We leveraged LLaMA, utilizing its ability to evaluate the relevance of retrieved claims within a retrieval-based fact-checking framework. This approach aimed to explore the impact of large language models (LLMs) on retrieval tasks and assess their effectiveness in enhancing fact-checking accuracy. Additionally, we integrated Jina embeddings v2 and the MPNet multilingual sentence transformer to filter and rank a set of 500 candidate claims. These refined claims were then used as input for LLaMA, ensuring that only the most contextually relevant ones were assessed.

Today, news media organizations regularly engage with readers by enabling them to comment on news articles. This creates the need for comment moderation and removal of disallowed comments – a time-consuming task often performed by human moderators. In this paper we approach the problem of automatic news comment moderation as classification of comments into blocked and not blocked categories. We construct a novel dataset of annotated English comments, experiment with cross-lingual transfer of comment labels and evaluate several machine learning models on datasets of Croatian and Estonian news comments. Team name: SuperAdmin; Challenge: Detection of blocked comments; Tools/models: CroSloEn BERT, FinEst BERT, 24Sata comment dataset, Ekspress comment dataset.

This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.