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Running Head: FAKE NEW DETECTION USING MACHINE LEARNING IN PYTHON 1
FAKE NEW DETECTION USING MACHINE LEARNING IN PYTHON         3
FAKE NEW DETECTION USING MACHINE LEARNING IN PYTHON
Table of Contents
Introduction    3
Proposed Research Problem and Some Background Information    3
Fake New Detection    3
Fake New Detection using TFIDF Vectorizer    3
TF (Term Frequency):    3
IDF (Inverse Document Frequency):    4
Passive Aggressive Classifier:    4
Natural Language Processing:    4
Machine Learning Classification:    4
Findings    4
Data Sets    5
LIAR:    5
Fake News Net:    5
Conclusion    5
References    7
Introduction
We rely greatly on online sources for news in today’s day and age, but not all sources are reliable. There is a great inflow of fake news from various sources, thus the need to differentiate between the genuine news sources and the fake ones is extremely high. One’s perspective about life and society relies heavily on the news, to which we are exposed.
To stop the plague of fake news from consuming people’s minds we should use every resource available and today one of the greatest resources we have is the advancement in data science and the use of Python. It is imperative that we use machine learning and Python to the best of its abilities to quell the endless flow of fake news.
Proposed Research Problem and Some Background Information
Fake New Detection
Fake news is a very common term used today, which implies the news that is shared from questionable sources on web-based platforms or on social media, which is sensationalized or in a number of cases, which outright lies. This kind of news generally helps either one or another political ideology. Sometimes, such news is also spread for spreading an untrue image of products (De Beer & Matthee, 2020).
These sources tend to be highly radicalized and consumers of such fake news start to be exposed to these radicalized lies. That is a very dangerous reality, which needs to be fought against. No matter what, one’s political ideology is or...