Automated Assessment of Deliberative Quality in Online Discussions Using Adapter Models and Crowd Annotations
A single deliberative quality score (AQuA) is calculated by combining predictions from adapter models trained on various aspects of deliberation and insights from both expert and non-expert annotations.
Examining Limitations of Computational Rumor Detection Models Trained on Static Datasets
Rumor detection models struggle to detect unseen rumors due to overreliance on source post information and lack of consideration for contextual data.
Navigating Prompt Complexity for Zero-Shot Classification in Computational Social Science
LLMs struggle to match smaller models in zero-shot settings, prompting strategies impact accuracy significantly.
Can Large Language Models Distill Text? Investigating LLMs' Ability to Remove Forbidden Variables
Large language models struggle to effectively distill text by removing forbidden variables while preserving other semantic content, posing challenges for computational social science investigations.
Social Orientation: A New Feature for Dialogue Analysis
Social orientation tags improve dialogue outcome prediction and explainability.
Cultural Evolution in Large Language Models: Simulating Cultural Dynamics
Leveraging Large Language Models for simulating cultural evolution provides insights into human and machine-generated culture dynamics.
Social Orientation: Predicting Dialogue Outcomes with Social Orientation Tags
The author introduces the use of social orientation tags to predict and explain dialogue outcomes, demonstrating their effectiveness in improving task performance.
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