M
Michael S. Bernstein
Researcher at Stanford University
Publications - 207
Citations - 59397
Michael S. Bernstein is an academic researcher from Stanford University. The author has contributed to research in topics: Crowdsourcing & Computer science. The author has an hindex of 52, co-authored 191 publications receiving 42744 citations. Previous affiliations of Michael S. Bernstein include Association for Computing Machinery & Massachusetts Institute of Technology.
Papers
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Proceedings ArticleDOI
PingPong++: community customization in games and entertainment
Xiao Xiao,Michael S. Bernstein,Lining Yao,David Lakatos,Lauren Gust,Kojo Acquah,Hiroshi Ishii +6 more
TL;DR: PingPong++ as discussed by the authors is an augmented ping pong table that applies Do-It-Youself (DIY) and community contribution principles to the world of physical sports and play, and includes an API for creating new visualizations, easily recreateable hardware, an end-user interface for those without programming experience, and a crowd data API for replaying and remixing past games.
Proceedings ArticleDOI
Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms
Mark E. Whiting,Dilrukshi Gamage,Snehalkumar (Neil) S. Gaikwad,Aaron Gilbee,Shirish Goyal,Alipta Ballav,Dinesh Majeti,Nalin Chhibber,Angela Richmond-Fuller,Freddie Vargus,Tejas Sarma,Varshine Chandrakanthan,Teogenes Moura,Mohamed Hashim Salih,Gabriel Bayomi Tinoco Kalejaiye,Adam Ginzberg,Catherine A. Mullings,Yoni Dayan,Kristy Milland,Henrique R. Orefice,Jeff Regino,Sayna Parsi,Kunz Mainali,Vibhor Sehgal,Sekandar Matin,Akshansh Sinha,Rajan Vaish,Michael S. Bernstein +27 more
TL;DR: Drawing inspiration from historical worker guilds is drawn to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment.
Posted Content
Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships
TL;DR: In this article, a micro-internship platform that connects crowd interns with crowd mentors is proposed. But it does not address the issue that many workers cannot invest the time and sacrifice the earnings required to learn a new skill, and a lack of experience makes it difficult to get job offers even if they do.
Proceedings ArticleDOI
Meta: Enabling Programming Languages to Learn from the Crowd
Ethan Fast,Michael S. Bernstein +1 more
TL;DR: Meta: a language extension for Python that allows programmers to share functions and track how they are used by a crowd of other programmers is introduced, finding that professional programmers are able to use Meta for complex tasks, and that Meta is able to find 44 optimizations and 5 bug fixes across the crowd.
Proceedings ArticleDOI
Lexicons on Demand: Neural Word Embeddings for Large-Scale Text Analysis
TL;DR: Empath is a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like “bleed” and “punch” to generate the category violence) and is highly correlated with similar categories in LIWC.