Pith. sign in

REVIEW

Classification of Misinformation in New Articles using Natural Language Processing and a Recurrent Neural Network

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.13534 v1 pith:GZNQEXJA submitted 2022-10-24 cs.CL cs.LG

Classification of Misinformation in New Articles using Natural Language Processing and a Recurrent Neural Network

Brendan Cunha , Lydia Manikonda This is my paper
classification cs.CL cs.LG
keywords articlesclassificationmisinformationmodelnetworkneuralrecurrentaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

This paper seeks to address the classification of misinformation in news articles using a Long Short Term Memory Recurrent Neural Network. Articles were taken from 2018; a year that was filled with reporters writing about President Donald Trump, Special Counsel Robert Mueller, the Fifa World Cup, and Russia. The model presented successfully classifies these articles with an accuracy score of 0.779944. We consider this to be successful because the model was trained on articles that included languages other than English as well as incomplete, or fragmented, articles.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.