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Found 11 papers, 3 papers with code
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents.
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.
Examining Temporal Bias in Abusive Language Detection
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death.
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors.
Classification-Aware Neural Topic Model Combined With Interpretable Analysis -- For Conflict Classification
A large number of conflict events are affecting the world all the time.
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts.
Examining Temporalities on Stance Detection towards COVID-19 Vaccination
It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale.
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.
It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits
New events emerge over time influencing the topics of rumors in social media.
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media
The phenomenon of misinformation spreading in social media has developed a new form of active citizens who focus on tackling the problem by refuting posts that might contain misinformation.
