TY  - JOUR
AU  - Dr. P. Namratha
AU  - A. Keerthana
AU  - S.L. Harini
AU  - E. Jhansi
AU  - R. Jeevamani
AU  - V. Bhavani Shankar
PY  - 2025
DA  - 2025/04/22
TI  - A Supervised Learning Estimator And NLP Framework For Detection of Cyber Fraud in Promotional Content
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 2
AB  - The dissemination of intentionally deceptive content disguised as legitimate journalism is a global
issue undermining information accuracy and integrity. This phenomenon significantly influences public
opinion, decision-making, and voting patterns. Most false news originates on social media platforms
like Facebook and Twitter, later infiltrating mainstream media outlets such as television and radio.
These false stories often share linguistic traits, including excessive use of unsubstantiated hyperbole
and non-attributed quotes. This paper presents the findings of a study on false news detection, focusing
on a novel classifier developed using Textblob, Natural Language Toolkit (NLTK), and SciPy. The
system employs quoted attribution as a key feature within a Bayesian machine learning framework to
estimate the likelihood of an article being false. The methodology achieved a precision rate of 63.333%
in identifying false articles containing quotes. This innovative process, termed "influence mining," is
introduced as a potential tool for detecting false news and propaganda. The study details the research
process, technical analysis, linguistic features, and classifier performance. It concludes with insights
into the evolution of the current system into a comprehensive influence mining framework capable of
addressing broader disinformation challenges.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1092
DO  - 10.33425/3066-1226.1092