A Statistical Analysis of Persian Fake News on COVID-19

In this research, an attempt is made to investigate the characteristics of Persian fake news related to Covid-19 by using statistical analysis.  To this end, first, a language corpus containing reliable and fake news in Persian in the field of Corona is prepared. Then, the language patterns of these two data sets, as well as two statistical analyzes of the amount of information and the readability of reliable and fake news, are examined and compared with each other. According to the exteracted information and the experimental results achieved from the developed corpus on COVID-19 fake news, there are common language patterns in these two datasets. Moreover, the amount of information in reliable news is more than fake news based on two measures of entropy and surprise. Based on the results, the readability level of the fake news is measured based on the readability formulas. According to the results, the text of fake news is simpler than real news. In the process of automatic labeling of reliable and fake news based on the level of difficulty, most news is recognized as simple texts. The results show that fake news is mostly simple and not difficult compared to reliable news. In addition to this achievement, to study linguistic properties of fake news statistically based on the information amount and readability, the applicablity of this statistical information was studied to detect fake news using machine learning methods.