David M. J. Lazer
Sociology and Political Science · 2003–2020
A researcher in the Science corpus: 21 papers; trusts 65 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Sociology and Political Science — 102 citations into it; #3,614 of 22,756 by trust here, #1,017 by citations
- Artificial Intelligence — 84 citations into it; #16,122 of 35,345 by trust here, #5,970 by citations
- Statistical and Nonlinear Physics — 77 citations into it; #3,545 of 10,127 by trust here, #416 by citations
- AI Ethics, Fairness & Society — 32 citations into it
- Communication — 27 citations into it; #1,224 of 3,460 by trust here, #192 by citations
- Safety, Robustness & Interpretability — 27 citations into it
- Management Science and Operations Research — 26 citations into it
- Social Sciences & Humanities — 26 citations into it
Whom David M. J. Lazer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Duncan J. Watts
- Alberto Cavallo
- Beth A. Simmons
- Jan W. Rivkin
- Mark S. Granovetter
- Brian Uzzi
- Benjamin Edelman
- Jeffrey Pennington
- Albert-László Barabási
- Glen A. Coppersmith
Who trusts David M. J. Lazer overall
- Sarah Brayne
- Martin Hilbert
- Winter A. Mason
- P. May
- Ritu Agarwal
- John F. Symons
- Jakko Kemper
- Alexandra Olteanu
- Marzyeh Ghassemi
- Elizabeth Suhay
Papers from David M. J. Lazer's perspective
Ordered by how strongly the researchers David M. J. Lazer trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- GloVe: Global Vectors for Word Representation
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- The Parable of Google Flu: Traps in Big Data Analysis (2014) — 2,270 citations
- The science of fake news (2018) — 3,794 citations
- Fake news on Twitter during the 2016 U.S. presidential election (2019) — 1,501 citations
- The Network Structure of Exploration and Exploitation (2007) — 748 citations
- Public Health and Online Misinformation: Challenges and Recommendations. (2019) — 907 citations
- Measuring Price Discrimination and Steering on E-commerce Web Sites (2014) — 347 citations
- Management-Based Regulation: Prescribing Private Management to Achieve Public Goals (2003) — 431 citations
- More Voices Than Ever? Quantifying Media Bias in Networks (2011) — 73 citations
- ConStance: Modeling Annotation Contexts to Improve Stance Classification (2017) — 32 citations
- A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns (2015) — 113 citations
- Computational social science: Obstacles and opportunities (2020) — 365 citations
- Data ex Machina: Introduction to Big Data (2017) — 198 citations
- Networks in Political Science: Back to the Future (2011) — 149 citations
- The Strength of Strong Ties (2003) — 191 citations
- Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power (2020)
- Machine behaviour (2019) — 1,247 citations
- Measuring personalization of web search (2013) — 451 citations
- Inferring friendship network structure by using mobile phone data (2009) — 1,925 citations
- Life in the network: The coming age of computational social science: Science (2009) — 930 citations
- Structure and tie strengths in mobile communication networks (2006) — 1,957 citations