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Full-Text Articles in Physical Sciences and Mathematics
Ideology Prediction From Scarce And Biased Supervision: Learn To Disregard The “What” And Focus On The “How”!, Chen Chen, Dylan Walker, Venkatesh Saligrama
Ideology Prediction From Scarce And Biased Supervision: Learn To Disregard The “What” And Focus On The “How”!, Chen Chen, Dylan Walker, Venkatesh Saligrama
Business Faculty Articles and Research
We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the fact that manual data-labeling is expensive, while self-reported labels are often scarce and exhibit significant selection bias. We propose a novel statistical model that decomposes the document embeddings into a linear superposition of two vectors; a latent neutral context vector independent of ideology, and a latent position vector aligned with ideology. We train an end-to-end model that has intermediate contextual and positional vectors as outputs. At deployment time, our model predicts labels for input documents …
Identifying Social Influence In Networks Using Randomized Experiments, Sinan Aral, Dylan Walker
Identifying Social Influence In Networks Using Randomized Experiments, Sinan Aral, Dylan Walker
Business Faculty Articles and Research
The recent availability of massive amounts of networked data generated by email, instant messaging, mobile phone communications, micro blogs, and online social networks is enabling studies of population-level human interaction on scales orders of magnitude greater than what was previously possible.1'2 One important goal of applying statistical inference techniques to large networked datasets is to understand how behavioral contagions spread in human social networks. More precisely, understanding how people influence or are influenced by their peers can help us understand the ebb and flow of market trends, product adoption and diffusion, the spread of health behaviors such as smoking and …