Our Fast Lifestyles Will Enable the Evolution of Fake News to Destroy Us.

A Culture of Division and Content Shock

There is a dividing tension in our culture, birthed from a media that preys on a population quick to believe striking headlines, especially if those headlines fit into our idea of the world. This phenomenon is known as ‘confirmation bias.’ As a society, we berate each other behind our screens, addicted to our quick-to-accuse and quick-to-defend tendencies. We protect our false sense of justice often built on fake news and half truth headlines.

We also live in a cyber age where technological advancements are exhausting to follow and new information rapidly floods the internet. In fact, every 60 seconds, there is an average of 350 million tweets, 294 billion emails sent, 54 million WhatsApp messages sent, and 500 hours of material uploaded on YouTube. With this massive amount of data, we are paralyzed with our ability to meaningfully process information.

This divisive tension, paired with the overwhelming surplus of data, can, and will be, exploited and further reinforced by the coming generation of fake news — deepfakes.

Daunting Deep Learning Fakes

Deepfakes are synthetically generated media forged to impersonate an individual’s likeness. Essentially, they are produced clips that present someone as behaving in a way that they are not. This type of synthetic media is not new, but only recently has it evolved to be frighteningly realistic through the use of deep learning techniques and by combining technologies such as face re-enactment and voice synthesizers. Hence, the term ‘deepfake’ was coined: to be a combination of ‘deep learning’ and ‘fake’. Through the use of artificial intelligence, anyone can produce persuasive replicas by training on a large set of images and videos of a specific person where the model will learn and reproduce their behaviors.

#machine-learning #deepfakes #artificial-intelligence #data-science #technology #deep learning

What is GEEK

Buddha Community

Our Fast Lifestyles Will Enable the Evolution of Fake News to Destroy Us.

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

Our Fast Lifestyles Will Enable the Evolution of Fake News to Destroy Us.

A Culture of Division and Content Shock

There is a dividing tension in our culture, birthed from a media that preys on a population quick to believe striking headlines, especially if those headlines fit into our idea of the world. This phenomenon is known as ‘confirmation bias.’ As a society, we berate each other behind our screens, addicted to our quick-to-accuse and quick-to-defend tendencies. We protect our false sense of justice often built on fake news and half truth headlines.

We also live in a cyber age where technological advancements are exhausting to follow and new information rapidly floods the internet. In fact, every 60 seconds, there is an average of 350 million tweets, 294 billion emails sent, 54 million WhatsApp messages sent, and 500 hours of material uploaded on YouTube. With this massive amount of data, we are paralyzed with our ability to meaningfully process information.

This divisive tension, paired with the overwhelming surplus of data, can, and will be, exploited and further reinforced by the coming generation of fake news — deepfakes.

Daunting Deep Learning Fakes

Deepfakes are synthetically generated media forged to impersonate an individual’s likeness. Essentially, they are produced clips that present someone as behaving in a way that they are not. This type of synthetic media is not new, but only recently has it evolved to be frighteningly realistic through the use of deep learning techniques and by combining technologies such as face re-enactment and voice synthesizers. Hence, the term ‘deepfake’ was coined: to be a combination of ‘deep learning’ and ‘fake’. Through the use of artificial intelligence, anyone can produce persuasive replicas by training on a large set of images and videos of a specific person where the model will learn and reproduce their behaviors.

#machine-learning #deepfakes #artificial-intelligence #data-science #technology #deep learning

Apps For Short News – The Trend Is About To Arrive

Short news apps are the future, and if they will play a defining role in changing the way consumers consume their content and how the news presenters write their report.

If you want to build an app for short news then you can check out some professional app development companies for your app project As we head into the times where mobile applications and smartphones will be used for anything and everything, the short news applications will allow the reader to choose from various options and read what they want to read.

#factors impacting the short news apps #short news applications #personalized news apps #short news mobile apps #short news apps trends #short news apps

Alpha Evolution Vital Keto Advanced Formula

Alpha Evolution Vital Keto is a natural and healthy fitness supplement for the body. This is the kind of supplement that people can use to make sure that they get the perfect fit. This supplement makes use of ketosis and the metabolism for getting the fat to be burnt away.

https://sites.google.com/view/alphaevolutionketocanadareview/home
https://sites.google.com/view/alpha-evolution-vital-keto-buy/home
https://sites.google.com/view/alphaevolutionvitalketoorder/
https://sites.google.com/view/vitalketoadvancedformula/home

#alpha evolution keto #alpha evolution vital keto #alpha evolution vital keto reviews #alpha evolution keto canada #alpha evolution vital keto canada #alpha evolution reviews

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