Artificial Intelligence and Fake News
Machine Learning vs. Artificial Intelligence
When talking about Artificial Intelligence, we can’t go around the fact that many people don’t truly understand the meaning of the term. More often than not artificial intelligence is used as a synonym for machine learning algorithms, and sometimes people refer to both as parallel advancements. The terms are also often deliberately confused in order to create additional excitement for advertising and sales purposes.
In fact, machine learning is only a branch of artificial intelligence. Machine learning is the field of developing computer algorithms that learn and improve automatically through experience. Artificial intelligence, on the other hand, is a very broad term that encompasses all the science of making computers perform the tasks that, until recently, required human intelligence. This means the term, “artificial intelligence,” includes different technologies that change over time.
For example, a few decades ago a regular calculator was considered a form of AI. Back then the mathematical calculation was something only humans could do. Nowadays, the calculator is one of the most common applications you will find on any computer and online. For the purpose of this article, we will use the term, “artificial intelligence,” even though most of the technology is based on machine learning algorithms.
Correlating the Linguistic Features
One of the most reliable ways to detect fake news is to examine the common linguistic features across various source’s articles. Those features include sentiment, complexity, and structure. Sources that often produce fake news are more likely to use words that are exaggerated, subjective, and full of emotion.
Another common feature of fake news is a sensational headline. As headlines are the key to capture the attention of the audience, they have become a tool of attracting the interest from a wider population. Fake news almost always uses a sensational headline since they are only partially limited by actual facts.
There is already a type of AI (machine learning algorithms) widely deployed to fight spam email. Those algorithms analyze the text and determine if the email comes from a genuine person or if it is a mass-distributed message, designed to sell something or spread a political message. Refining and adjusting those algorithms would make it possible to examine and compare the title and the text of a post with the actual content of the article. This can be another clue in assessing the post’s accuracy.
Considering some aspects of artificial intelligence make it possible to learn from past behaviors, the best approach is to train the machine learning algorithms to improve based on past articles already proven to be fake. By doing so, it is possible to determine the most apparent commonalities and develop a foundation upon which we can predict the likelihood an article is fake.
Weighing the Facts
Weighing the facts that the news is relying on is another important aspect. Artificial intelligence has developed to a stage where it is possible to examine the facts in a certain article (a Natural Language Processing engine can be used to go through the headline and the subject of the story, including the geolocation) and compare them with the facts from other sites covering the same subject. After this is done, the facts can be weighed against each other, which adds another dimension to the credibility of the story.
In crypto weighing the facts could also backfire, because there are many cases when the initial report comes from within a crypto project. When the news is spread the untrue parts of the story are also replicated. That’s why it is important to add another crucial aspect of news assessment – keeping a good track of source reputation.
Focusing on the news sources themselves is a very important aspect of assessing the news. Machine learning algorithms have already been successful in examining the accuracy and political bias of news sources.
Artificial intelligence can also be used to find correlations with a source’s Wikipedia page, which can be examined and rated based on various criteria. For example, a longer Wikipedia description of a source associated with a higher credibility. Furthermore, words like “extreme” or “conspiracy” are often used when describing an unreliable source. Another thing to look at is the source’s URL text. A less reliable source is more likely to have a URL with lots of special characters and complex subdirectories.
Keeping a good track record of news sources is also very important, as it is necessary to constantly update the source reputation. Every piece of news should influence the overall source score, for it is important to assess every situation in a quick and accurate fashion.
AI as the Creator of Fake News
One of the biggest challenges of using artificial intelligence to combat fake news is the arms race with itself. Artificial intelligence is already used to creating incredible “deepfakes”(photos and video in which someone’s face is replaced or footage is manipulated, making it appear as if the person said something he actually didn’t). Even smartphone apps are capable of this kind of manipulation, making the technology accessible to nearly anyone.
Researchers have already developed artificial intelligence capable of recognizing the manipulated image and video material. For example, through video magnification, it is possible to detect human pulse patterns to confirm whether a person is real or computer generated. This is just the beginning as technology on both sides is just going to get better and better.
Human Intelligence is Still Crucial
Humans will still play an important role in the process of news assessment. There are complex cases where humans will have to work together with technology to efficiently address the situation. The evolution of artificial intelligence should reduce the number of such situations, but it is likely human intervention will still be required for quite some time.
Audience awareness and critical thinking are additional aspects of human intelligence. People should be encouraged to always investigate information rather than simply sharing it. Sharing means giving credibility to an article. People who know you personally and trust you will more likely believe the shared post and won’t necessarily question its factuality.
The good news is that the audience exposed to factual reporting is much more likely to differentiate between real and fake information. Therefore, a lot can be done just by sharing true information as much as possible.
Developing a Solution
All in all, artificial intelligence can be a very useful tool for detecting and exposing fake news and articles based on misconceptions. This is the reason why we are developing a platform that gathers, evaluates, and enriches crypto news and information. We are constantly improving the real-time accuracy of news evaluation using the combination of community knowledge and artificial intelligence. You are welcome to read our blueprint and become a part of the BLOCKBIRD movement.