Identity deception detection on social media platforms

Abstract

Whenever they interact on big data platforms such as social media, humans run the risk of being targeted by other malicious individuals. The protection of these humans is problematic, even though cyber-attack events have received much attention in public in order to create greater awareness. Cyber protection is di cult, largely due to the nature of these social media platforms (SMPs), as they allow individuals to create and use almost any persona they choose, with minimal validation. This, together with the sheer volume of data being generated, warrants the use of automated threat detection methods. The victims are targeted through di erent forms of cyber threats of which identity deception is but one example. Identity deception is by no means a novel concept. The social sciences, more speci cally psychology, have for many years attempted to understand the motive(s) behind human deception. More recently, research work aimed at nding fake or bot accounts has had some success. This study used the abundant knowledge about identity deception, in addition to the information already available by default on SMPs, to detect those humans who lie about their identity. The study in hand presents an SMP research environment with a methodology and prototype that will assist with the automated detection of human identity deception on SMPs. This environment enables various supervised machine learning experiments. The rst experiment used those attributes describing an SMP pro le to detect identity deception with a nal F1 score of 32%. The second experiment added additional features known to detect deceptive bots on SMPs with a nal F1 score of 49%. The third experiment added further features from psychology, known to identify deceptive humans, with a nal F1 score of 86%. A critical evaluation of the SMP attributes and engineered features reveals that age, name, and location contributed most towards identity deception as executed by humans in respect to other humans. The results show that human identity deception detection can be achieved by using SMP attributes and engineered features that describe the identity of the individual only, thus excluding SMP content that can be costly to construe. The prototype furthermore includes an Identity Deception Detection Model (IDDM) that scores a human's perceived deceptiveness and intuitively explains the score. The IDDM not only indicates when a human is potentially deceptive but also highlights those attributes or features that were most prevalent in the conclusion. This aids investigators, like the police force, to not only identify potential deceptive humans, but to also make a more informed decision. The results from this research make a signi cant contribution to the elds of both cyber security and the social sciences.