Pith. sign in

REVIEW 1 cited by

Methods of Informational Trends Analytics and Fake News Detection on Twitter

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 2204.04891 v1 pith:RISUIDSX submitted 2022-04-11 cs.CL cs.AIcs.SI

Bohdan M. Pavlyshenko This is my paper
classification cs.CLcs.AIcs.SI
keywords beennewstrendstwitteranalysisanalyticsconsidereddetection
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In the paper, different approaches for the analysis of news trends on Twitter has been considered. For the analysis and case study, informational trends on Twitter caused by Russian invasion of Ukraine in 2022 year have been studied. A deep learning approach for fake news detection has been analyzed. The use of the theory of frequent itemsets and association rules, graph theory for news trends analytics have been considered.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model

    cs.CL 2025-08 reject novelty 3.0 of 10

    A fine-tuned Mistral 7B model produces graph and text summaries, sentiment scores, and stacked meta-summaries of crypto news, but the paper reports no quantitative evaluation.

Pith tools