Discovery of dangerous self-medication methods with patients, by using social network mining
Reza Samizadeh, Morteza Khavanin Zadeh, Mahsa Jadidi, Mohammad Rezapour, Sahar Vatankhah · International Journal of Business Intelligence and Data Mining · 2023
Nowadays, social networks have replaced traditional media for information, and unfortunately, some people around the world, instead of reading books, turn to writings that are easily accessible on these networks. The present study categorises Persian texts on the Telegram social network at Jam Hospital and some Iranian websites on metabolic disease, obesity, and diabetes. Classifying data was done by text mining algorithms and the naive Bayes was more accurate than support vector machine. The results conclude that the 'Venustat' is one of the treatments that are emphasised by people, and they recommend this treatment to each other. In medical science, this drug has many complications, and it should not be used arbitrarily. Also a very dangerous drug namely 'Super Slim' is another drug that is strongly recommended by users. Therefore, raising public awareness is necessary to avoid relying on unscientific media content and facilitating access to medical services such as telemedicine.