Article: Discovery of dangerous self-medication methods with patients, by using social network mining Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2023 Vol.23 No.3 pp.277 - 287 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Discovery of dangerous self-medication methods with patients, by using social network mining

Authors: Reza Samizadeh; Morteza Khavanin Zadeh; Mahsa Jadidi; Mohammad Rezapour; Sahar Vatankhah

Addresses: Technical & Engineering Faculty, Alzahra University, Tehran, Iran ' Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran ' Alzahra University, Tehran, Iran ' Iranian Ministry of Science, Research and Technology, I.R., Iran; Tehran University of Medical Sciences, Tehran, IR Iran ' Alzahra University, Tehran, Iran

Abstract: 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.

Keywords: text mining; sentiment analysis; data mining; support vector machine; SVM; naive Bayes; social network mining; health; obesity; diabetes mellitus; metabolic; self-medication.

DOI: 10.1504/IJBIDM.2023.133186

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.3, pp.277 - 287

Received: 23 Mar 2021
Accepted: 03 Mar 2022
Published online: 01 Sep 2023 *

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Title: Discovery of dangerous self-medication methods with patients, by using social network mining

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