Text Similarity Using Word Embeddings to Classify Misinformation

Abstract

Fake news is a growing problem in the last years, especially during elections. It's hard work to identify what is true and what is false among all the user generated content that circulates every day. Technology can help with that work and optimize the fact-checking process. In this work, we address the challenge of finding similar content in order to be able to suggest to a fact-checker articles that could have been verified before and thus avoid that the same information is verified more than once. This is especially important in collaborative approaches to fact-checking where members of large teams will not know what content others have already fact-checked.

References in corpus (5)

• Distributed Representations of Words and Phrases and their Compositionality
• word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
• Automatic Detection of Fake News
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• Investigating Political Participation and Social Information Using Big Data and a Natural Experiment

Cited by in corpus (1)

• Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp