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How to Fight Fake News

By Katie Lee on 2018-10-18

Researchers are currently working to create an algorithm that selects for and removes fake news from the internet and the data so far has proven to be a great success.

The media is going absolutely nuts over the whole fake news scandal shadowing the United States and even other countries all over the world. With so many political alignments, there are so many opinions to consider. In fact, for the same topic, 3 different alignments (left, right and center generally) displayed the information differently. Perhaps the left would omit some information that the right would not or the right would present the information in a negative connotation whereas the left would convey their point in a neutral or positive connotation. Fake news is often thought to be simply news that is incorrect in any way. But, in reality fake news is this as well as any strategic attempt to mislead an audience by omitting information or giving an audience false data in order to mislead them from the truth.

How does this matter in the context of computer science though? Well, as of last february, researchers at UCR have been attempting to create an algorithm that selects for fake news on the internet.

This was as a response to the government catching 13 Russians who were spreading fake news to the American people in regards to the presidential election. The algorithm is designed to root out any fake news prior to them being launched and causing a lot of unnecessary chaos. Further, these fake news sources can be recorded in order to provide evidence in the event of prosecution.

Evangelos E. Papalexakis, the lead researcher of the project, said “"Previous studies have provided useful insights on the propagation of an article in a social network. However, detection based solely on this poses the risk of a fake news article 'infecting' a number of social media users before it is detected. Instead, our work aims at the early detection of such articles, especially in cases where we have no external knowledge regarding the validity and veracity of any article."

Current human monitoring involves the monitor using common sense as well as general human decency rules to screen for posts to remove. Such rules include the use of all caps (known as shouting) as well as any utilization of hateful remarks or comments towards any one group of people. Human monitors also check for credible sources in the event that they find a very vague or unlikely “fact” in the post.

As we know, algorithms are solely based off of mathematical ideas and concepts. So how do we select for fake news using one?

The Multi-Aspect Data Lab essentially monitors input from various groups of people connected to each other on various devices. The algorithm does this through tensor decompositions where a tensor is basically a multidimensional object such as a pyramid or cube. The input is captured as one of these objects such that the algorithm can mathematically understand what is going on.

Papalexakis said "The tensor decomposition techniques we develop are able to capture nuanced patterns that successfully identify different categories of fake news, without using any external knowledge about the validity of any particular article."

The algorithm is able to differentiate different events through this tensor decomposition so it can find the fake news. According to a recent study on this algorithm, it found approximately 80% of fake news in a controlled setting.

Papalexakis is currently pursuing a partnership with one or a few of the major tech companies in order to help further his research and take his algorithm into the real world.

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