How many times have you scrolled through a post or article and wondered, "Is what I'm reading going to be true? How reliable is the source? How to recognize fake news?" More importantly, how many times have you scrolled through a post or article and not asked yourself?
In fact, when it comes to information, doubting what we read is the best thing we can do. Sure, it's not the most comfortable choice, but a difficult question is rarely followed by an easy answer. A Statista research of 2019 highlighted that 98.6% of Italians believe it is difficult to distinguish real news from fake news on the web: moreover, it is precisely social networks that inspire less confidence in Italians.
5 practical tips for distinguishing quality data from trashy data
So, in the end, how to recognize fake news?
- Doubt overly concise content: a difficult question is rarely followed by an easy answer. Remember: those who provide good information have no problem going into detail ( be dubious of those who merely give you superficial data).
- Pay attention to the sources: choose official sources (preferably governmental): worldbank, eurostat, etc.. Remember: most countries have an official statistical institute that publishes daily research and updates its databases.
- Avoid clickbait: stay away from "clickbait" articles or posts. Remember: quality data doesn't need exclamation points, capitalized words or winking images.
- "Einstein said it!": the most authoritative source is not always the most reliable. Remember: often the writer of fake news reports an authoritative source because he knows that the majority of people trust it uncritically.
- Don't stop at the first google page. If you are looking for data on the web, use simple and not very articulated queries and remember: not always the first results on google are also the most reliable.
In conclusion, recognizing fake news is neither simple nor obvious; it would be wrong to say that the 5 tips above are enough to distinguish quality data from trashy data in a definitive way. In fact, it is necessary to train, develop critical sense and, most importantly, look for sources that make data-driven information an imperative.
