Fraudulent News Detection using Machine Learning Approaches

2019 Global Conference for Advancement in Technology (GCAT) Pub Date : 2019-10-01 DOI:10.1109/GCAT47503.2019.8978436

K. Rajesh, Aditya Kumar, Rajesh Kadu

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引用次数: 8

Abstract

The rampant spread of fake news on social media has skyrocketed over the years. Fake news has become a notorious devil affecting the overall demographic of the nation. Its not only regular users who are worried but also the marketers who raised concerns about the impact of fake news on trade. Online sources for news consumption are a double edged sword. Fake news is increasingly becoming a menace to our society. It is typically generated for commercial interests to attract viewers and also to collect advertising revenue. However, media giants with potentially malicious agendas have been known to produce fake news in order to influence events and policies around the world. This paper addresses a classifier that can predict whether a piece of news is legit and not just a botched up fact. The proposed model train itself using data sets having headlines of news of multiple years to predict whether a news article is true to its word. The proposed work provides a convenient hassle-free platform for everyone and aims to spread calm by decreasing rumors and misunderstandings in the society.

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使用机器学习方法检测欺诈性新闻

近年来，假新闻在社交媒体上的猖獗传播激增。假新闻已经成为影响整个国家人口的臭名昭著的魔鬼。担心的不仅是普通用户，就连营销人员也对假新闻对贸易的影响表示担忧。新闻消费的网络资源是一把双刃剑。假新闻正日益成为我们社会的威胁。它通常是为了商业利益而产生的，以吸引观众并收集广告收入。然而，众所周知，有潜在恶意议程的媒体巨头为了影响世界各地的事件和政策而制造假新闻。本文介绍了一个分类器，它可以预测一条新闻是否合法，而不仅仅是一个拙劣的事实。所提出的模型使用具有多年新闻标题的数据集来训练自己，以预测新闻文章是否真实。提议的工作为每个人提供了一个方便的无障碍平台，旨在通过减少社会中的谣言和误解来传播平静。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 Global Conference for Advancement in Technology (GCAT)

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