There is a rapid increase in yellow journalism (fake news) and this is mainly because the reach and speed of social media networks make it easy for fake stories to spread before they can be deflated. Though with social media the information we need is just a click away, there is also a lot of disinformation on products, religion, communities, etc., on the internet that spreads more through social media, print, and news channels. Thus with the spread of fake news the demand for fake news detection is increasing. Can artificial intelligence help in detecting fake news?
Social media puts no restraints on the contents that are published or posted. Most of the time the sources of the information are not verified. This leads to the spread of fake news. As it is difficult to determine the source of fake information, it makes it harder to get access to the accuracy.
However, AI-based models and machine learning models are becoming more advanced and effective in detecting fake news. Soon, artificial intelligence and machine learning will help in determining whether the news is real or fake. Natural language processing (NLP) tools are receiving more attention for the detection of fake news.
Online media is increasingly growing, in such cases, it is difficult to detect fake news manually. Therefore automated identification of fake news seems to be the only way. Till now there have been text-based methods to identify fake news but they could not provide the desired result because of the increasing number of users on social media.
In the coming future, we will be able to see AI detecting fake news. Machine learning and highly advanced artificial intelligence models are being continuously used by researchers and industrialists to design automated fake news detection-based models. AI-based models are bringing transformation in almost every walk of life. There is a recent development in NLP that promises to identify fake news.
Social media uses machine learning algorithms that have no context and therefore stand the problem of making errors. AI-based tools would include model classification to identify whether the headline is linked with the article body, text processing to examine the author’s writing style, and image forensics to identify photoshop use. To identify the reliability of an article, algorithms could extricate even relatively simple data features, like image size, readability level, and the ratio of reactions versus shares on Facebook.
The fake news issue can also be identified by focusing on oddities. When a social media algorithm starts pushing a trending post or article to the top, if AI-powered analytics tracked the sudden stream of a new topic, correlating this data with the source site or Facebook page, it would evolve as an obvious oddity and be paused from gaining any further momentum until a human at Facebook or Google can validate the specific item, rather than needing a human review of all topics.
The power of this application of AI-powered analytics to spot oddities, far faster than humans could, can be used when working with thousands or millions of metrics. Real-time oddity identification can catch even the most subtle, yet important, deviations in data.
Thus AI-based models can be used for fact-checking. The advent of AI has brought many new changes, there is nothing that can not be done with the help of Artificial Intelligence. It is indeed AI that boosts the increase of fake news but it is only AI that can prevent the spread of fake news. Continuous development in AI and the evolution of modern technologies can surely result in fruitful detection of fake news.