Estimating the Prevalence of Toxic Comments on Social Media Platforms Using a Calibrate-Extrapolate Framework

แนวคิดหลัก

The core message of this article is to introduce a "Calibrate-Extrapolate" framework for efficiently processing and analyzing content to estimate the prevalence of toxic comments on social media platforms, using a pre-trained black box classifier.

บทคัดย่อ

The article introduces a "Calibrate-Extrapolate" framework for prevalence estimation using a pre-trained black box classifier.
Calibration Phase:

A limited sample of data is selected and ground truth labels are obtained through manual annotation.
A calibration curve is estimated, mapping the classifier outputs to calibrated probabilities.
The base dataset's joint distribution between classifier outputs and ground truth labels is inferred.
Extrapolation Phase:

The target dataset's classifier outputs are obtained.
Stability assumptions are made to link the base and target joint distributions.
Two techniques are discussed - assuming stable calibration curve or stable class-conditional densities.
The linked joint distribution is used to estimate the prevalence in the target dataset.
The framework is applied to estimate the weekly prevalence of toxic comments on news topics across Reddit, Twitter/X, and YouTube in 2022, using Jigsaw's Perspective API as the black box classifier. The results show consistently higher prevalence of toxic comments on YouTube compared to Twitter/X and Reddit.
The article also conducts simulation experiments to analyze the impacts of classifier predictive power and violations of stability assumptions on the accuracy of prevalence estimates.

สถิติ

The dataset contains over 15,000 hard news URLs with at least 10 comments each on Reddit, Twitter/X, and YouTube in 2022.
On average, the dataset includes 5,631 distinct comments per day on Reddit, 5,355 on Twitter/X, and 4,271 on YouTube.
A calibration sample of 1,144 Reddit comments, 1,154 Twitter/X replies, and 1,162 YouTube comments from August 2021 were manually annotated for toxicity.

คำพูด

"Measuring the frequency of certain labels within a data sample is a common task in many disciplines. This problem, generally called "prevalence estimation" or "quantification", has a wide range of real world applications, from quantifying the number of infected COVID-19 patients in a country (Sempos and Tian 2021), to automated accounts in a social platform (Yang et al. 2020), and to anti-social posts in an online community (Park, Seering, and Bernstein 2022)."
"The Calibrate-Extrapolate framework is broadly applicable to many real world settings. It is also flexible because researchers can still customize design elements in some steps."

ข้อมูลเชิงลึกที่สำคัญจาก

Calibrate-Extrapolate

by Siqi Wu,Paul... ที่ arxiv.org 04-03-2024

https://arxiv.org/pdf/2401.09329.pdf

สอบถามเพิ่มเติม

How can the Calibrate-Extrapolate framework be extended to handle more than two classes or continuous target variables?

The Calibrate-Extrapolate framework can be extended to handle more than two classes by adapting the prevalence estimation techniques to accommodate multiple classes. For example, in the calibration phase, the calibration curve can be adjusted to map the classifier outputs to probabilities for each class. This would involve estimating separate calibration curves for each class. In the extrapolation phase, the joint distribution between the classifier outputs and ground truth labels can be expanded to include multiple classes, allowing for the estimation of prevalence for each class in the target dataset.
For continuous target variables, the framework can be modified to estimate the joint distribution between the classifier outputs and the continuous target variable. This would involve using regression techniques to model the relationship between the classifier scores and the continuous target variable in both the calibration and extrapolation phases. The stability assumptions would need to be redefined to account for the continuous nature of the target variable, ensuring that the estimation techniques are appropriate for handling continuous data.

What are the potential biases and limitations in the manual annotation process used to obtain ground truth labels for the calibration sample?

The manual annotation process used to obtain ground truth labels for the calibration sample may introduce biases and limitations that can impact the accuracy of the prevalence estimates. Some potential biases and limitations include:

Annotation Bias: The annotations provided by the MTurk workers may be subjective and influenced by their personal beliefs or perspectives. This could lead to inconsistencies in labeling toxic comments, affecting the quality of the ground truth labels.

Labeling Errors: Human annotators may make mistakes or misinterpret the guidelines provided for labeling toxic comments. This can result in inaccuracies in the ground truth labels, leading to biased prevalence estimates.

Limited Sample Size: The size of the calibration sample may not be representative of the entire dataset, especially if it is small. This can introduce sampling bias and affect the generalizability of the prevalence estimates to the larger population.

Worker Variability: Different MTurk workers may have varying levels of experience and expertise in labeling toxic comments. This variability can lead to inconsistencies in the annotations and impact the reliability of the ground truth labels.

Annotation Fatigue: Annotating a large number of comments can lead to annotation fatigue, causing workers to rush through the task or become less attentive to the nuances of each comment. This can result in lower quality annotations and biased ground truth labels.

How can the insights from the simulation experiments be used to develop more robust prevalence estimation techniques that can handle a wider range of dataset shifts between base and target data?

The insights from the simulation experiments can be leveraged to develop more robust prevalence estimation techniques by:

Model Adaptation: Using the findings from the experiments, prevalence estimation techniques can be adapted to handle different types of dataset shifts, such as intrinsic or extrinsic data generation processes. Techniques can be tailored to account for stability assumptions based on the specific characteristics of the data generating process.

Algorithm Optimization: The simulation results can guide the optimization of prevalence estimation algorithms to be more resilient to violations of stability assumptions. This may involve incorporating adaptive mechanisms that adjust to changes in the dataset distribution or classifier performance.

Sampling Strategies: Insights from the experiments can inform the development of more effective sampling strategies for collecting calibration samples and ground truth labels. Strategies like Neyman allocation can be refined to improve the efficiency and accuracy of prevalence estimates.

Error Analysis: Understanding the errors and limitations identified in the simulation experiments can help in developing error mitigation strategies and robustness checks for prevalence estimation techniques. This can involve implementing validation procedures to detect and correct biases in the estimation process.

By incorporating these insights into the development and refinement of prevalence estimation techniques, researchers can create more reliable and adaptable methods for estimating the prevalence of target variables in unlabeled datasets.