.1145/2998181.2998300
View details for Web of Science ID 000455087800125
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The Daemo Crowdsourcing Marketplace
ASSOC COMPUTING MACHINERY. 2017: 1-4
View details for DOI 10.1145/3022198.3023270
View details for Web of Science ID 000455085000001
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Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms
ASSOC COMPUTING MACHINERY. 2017: 1902-1913
View details for DOI 10.1145/2998181.2998234
View details for Web of Science ID 000455087800139
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Founder Center: Enabling Access to Collective Social Capital
ASSOC COMPUTING MACHINERY. 2017: 2010-2022
View details for DOI 10.1145/2998181.2998244
View details for Web of Science ID 000455087800147
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Crowd Research: Open and Scalable University Laboratories
ASSOC COMPUTING MACHINERY. 2017: 829-843
View details for DOI 10.1145/3126594.3126648
View details for Web of Science ID 000455360100072
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Mechanical Novel: Crowdsourcing Complex Work through Reflection and Revision
ASSOC COMPUTING MACHINERY. 2017: 233–45
View details for DOI 10.1145/2998181.2998196
View details for Web of Science ID 000455087800018
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Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress
ASSOC COMPUTING MACHINERY. 2017: 246–58
View details for DOI 10.1145/2998181.2998195
View details for Web of Science ID 000455087800019
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A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
ASSOC COMPUTING MACHINERY. 2017: 889–901
View details for DOI 10.1145/299818.2998248
View details for Web of Science ID 000455087800066
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MyriadHub: Efficiently Scaling Personalized Email Conversations with Valet Crowdsourcing
ASSOC COMPUTING MACHINERY. 2017: 73–84
View details for DOI 10.1145/3025453.3025954
View details for Web of Science ID 000426970500007
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Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions
ASSOC COMPUTING MACHINERY. 2017: 1217–30
Abstract
In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.
View details for PubMedID 29399664
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Visual7W: Grounded Question Answering in Images
IEEE. 2016: 4995–5004
View details for DOI 10.1109/CVPR.2016.540
View details for Web of Science ID 000400012305008
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Talkabout: Making Distance Matter with Small Groups in Massive Classes
DESIGN THINKING RESEARCH: MAKING DESIGN THINKING FOUNDATIONAL
2016: 67-92
View details for DOI 10.1007/978-3-319-19641-1_6
View details for Web of Science ID 000371623000007
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Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
ASSOC COMPUTING MACHINERY. 2016: 625-637
View details for DOI 10.1145/2984511.2984542
View details for Web of Science ID 000387605000058
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Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
ASSOC COMPUTING MACHINERY. 2016: 237-247
View details for DOI 10.1145/2858036.2858528
View details for Web of Science ID 000380532900022
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Pay It Backward: Per-Task Payments on Crowdsourcing Platforms Reduce Productivity
ASSOC COMPUTING MACHINERY. 2016: 4111-4121
View details for DOI 10.1145/2858036.2858327
View details for Web of Science ID 000380532904010
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Empath: Understanding Topic Signals in Large-Scale Text
ASSOC COMPUTING MACHINERY. 2016: 4647-4657
View details for DOI 10.1145/2858036.2858535
View details for Web of Science ID 000380532904059
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Designing Scalable and Sustainable Peer Interactions Online
DESIGN THINKING RESEARCH: TAKING BREAKTHROUGH INNOVATION HOME
2016: 237-273
View details for DOI 10.1007/978-3-319-40382-3_14
View details for Web of Science ID 000399244000015
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Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
ASSOC COMPUTING MACHINERY. 2016: 2645–56
View details for DOI 10.1145/2858036.2858121
View details for Web of Science ID 000380532902062
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Meta: Enabling Programming Languages to Learn from the Crowd
ASSOC COMPUTING MACHINERY. 2016: 259-270
View details for DOI 10.1145/2984511.2984532
View details for Web of Science ID 000387605000025
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Visual Relationship Detection with Language Priors
SPRINGER INTERNATIONAL PUBLISHING AG. 2016: 852-869
View details for DOI 10.1007/978-3-319-46448-0_51
View details for Web of Science ID 000389382700051
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Embracing Error to Enable Rapid Crowdsourcing
ASSOC COMPUTING MACHINERY. 2016: 3167-3179
View details for DOI 10.1145/2858036.2858115
View details for Web of Science ID 000380532903017
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ImageNet Large Scale Visual Recognition Challenge
INTERNATIONAL JOURNAL OF COMPUTER VISION
2015; 115 (3): 211-252
View details for DOI 10.1007/s11263-015-0816-y
View details for Web of Science ID 000365089800001
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Soylent: A Word Processor with a Crowd Inside
COMMUNICATIONS OF THE ACM
2015; 58 (8): 85-94
View details for DOI 10.1145/1866029.1866078
View details for Web of Science ID 000358782600026
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Image Retrieval using Scene Graphs
IEEE. 2015: 3668–78
View details for Web of Science ID 000387959203075
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Human-Computer Interaction and Collective Intelligence
HANDBOOK OF COLLECTIVE INTELLIGENCE
2015: 57-83
View details for Web of Science ID 000387903500007
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Flock: Hybrid Crowd-Machine Learning Classifiers
ASSOC COMPUTING MACHINERY. 2015: 600-611
View details for DOI 10.1145/2675133.2675214
View details for Web of Science ID 000371990400048
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Talkabout: Making Distance Matter with Small Groups in Massive Classes
ASSOC COMPUTING MACHINERY. 2015: 1116-1128
View details for DOI 10.1145/2675133.2675166
View details for Web of Science ID 000371990400094
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Handbook of Collective Intelligence Introduction
HANDBOOK OF COLLECTIVE INTELLIGENCE
2015: 1-13
View details for Web of Science ID 000387903500001
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Measuring Crowdsourcing Effort with Error-Time Curves
ASSOC COMPUTING MACHINERY. 2015: 1365-1374
View details for DOI 10.1145/2702123.2702145
View details for Web of Science ID 000412395501048
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We Are Dynamo: Overcoming Stalling and Friction in Collective Action for Crowd Workers
ASSOC COMPUTING MACHINERY. 2015: 1621-1630
View details for DOI 10.1145/2702123.2702508
View details for Web of Science ID 000412395501080
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Break It Down: A Comparison of Macro- and Microtasks
ASSOC COMPUTING MACHINERY. 2015: 4061-4064
View details for DOI 10.1145/2702123.2702146
View details for Web of Science ID 000412395504011
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Motif: Supporting Novice Creativity through Expert Patterns
ASSOC COMPUTING MACHINERY. 2015: 1211–20
View details for DOI 10.1145/2702123.2702507
View details for Web of Science ID 000412395501029
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Learning Perceptual Kernels for Visualization Design
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
2014; 20 (12): 1933-1942
Abstract
Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowd-sourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types--including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement--and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions.
