Hands-On Guide to Predict Fake News Using Logistic Regression, SVM and Naive Bayes Methods

There are more than millions of news contents published on the internet every day. If we include the tweets from twitter, then this figure will be increased in multiples. Nowadays, the internet is becoming the biggest source of spreading fake news. A mechanism is required to identify fake news published on the internet so that the readers can be warned accordingly. Some researchers have proposed the methods to identify fake news by analyzing the text data of the news based on the machine learning techniques. Here, we will also discuss the machine learning techniques that can identify fake news correctly.
Read more: https://analyticsindiamag.com/hands-on-guide-to-predict-fake-news-using-logistic-regression-svm-and-naive-bayes-methods/

#classification #fakenews #fakenewsclassification

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Hands-On Guide to Predict Fake News Using Logistic Regression, SVM and Naive Bayes Methods
Alec  Nikolaus

Alec Nikolaus

1596465840

The Ironic Sophistication of Naive Bayes Classifiers

Filtering spam with Multinomial Naive Bayes (From Scratch)

In the first half of 2020 more than 50% of all email traffic on the planet was spam. Spammers typically receive 1 reply for every 12,500,000 emails sent which doesn’t sound like much until you realize more than 15 billion spam emails are being sent each and every day. Spam is costing businesses 20–200 billion dollars per year and that number is only expected to grow.

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What can we do to save ourselves from spam???

Naive Bayes Classifiers

In probability theory and statistics, Bayes’ theorem (alternatively Bayes’s theoremBayes’s law or Bayes’s rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

For example, if the risk of developing health problems is known to increase with age, Bayes’s theorem allows the risk to an individual of a known age to be assessed more accurately than simply assuming that the individual is typical of the population as a whole.

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Bayes Theorem Explained

A Naive Bayes Classifier is a probabilistic classifier that uses Bayes theorem with strong independence (naive) assumptions between features.

  • Probabilistic classifier: a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to.
  • Independence: Two events are **independent **if the occurrence of one does not affect the probability of occurrence of the other (equivalently, does not affect the odds). That assumption of independence of features is what makes Naive Bayes naive! In real world, the independence assumption is often violated, but naive Bayes classifiers still tend to perform very well.

#naive-bayes-classifier #python #naive-bayes #naive-bayes-from-scratch #naive-bayes-in-python

Hands-On Guide to Predict Fake News Using Logistic Regression, SVM and Naive Bayes Methods

There are more than millions of news contents published on the internet every day. If we include the tweets from twitter, then this figure will be increased in multiples. Nowadays, the internet is becoming the biggest source of spreading fake news. A mechanism is required to identify fake news published on the internet so that the readers can be warned accordingly. Some researchers have proposed the methods to identify fake news by analyzing the text data of the news based on the machine learning techniques. Here, we will also discuss the machine learning techniques that can identify fake news correctly.
Read more: https://analyticsindiamag.com/hands-on-guide-to-predict-fake-news-using-logistic-regression-svm-and-naive-bayes-methods/

#classification #fakenews #fakenewsclassification

Arno  Bradtke

Arno Bradtke

1602846000

Fake News Detection Using Machine Learning

n this modern world, data is very important and by the 2020 year, 1.7 megaBytes data generated per second. So there are many technologies that change the world by this large amount of data. Machine learning is one of them and we are using this technology to detect fake news.

Machine Learning

Machine learning is an application of AI which provides the ability to system to learn things without being explicitly programmed. Machine learning works on data and it will learn through some data. Machine learning is very different from the traditional approach. In, Machine learning we fed the data, and the machine generates the algorithm. Machine learning has three types of learning

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning

Supervised learning means we trained our model with labeled examples so the machine first learns from those examples and then performs the task on unseen data. In this fake news detection project, we are using Supervised learning.

Check out more here

What is Fake news?

Fake news simple meaning is to incorporate information that leads people to the wrong path. Nowadays fake news spreading like water and people share this information without verifying it. This is often done to further or impose certain ideas and is often achieved with political agendas.

For media outlets, the ability to attract viewers to their websites is necessary to generate online advertising revenue. So it is necessary to detect fake news.

#fake-news #machine-learning #naive-bayes #naturallanguageprocessing #fake-news-detection

Fake News Detection Project in Python [With Coding]

Ever read a piece of news which just seems bogus? We all encounter such news articles, and instinctively recognise that something doesn’t feel right. Because of so many posts out there, it is nearly impossible to separate the right from the wrong. Here, we are not only talking about spurious claims and the factual points, but rather, the things which look wrong intricately in the language itself.

Did you ever wonder how to develop a fake news detection project? But there is no easy way out to find which news is fake and which is not, especially these days, with the speed of spread of news on social media. Still, some solutions could help out in identifying these wrongdoings.

There are two ways of claiming that some news is fake or not: First, an attack on the factual points. Second, the language. The former can only be done through substantial searches into the internet with automated query systems. It could be an overwhelming task, especially for someone who is just getting started with data science and natural language processing.

The latter is possible through a natural language processing pipeline followed by a machine learning pipeline. It is how we would implement our fake news detection project in Python. It is another one of the problems that are recognized as a machine learning problem posed as a natural language processing problem.

There are many datasets out there for this type of application, but we would be using the one mentioned here. The data contains about 7500+ news feeds with two target labels: fake or real. The dataset also consists of the title of the specific news piece.

The steps in the pipeline for natural language processing would be as follows:

  1. Acquiring and loading the data
  2. Cleaning the dataset
  3. Removing extra symbols
  4. Removing punctuations
  5. Removing the stopwords
  6. Stemming
  7. Tokenization
  8. Feature extractions
  9. TF-IDF vectorizer
  10. Counter vectorizer with TF-IDF transformer
  11. Machine learning model training and verification

#data science #fake news #fake news detection #fake news detection project #python project #python project ideas

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The essence of apposite handwashing is based around time invested in washing and the amount of soap and water used. Technically, washing hands without soap is much less effective anyway. But incase a proper handwashing support system doesn’t become possible around, the usage of Effective Hand Sanitizer will certainly help fight to reduce the number of microbes on the surface of hands efficiently, eliminating most variants of harmful bacteria to settle.

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