Can AI Combat Fake News?
The “fake news” phenomenon may have captured the imagination of Americans during the 2016 presidential campaign and the subsequent investigation into Russia’s attempts to turn over the election to Donald Trump by posting false news on Facebook.
The truth is that fake or fake news has been a tool for spreading propaganda and conspiracy theories for some time and many for years before the 2016 election.
However, after the election it became a political and social problem, and bad Facebook became the flagship of the sites covered by the plan.
Recently, the social media company admitted its mistakes and tried to correct it with its followers. Now he reports fake news articles that are sent to Facebook members through their news feed. To do this, he uses AI.
The company uses AI to identify words or phrases that may indicate that the article is fake. The data for this task is based on articles that Facebook members individually label as fake.
Currently, the technology uses four methods of detecting fake news. They contain:
Notice the web pages. The first to use this technique was Google. It uses facts to evaluate websites. Obviously, the rating of the site is a constant problem. But like Google, technology has skyrocketed.
Weigh the facts. This method uses natural language processing mechanisms to change the subject matter of stories. AI, which uses other models, will find out whether other sites report the same facts.
Predict reputation. This method is based on artificial intelligence, which uses predictive analytics and machine learning to predict the website’s reputation with a number of features, including the Domain domain name and the position of the Internet.
Discover sensational words. Fake news advocates used sensational headlines to attract the attention of potential audiences. This method detects false headlines and reports them through keyword analysis.
The actual detection of these types of AI items is a complex task. This is, of course, an analysis of big data, but also its reliability. Its identification is actually involved in determining the reliability of the data. This can be done with the weighing method. What if fake news appears on hundreds of sites at once? In these circumstances, the use of fact-finding techniques can force AI to determine that history is true. The use of reputation forecasting in combination with fact-finding may help, but there may still be problems. For example, websites of reliable news sources that don’t spend time checking a story can pick it up, assuming it’s true.
It is clear that the use of AI to identify these items requires further development. A number of organizations are involved in improving AI capabilities. One such institution is the University of West Virginia.
Reed College of Media, in collaboration with the Benjamin M. Statler College of Engineering and Mineral Resources at the University of West Virginia, developed a course on the use of AI to identify fake news articles.
Senior students, undergoing elective computer science, work in a team to develop and implement their own artificial intelligence programs.
Another group, known as the Fake News Challenge, is also looking for a way to enable AI to successfully combat fake news. It is a massive organization of more than 100 volunteers and 71 academic and industry teams working on fake news. He develops tools to help people track and detect fake news.
As organizations seek to improve AI to find these stories, various tools are available to give them a chance. They include:
Spike, which identifies and predicts devastating and viral stories and uses big data to predict what will stimulate interaction.
Hoaxy is a tool that helps users identify sites with fake news.
Snoopey, the website that identifies fake news articles.
CrowdTangle, a tool for monitoring social content.
Meedan, a tool for checking news online.
Google Trends, which tracks search queries.