View details for DOI 10.1109/TVCG.2014.2346978
View details for Web of Science ID 000344991700038
- Ensemble: Exploring Complementary Strengths of Leaders and Crowds in Creative Collaboration 2014
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Designing and Deploying Online Field Experiments
ASSOC COMPUTING MACHINERY. 2014: 283-292
View details for DOI 10.1145/2566486.2567967
View details for Web of Science ID 000455945100028
- Catalyst: Triggering Collective Action with Thresholds 2014
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Crowd-Powered Systems
KUNSTLICHE INTELLIGENZ
2013; 27 (1): 69-73
View details for DOI 10.1007/s13218-012-0233-0
View details for Web of Science ID 000410094700011
- Crowd-scale Interactive Formal Reasoning and Analytics 2013
- EmailValet: Managing Email Overload through Private, Accountable Crowdsourcing 2013
- The Future of Crowd Work 2013
- Quantifying the Invisible Audience in Social Networks 2013
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Leveraging Online Populations for Crowdsourcing
IEEE INTERNET COMPUTING
2012; 16 (5): 10-12
View details for Web of Science ID 000308123100003
- Who Gives A Tweet? Evaluating Microblog Content Value 2012
- Direct Answers for Search Queries in the Long Tail 2012
- Analytic Methods for Optimizing Realtime Crowdsourcing CI: Collective Intelligence 2012 2012
- The Trouble with Social Computing Systems Research 2011
- Crowds in Two Seconds: Enabling Realtime Crowd-Powered Interfaces 2011
- PingPong++: Community Customization in Games and Entertainment 2011
- TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration 2011
- 4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community 2011
- Eddi: Interactive Topic-Based Browsing of Social Status Streams 2010
- Short and Tweet: Experiments on Recommending Content from Information Streams 2010
- Personalization via Friendsourcing ACM Transactions on Computer-Human Interaction 2010 2010
- Who Am I? Two-Four-Six-Oh-One! 2010
- Enhancing Directed Content Sharing on the Web 2010
- A Torrent of Tweets: Managing Information Overload in Online Social Streams 2010
- Soylent: A Word Processor with a Crowd Inside 2010
- Collabio: A Game for Annotating People within Social Networks 2009
- Note to Self: Examining Personal Information Keeping in a Lightweight Note-Taking Tool 2009
- CHIstory 2009
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Taskpose: Exploring Fluid Boundaries in an Associative Window Visualization
21st Annual ACM Symposium on User Interface Software and Technology
ASSOC COMPUTING MACHINERY. 2008: 231–234
View details for Web of Science ID 000267537800026
- Simplifying Knowledge Creation and Access for End-Users on the Semantic Web 2008
- Evolution and Evaluation of an Information Scrap Manager 2008
- Inky: A Sloppy Command Line for the Web with Rich Visual Feedback 2008
- Wicked Problems and Gnarly Results: Reflecting on Design and Evaluation Methods for Idiosyncratic Personal Information Management Tasks MIT-CSAIL-TR-2008-007 2008 2008
- Information Scraps: How and Why Information Eludes our Personal Information Management Tools ACM Transactions on Information Systems 2008 2008
- Management of Personal Information Scraps 2007
- GUI — Phooey!: The Case for Text Input 2007
- Personal Information Management, Personal Information Retrieval? 2007
- Diamond's Edge: From Notebook to Table and Back Again Ubicomp: Posters 2006 2006
- Reflective Physical Prototyping through Integrated Design, Test, and Analysis 2006
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D.tools: Integrated prototyping for physical interaction design
IEEE PERVASIVE COMPUTING
2005; 4 (4): 79-79
View details for Web of Science ID 000233117700013
- d.tools: Visually Prototyping Physical UIs through Statecharts UIST: Extended Abstracts 2005 2005
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Flash Organizations: Crowdsourcing Complex Work by Structuring Crowds As Organizations
2017
View details for DOI 10.1145/3025453.3025811