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Found 10 papers, 2 papers with code
Large Language Models for Data Annotation: A Survey
Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation.
Estimating Topic Exposure for Under-Represented Users on Social Media
To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors.
Text Transformations in Contrastive Self-Supervised Learning: A Review
Contrastive self-supervised learning has become a prominent technique in representation learning.
Domain Adaptive Fake News Detection via Reinforcement Learning
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.
A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily.
Causal Inference for Time series Analysis: Problems, Methods and Evaluation
In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.
"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?
Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.
Causal Adversarial Network for Learning Conditional and Interventional Distributions
The LGN is a GAN-based architecture which learns and samples from the causal model over labels.
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
In this work, models that aim to answer causal questions are referred to as causal interpretable models.
