Article by Materahub
Fake news are one of the most prominent and dangerous phenomena on social media. Since they are spreading to traditional media as well, having a reliable way of identifying fake news is more relevant than ever.
Internet is an open source where you can find everything you need, sometimes it can be misleading.
Articles, websites, videos, social media posts can try to influence people, they might represent a form of cyberwarfare between states, they might be aimed at raising someone’s profile and influence, or discrediting their opponents.
The first step is identifying who benefits from them, who is criticized, etc. For this reason, linguistics plays a key role when it comes to identifying patterns in the language used to flag it as potentially dubious. The linguistic characteristics of a written piece can tell us a lot about the authors and their motives. Purveyors of disinformation can be caught out by the particular words they use, according to a new research.[1]
It is important to identify fake news, and for the past few years, researchers have been trying to work out what the linguistic characteristics of fake news are. Today computers are able to identify patterns in the language used, and some studies recently found that fake news articles use more words related to sex, death and anxiety. The emotional language is the one used to attract the audience. Furthermore, a group of researchers analysed the relationship of various grammatical categories to fake news, for example superlatives like “most” and “worst” or objectives like “brilliant” and “terrible”. They also noted that propaganda tends to use abstract generalities like “truth” and “freedom”, and intriguingly showed that use of the second-person pronoun “you” was closely linked to fake news.
The language of fakery, with its powerful subjective statements and focus on anxiety, and the style, which often involves “adversarial, emotional, patriotic and abrasive speech”. Specifically, fabricated news articles display significantly higher levels of anger and disgust and substantially lower levels of “joy” in their article body than real news stories. Although research into the impact of specific emotions, such as disgust, is rare, a number of studies have considered negative emotions and their impact on message processing (Dens et al., 2008). [2]
[1] https://blog.bitext.com/language-to-detect-fake-news
[2] https://www.diva-portal.org/smash/get/diva2:1395546/FULLTEXT01.pdf