Multi-contextual spammer detection for online social networks

Abstract

With prevalent usage of internetworking, online social platforms have greatly revolutionized the traditional exchange of information. Social networking sites, including Facebook, Twitter, LinkedIn, etc., are being extensively used by the masses to create social as well as professional content and connections. However, due to wide transparency in information spread, the user profiles often remain vulnerable to spammers. Such threats occur in terms of harmful links being posted, involving eavesdropping attack and making fake identities of other people. The proposed model performs detection of spammers on the basis of real-time tweets extracted from Twitter. Several features have been taken into consideration while detecting the spammed Twitter profiles. The social network features include replies, mentions, web links, trending tags and profile reputation. Another feature has been introduced in the content-based feature to analyze sentiment of each tweet with consideration of the emoticons. Further, machine learning algorithms have been applied to conduct performance evaluation with respect to accuracy and spam accounts detection.

Subject Classification:

• 91D30
• 97M70
• 68Q87
• 68W20
• 68W40

Keywords:

• Online social network
• Spammer detection
• Feature extraction
• Decision tree
• Random forest
• Sentimental analysis
• Profile reputation