Nate TeBlunthuis

@groceryheist.cc

666 Followers 1919 Following

https://tebluntuhis.cc Assistant Professor at the University of Texas at Austin in the School of Information. Computational social science, peer production, social computing, HCI.

Frequency plot of the durations of competition and mutualism episodes. Mutualism tends to last longer than competition. The y-axis is log-transformed. The axes truncated to omit outliers for visibility.
Ecological network of a cluster of mental subreddits. Blue arrows indicate mutualism and yellow arrows indicate competition according to a vector autoregression model.
Names of bicycle-related subreddits in cluster of subreddits with many overlapping users.
Cover of a Zine titled "A Web worth wandering: The radical internet and you". The paper is pink. The title is above. "A" is in a serif font. "Web" is italic serif. "WORTH WANDERING" is all caps sans serif. A black and white image of a 90s desktop computer, possibly a Macintosh Performa is right-aligned and separated from the title by whitespace. The subtitle is below the image.
Photo of Nathan TeBlunthuis, smiling, wearing a longhorns T-shirt, and making the "hook'em horns" gesture. The background is a vibrant green plant.
igure showing the simulation results for systematic misclassification of a dependent variable. Only the maximum likelihood adjustment (MLA; ours) approach obtains consistent estimates of 𝐵𝑋
Figure showing the effectiveness of methods against ystematic misclassification of an independent variable. Only the maximum likelihood adjustment (MLA; ours) and multiple imputation (MI) approaches obtain consistent estimates of 𝐵𝑋 , but MLA is more efficient.
Slide summarizing the other methods we investigated. These are Regression Calibration via Generalized Method of Moments (GMM) by Fong and Tyler in the paper “Machine Learning Predictions as Regression Covariates” which uses manual annotations to calibrate predictions but only works for IVs. Multiple Imputation (MI), which uses predictions to impute manual annotations and is said to work with IVs or DVs. (citation Blackwell, Honaker, and King, “A Unified Approach to Measurement Error and Missing Data”) Pseudo-likelihood (PL), which uses use precision and recall to model misclassification and is said to work with IVs or DVs (citation Zhang, How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It).
Visualization of Bayesian networks showing the conditional independence structure of the simulations.
Figure showing results from our real data example using the Perspective API and the Civil Comments Dataset. On the top, a model using Perspective's classifications results in a type-II error. It fails to detect a direct relationship toxicity and whether a comment discloses racial or ethnic identity, but when human annotations are used instead this is observed. On the bottom, using Perspective's classifications results in a type-I error. It falsely detects a negative direct relationship between the likes a comment received and its toxicity
Bob the builder asks "Can we fix it?"