Does AI have the Ability to Control the Fake Media?

It’s being rightly said that “Lies spread faster than the truth” and presently, we all are living in the world of post-truth. A society containing fake news and distorted truth. A place where everyone believes what they want to believe forfeiting everything else. Fake news might have become a scorching subject right now, but the truth is, there’s always been fake news.

Fake media is the reason why the internet is heading in the wrong direction whether it comes from legitimate sites or from bogus social media accounts, placing fake news in the public domains like Facebook, Whatsapp, Twitter and others is still one of the biggest problems.

Fake News By The Numbers

As per a survey conducted by Statista,42% of the social media channels are responsible for spreading the Fake News,and 30% of the fake news are being spread from bogus websites.

The survey also mentions that the current fake news market is currently fluctuating between 70.8 thousand /118 thousand clicks per month on Google Search and has over 251.2 thousand mentions on Twitter per month.

How AI Can Help us?

AI promotes the learning of pattern behaviours through pattern recognition techniques. Numerous algorithms have evolved over the last couple of years to distinguish between human and AI-generated content. These algorithms are generally developed by training/feeding several data sets which contain valid information from different sources. And based on the power of artificial intelligence, fake news can be detected by taking a cue from stories that people in the past have reported as inaccurate.

AI enhances the fact-checking process in collaboration with manforce. Together, machine learning and AI could fight digital misinformation, disinformation, and political abuse in a variety of other ways. With the help of AI, we are not programming them. Rather, they are being taught how to function as per the data that will be generated.

Since the main objective of developing such AI-based algorithms and tools is to distinguish between the real and fake datasets and while doing so one must first teach the system what these are. One needs to feed all the virtual dataset libraries in order to help AI learn Human patterns. Artificial intelligence offers some features which help us rate the authenticity of the news and identify if it is fake or not. The best way to fight against fake news is to use an automated tool. Artificial intelligence and machine learning techniques became more sophisticated and efficient when big data came into the frame.

Let’s have a deeper look into how AI can help us in preventing the spread of fake news.

Can AI-powered analytics using the identification of anomalies will hold back the spread of fake news?

Detection of Anomalies is one of the data science-based approaches in-order to filter out the fake news circulating all over the digital platform. This approach will generally include role classification to decide if a headline is aligned with the context of the document and text analysis for the writing style of the publisher. Detection of Anomalies constitutes certain phases. Let’s a brief look at it

Detecting Anomalies

This first phase of the process generally involves capturing of different posts on a trending topic for a predefined time. Next, the main phrases from the selected posts will be analyzed and thus the discrete time series will be generated. Once we have attained the time series, we will get the graph on the volume of phrases with respect to time.

The Anomalies Classification

This is one of the crucial steps involving classification of news as Fake or True. While comparing two data streams with the corresponding time information implicitly or explicitly, a measurement function is needed which provides information on the resemblance of the two data streams. This phase is termed to be as crucial because we need to distinguish between fake and true news based on the generated data sets because at this stage, there are chances that we might end up losing some information.

Once we have classified the raw anomalies, we enter into the Anomalies detection phase where a wide range of available techniques like Holt Winter and ARIMA is being used. Here all the raw data sets of both negative and positive volumes are detected for a certain time interval.

For instance, if a post starts trending on different social media platforms, then AI-powered Analytics will track the sudden rise of that post-related content, correlating the data with the source site, and will be tracked down as an anomaly. And after its classification, we can restrict the content from achieving any further momentum in the public domain.

Conclusion

We can’t stop someone from spreading fake news, but we can definitely prevent it from spreading by implementing AI-powered analytics that employs anomaly detection. When the amount of data rises by the day, so is the chance to tackle the information, and rather than having the human verification approach, we can rely on Artificial Intelligence since it has emerged as a new ray of hope for ensuring data veracity and detection of fake news.For more details feel free to contact us.