Employing Behavioral Analysis to Predict User Attitude towards Unwanted Content in Online Social Network Services: The Case of Makkah Region in Saudi Arabia

• Main Authors: Mashael M. Alsulami, Arwa Yousef Al-Aama
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Computers
• Subjects: personalized privacy protection; user behavior; user attitude; unwanted content; behavioral analysis
• Online Access: https://www.mdpi.com/2073-431X/9/2/34

The high volume of user-generated content caused by the popular use of online social network services exposes users to different kinds of content that can be harmful or unwanted. Solutions to protect user privacy from such unwanted content cannot be generalized due to different perceptions of what is considered as unwanted for each individual. Thus, there is a substantial need to design a personalized privacy protection mechanism that takes into consideration differences in users’ privacy requirements. To achieve personalization, a user attitude about certain content must be acknowledged by the automated protection system. In this paper, we investigate the relationship between user attitude and user behavior among users from the Makkah region in Saudi Arabia to determine the applicability of considering users’ behaviors, as indicators of their attitudes towards unwanted content. We propose a semi-explicit attitude measure to infer user attitude from user-selected examples. Results revealed that semi-explicit attitude is a more reliable attitude measure to represent users’ actual attitudes than self-reported preferences for our sample. In addition, results show a statistically significant relationship between a user’s commenting behavior and the user’s semi-explicit attitude within our sample. Thus, commenting behavior is an effective indicator of the user’s semi-explicit attitude towards unwanted content for a user from the Makkah region in Saudi Arabia. We believe that our findings can have positive implications for designing an effective automated personalized privacy protection mechanism by reproducing the study considering other populations.