LinkedIn, a vocational social network, as a tool for promotion in selected healthcare service providers

The use of social media platforms and other online tools in the human resource management area has become a common part of the HR manager work. Today, the main aim of every corporation is to have the right employees at the right time and in the right job positions. The main objective of this research paper was to identify whether the size of the selected healthcare service providers influences the existence of a profile on the vocational social network LinkedIn, the active use of the vocational social network LinkedIn for sharing a job vacancy and the active use of the vocational social network LinkedIn for promoting or building the employer brand. Three research hypotheses were defined. The collection of research data was carried out from October 2018 to January 2019. The conducted research has shown that the size of the selected healthcare service provider does not affect the active use of the vocational social network LinkedIn and sharing a job vacancy, promoting or building the employer brand

Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual creation of fake users. Their goal is to deceive other users employing various methods, or even create a stream of fake news and opinions in order to influence an idea upon a specific subject, thus impairing the platform’s integrity. As such, machine learning techniques have been widely used in social networks to address this type of threat by automatically identifying fake accounts. Nonetheless, threat actors update their arsenal and launch a range of sophisticated attacks to undermine this detection procedure, either during the training or test phase, rendering machine learning algorithms vulnerable to adversarial attacks. Our work examines the propagation of adversarial attacks in machine learning based detection for fake Twitter accounts, which is based on AdaBoost. Moreover, we propose and evaluate the use of k-NN as a countermeasure to remedy the effects of the adversarial attacks that we have implemented