Checking Fact Worthiness using Sentence Embeddings

TL;DR

This paper explores automating fact-checking by leveraging Sentence-BERT embeddings, topic modeling, and sentiment analysis to improve check-worthiness detection, demonstrating enhanced evaluation metrics on CLEF-2019 data.

Contribution

It introduces a novel combination of Sentence-BERT embeddings, topic modeling, and sentiment analysis for fact-checking, showing improved performance over previous methods.

Findings

Improved MAP, MRR, R-Precision, and Precision@N scores with proposed techniques.

Sentence-BERT embeddings enhance check-worthiness detection accuracy.

Combining topic modeling and sentiment analysis further boosts results.

Abstract

Checking and confirming factual information in texts and speeches is vital to determine the veracity and correctness of the factual statements. This work was previously done by journalists and other manual means but it is a time-consuming task. With the advancements in Information Retrieval and NLP, research in the area of Fact-checking is getting attention for automating it. CLEF-2018 and 2019 organised tasks related to Fact-checking and invited participants. This project focuses on CLEF-2019 Task-1 Check-Worthiness and experiments using the latest Sentence-BERT pre-trained embeddings, topic Modeling and sentiment score are performed. Evaluation metrics such as MAP, Mean Reciprocal Rank, Mean R-Precision and Mean Precision@N present the improvement in the results using the techniques.