Have you ever read something online and shared it among your networks, only to find out it was false?
As a software engineer and computational linguist who spends most of her work and even leisure hours in front of a computer screen, I am concerned about what I read online. In the age of social media, many of us consume unreliable news sources. We’re exposed to a wild flow of information in our social networks — especially if we spend a lot of time scanning our friends’ random posts on Twitter and Facebook.
My colleagues and I at the Discourse Processing Lab at Simon Fraser University have conducted research on the linguistic characteristics of fake news.
The effects of fake news
A study in the United Kingdom found that about two-thirds of the adults surveyed regularly read news on Facebook, and that half of those had the experience of initially believing a fake news story. Another study, conducted by researchers at the Massachusetts Institute of Technology, focused on the cognitive aspects of exposure to fake news and found that, on average, newsreaders believe a false news headline at least 20 percent of the time.
For example, during the 2016 election in the United States, an astounding number of U.S. citizens believed and shared a patently false conspiracy claiming that Hilary Clinton was connected to a human trafficking ring run out of a pizza restaurant. The owner of the restaurant received death threats, and one believer showed up in the restaurant with a gun. This — and a number of other fake news stories distributed during the election season — had an undeniable impact on people’s votes.
It’s often difficult to find the origin of a story after partisan groups, social media bots and friends of friends have shared it thousands of times. Fact-checking websites such as Snopes and Buzzfeed can only address a small portion of the most popular rumors.
The technology behind the internet and social media has enabled this spread of misinformation; maybe it’s time to ask what this technology has to offer in addressing the problem.
Giveaways in writing style
Recent advances in machine learning have made it possible for computers to instantaneously complete tasks that would have taken humans much longer. For example, there are computer programs that help police identify criminal faces in a matter of seconds. This kind of artificial intelligence trains algorithms to classify, detect and make decisions.
When machine learning is applied to natural language processing, it is possible to build text classification systems that recognize one type of text from another.
During the past few years, natural language processing scientists have become more active in building algorithms to detect misinformation; this helps us to understand the characteristics of fake news and develop technology to help readers.
One approach finds relevant sources of information, assigns each source a credibility score and then integrates them to confirm or debunk a given claim. This approach is heavily dependent on tracking down the original source of news and scoring its credibility based on a variety of factors.
A second approach examines the writing style of a news article rather than its origin. The linguistic characteristics of a written piece can tell us a lot about the authors and their motives. For example, specific words and phrases tend to occur more frequently in a deceptive text compared to one written honestly.
Spotting fake news
Our research identifies linguistic characteristics to detect fake news using machine learning and natural language processing technology. Our analysis of a large collection of fact-checked news articles on a variety of topics shows that, on average, fake news articles use more expressions that are common in hate speech, as well as words related to sex, death and anxiety. Genuine news, on the other hand, contains a larger proportion of words related to work (business) and money (economy).
This suggests that a stylistic approach combined with machine learning might be useful in detecting suspicious news.
Our fake news detector is built based on linguistic characteristics extracted from a large body of news articles. It takes a piece of text and shows how similar it is to the fake news and real news items that it has seen before. (Try it out!)
The main challenge, however, is to build a system that can handle the vast variety of news topics and the quick change of headlines online, because computer algorithms learn from samples and if these samples are not sufficiently representative of online news, the model’s predictions would not be reliable.
One option is to have human experts collect and label a large quantity of fake and real news articles. This data enables a machine-learning algorithm to find common features that keep occurring in each collection regardless of other varieties. Ultimately, the algorithm will be able to distinguish with confidence between previously unseen real or fake news articles.
This article is republished from The Conversation under a Creative Commons license.
Lev Parnas has Trump ‘unnerved’: ex-FBI official says the president doesn’t know what he ‘has up his sleeve’
President Donald Trump is "very nervous" about what Lev Parnas may have on him, a former top FBI official suggested on MSNBC on Friday.
Former FBI Assistant Director for Counterintelligence was interviewed by MSNBC's Peter Alexander.
The host played clips of Trump denying any relationship with Parnas.
"Well, I don't know him, I don't know Lev Parnas, other than I guess I had pictures taken -- which I do with thousands of people," Trump argued. "I don't know him at all, don't know what he's about, don't where he comes from. I can tell you this -- I don't know him. I don't believe I've ever spoken to him. I don't believe I've ever spoken to him."
Trump is now feuding with Iran’s Supreme Leader on Twitter: ‘Make Iran Great Again!’
President Donald Trump is now replying directly to Iran Supreme Leader Ayatollah Ali Khamenei on Twitter.
I Friday, Khamenei tweeted a screenshot of a Trump tweet, with a message of his own.
"The villainous US govt repeatedly says that they are standing by the Iranian ppl. They lie. If you are standing by the Iranian ppl, it is only to stab them in the heart with your venomous daggers," Khamenei posted.
"Of course, you have so far failed to do so, & you will certainly continue to fail," he added.
Trump retweeted the message, adding his own argument.
‘Contempt of Congress is illegal’ declared one of Trump’s newest attorneys – just days before the House voted to impeach
One of the lesser-known names on the new list of lawyers President Donald Trump approved to defend him during the Senate impeachment trial delivered a damning remark last month – damning for President Trump, that is.
"Contempt of Congress is illegal," said Robert Ray, who served as the final Whitewater independent counsel after Ken Starr.
The Washington Post's Paul Waldman and Greg Sargent report Ray made the stunning remark – one of the Articles of Impeachment essentially is contempt of Congress, or technically, obstruction of Congress – to The Daily Signal, a right wing website run by the conservative Heritage Foundation.