Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application

by   Chris J. Kennedy, et al.

We propose a general method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT). We decompose the target construct, hate speech in our case, into multiple constituent components that are labeled as ordinal survey items. Those survey responses are transformed via IRT into a debiased, continuous outcome measure. Our method estimates the survey interpretation bias of the human labelers and eliminates that influence on the generated continuous measure. We further estimate the response quality of each labeler using faceted IRT, allowing responses from low-quality labelers to be removed. Our faceted Rasch scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction on new data. The ratings on the theorized components of the target outcome are used as supervised, ordinal variables for the neural networks' internal concept learning. We test the use of an activation function (ordinal softmax) and loss function (ordinal cross-entropy) designed to exploit the structure of ordinal outcome variables. Our multitask architecture leads to a new form of model interpretation because each continuous prediction can be directly explained by the constituent components in the penultimate layer. We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers to measure a continuous spectrum from hate speech to counterspeech. We evaluate Universal Sentence Encoders, BERT, and RoBERTa as language representation models for the comment text, and compare our predictive accuracy to Google Jigsaw's Perspective API models, showing significant improvement over this standard benchmark.


Learning a binary search with a recurrent neural network. A novel approach to ordinal regression analysis

Deep neural networks are a family of computational models that are natur...

Continuous-time modeling of self-reported outcome data: a dynamic Item Response Theory model

Item Response Theory (IRT) models have received growing interest in heal...

Analyzing Clustered Continuous Response Variables with Ordinal Regression Models

Continuous response variables often need to be transformed to meet regre...

Ordinal Outcome State-Space Models for Intensive Longitudinal Data

Intensive longitudinal (IL) data are increasingly prevalent in psycholog...

Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning

Multitask deep learning has been applied to patient outcome prediction f...

Universally Rank Consistent Ordinal Regression in Neural Networks

Despite the pervasiveness of ordinal labels in supervised learning, it r...

Measuring religious morality using very limited poll responses: Implementing "big-data analytics" to small data

Opinion polls remain among the most efficient and widespread methods to ...

Please sign up or login with your details

Forgot password? Click here to reset