Detecting spam in sharing economy : a case study on Airbnb

• Κύριος συγγραφέας: Τσεκούρα, Μαρία
• Άλλοι συγγραφείς: Tsekoura, Maria
• Γλώσσα: English
• Έκδοση: 2020
• Θέματα: Sharing economy; Twitter; Airbnb; Διαμοιραζόμενη οικονομία

Internet social networks (OSN) are an aspect of the daily life of the world. However, spammers are also attracted to the success of OSN. Spam is a threat to the economy. In particular, it imposes negative external effects on users without giving them an advantage or an exception. Negative externalities include the financial loss (if any) suffered by the recipients and the time and effort required to read and analyze the unwanted comment. Given all the above, in this dissertation, we dealt with the detection of spam in the OSN using a machine learning algorithm. More specifically, we selected the microblogging service Twitter as a representative of OSN, collected 7304 comments listed on Airbnb as a representative of the most successful hosting industry in the sharing economy and after processing the data, we applied the Naive Bayes classifier as well as 10 - fold cross - validation for better accuracy in the result. The experimental results show that the preferred classification model received relatively high evaluation results, with an average accuracy of 84%.