Visualization of the social bot's fingerprints

Abstract

As the number of social media users increases for platforms such as Twitter, Facebook, and Instagram, so does the number of bot or spam accounts on these platforms. Typically, these bots or spam accounts are automated programmatically using the social media site's API and attempt to convey or spread a particular message. Some bots are designed for marketers trying to sell products or attract users to new sites. Other types of bots are much more malicious and disseminate misinformation that harms or tricks users. Such bots (fake accounts) may lead to serious consequences, as people's social network has become one of the determining factors in their general decision making. Therefore, these accounts have the potential to influence people's opinions drastically and hence real life events as well. Through different machine learning techniques, researchers have now begun to investigate ways to detect these types of malicious accounts automatically. To successfully differentiate between real accounts and bot accounts, a comprehensive analysis of the behavioral patterns of both types of accounts is required. In this paper, we investigate ways to select the best features from a data set for automated classification of different types of social media accounts (ex. bot versus real account) via visualization. To help select better feature combinations, we try to visualize which features may be more effective for classification using self-organizing maps.