Fake News Detection Using Machine Learning
News is data; data is power. This sentence is very evident in today’s digital era. In the present day, most of our work and social lifestyle is getting shifted to online platforms. These platforms do hold the sway over an individual mindset by collecting their web data and targeting afterward. Political propaganda is the most common type of targeted aiming done in recent times.
What is Fake News
The definition of fake news according to the Cambridge dictionary, false stories that appear to be news, spread on the internet, or using other media, is usually created to influence political views or as a joke.
It is quite evident and clear from the above definition what the word fake news means. This term covers a vast space and is not just confined to recent issues only.
Reason For The Increased Fake News Encounter
- Rapid technological advancements.
- Social Media’s extensive reach.
- Advance tech to mask cyber footprint.
- Long term ulterior motives.
- Political.
- Easy availability of NLP on the web.
The incidents of fake news encounters are increasing at a rapid pace in this digital era. The harm potential that this fake news can deal with is way higher than an earlier threat. This harm potential is the prime reason many of the global corporations are also getting indulged in these nefarious activities.
The vast reach of fake news accredited to the extensive coverage of social media platforms. These platforms do reach every corner of the globe with minimum capital expenditure. People with a lesser degree of education are the target audience of the fake news broadcasts as they are most likely to believe what they see or hear rather than doing research themselves. A great example of this is evident in the Indian subcontinent where WhatsApp has an extensive reach with more than 400 million active users and is the primary source for the transfer of fake news in the form of fake news and memes.
Political parties are responsible for this mayhem as they are the ones running IT cells for the propaganda tasks. These IT cells tasked to tarnish the image of the opposition political parties and enhance their party image at the same time are a national threat.
NLP programs are available across the web for anyone to download. These are the programs used to deploy, neural-fake news. Neural Fake News is hoax news developed by an AI, which seems real to all extent.
Strength Of Fake News
The fake news is no longer a human process where it is limited to the hoax message forwards. High-end tech and equipment are at the center of the whole next-gen fake news fiasco. Machine Learning plays a vital role in the creation and distribution of fake news in this modern era.
ML equipped AI can have access to tons of digital data which the user generates each day while using the web. This data can later be processed to create a profile and target the user. After a while, the ML equipped AI will be self-sufficient to function without any human intervention. It will be the Skynet moment from the Terminator franchise.
Deepfakes
Deepfake is the greatest threat to humankind to date. It implements computer technology to replace a person’s face and video another with such a precision that it is becoming almost impossible to detect what is real and what is fake. Just imagine a leader of a superpower to announce war with another country. Hate speeches served by the leaders of the state etc. These incidents have the potential to destabilize a nation.
Leaders of the states are the easy target because their video and audio data is available over the web and the potential that they hold. The power that they hold is the key reason why their deepfakes have the most accountability.
ML To The Rescue?
Yup, the one responsible is the one that can fix it. Using ML is our best bet to counter these threats. ML can be trained over time to detect fake news and remove it from the web efficiently. ML, used as the cybersecurity guards, will be responsible for creating a haven. ML will be instrumental in the cyberspace security task. This security measure will help the enforcement agencies to keep a check on nefarious activities with ease.
ML is also a cost-effective countermeasure against the fake news issue.
Sure there is a high upfront cost, but after that phase, the running expenditure is a bare minimum.
At present, it is a bit difficult to trace if any news is in reality, machine-generated. There can be a lot of authentic tasks where the result is machine-generated. For example, Grammarly uses some text correcting tech which shows the result as machine-generated.
Conclusion
We have a long way to go in the fake news detection technology. Over time, data the ML will be way more efficient in the detection and removal of the fake news over the web.
Stricter rules and regulations are also needed, and will make some individual refrain from this illicit activity. Global Cooperation is of utmost priority. Through Global Cooperation, it will be easy to apprehend the guilty. It will also decrease the chance of global tension escalations.
Multinational Cooperation will bring down the cost of research and development of ML. The modern tech always had some implication when revealed at first. Sometime later the countermeasures are up to the mark. ML is a boon in which nefarious individuals are trying to turn in a bane.
However, it is just a matter of perspective. Someone’s boon might be another’s bane and vice versa.