Fake news. Two words you’ll have heard a lot of over the past year or so. It’s been such a popular term that it even made Word of the Year for 2017. But there is a much more to it than being a large part of Trump’s vocabulary.
This is a ‘uge problem that needs to be addressed and tackled.
So, what is it then? Isn’t ALL news in some way a bit fake - blown out of proportion, exaggerated, politically charged? Well, yes but fake news is a whole other level, it goes beyond the sensationalism of the tabloids we all loathe. It’s a type of propaganda that has garnered much attention in recent years, and this epidemic of ‘yellow journalism’, is extremely damaging and is a massive cause for concern. Given the popularity of social media and online sources, completely bogus headlines and sensationalist content is only fueling political tensions and social divides the world over.
Interestingly, it was also one of the most popular areas in which Data Scientists are wanting to work this year according to our salary report, data science professionals want to work in detecting fake news, and finding ways in which machine learning can help deliver real and relevant content across platforms. It’s great to see so many bright minds passionate about tackling this problem.
It’s so insidious that it goes beyond the recent and much reported-on Cambridge Analytica and Facebook scandal. Social media has notoriously played a huge part in how it spreads, with Facebook coming under fire for the role fake news and its distribution across the platform has impacted socio-political spectrums. With over 2 billion users (subject to people deleting their accounts), it’s a great outlet for these fraudsters to spread their fake publications.
Propaganda, intentionally misleading information, and hoaxes plague social media platforms and dark social (encrypted, or social sharing services like WhatsApp that cannot be tracked using web analytics programs). This is causing damage on many different levels, from swaying political elections and opinions, inciting hate and even violence - as seen in India. Fake news is a very real, very present problem.
Social Media sites are faced with mounting concern and urgency for them to take responsibility for the content on their sites and remove outlets that are spouting these ‘alternate facts’.
Amidst all of the fakery and phoniness, there are companies out there looking to use artificial intelligence to tackle the problem. Having closed a seed round of $1 million in February, the London based startup Factmata are wanting to use machine learning and AI to detect fake news, hoaxes and other such terrible, misleading information and clickbait out there on various platforms.
They use natural language processing to discern what a variety of fake news would look like from a large dataset. Using this information, the algorithms are then able to detect similar content and create a trust score based on these determining factors in real time.
The fakery goes beyond articles too. In extremely poor taste, Parkland shooting survivor Emma González fell victim to image doctoring shortly after the tragedy. After appearing with classmates in Teen Vogue, where she is seen tearing up a paper gun target, the American right got hold of the imagery. This was edited it to show her ripping up the US Constitution instead, in a bid to create propaganda for their own ends. This fakery has been part of the abuse she, and her classmates have faced having strong views on gun control.
A step further are Deepfakes. They began circulating on the internet using deep learning algorithms to swap, very realistically, faces in videos. Whilst that in itself isn’t ‘bad’ inherently, true to form, this soon escalated into using this technology to swap celebrity faces into porn. It’s not just celebrities that could face severe backlash and embarrassment from this should the tech fall into the wrong hands. The poor morality behind this led to Reddit banning the r/deepfakes subreddit, and even updated their rules on explicit imagery and consent.
Delving into the technology behind creating DeepFakes on Hackernoon, Gaurav Oberoi emphasizes the accessibility of creating such videos, “anyone with hundreds of sample images, of person A and person B can feed them into an algorithm, and produce high-quality face swaps — video editing skills are not needed.” As long as you have the algorithm and the right processing power, anyone can create DeepFakes.
To combat this, DARPA are building tools to help in the fight against fake news, by creating technology that will automatically assess the integrity of images and videos to decipher any manipulation. There aren’t tools out there right now that are available commercially to do this, and with the increase in such media being used to promote propaganda and other types of fake news - it’s needed now more than ever.
With advancements in artificial intelligence, deep learning, and better processing power, this is a problem that could well get more and more sophisticated very quickly.