A Study On Security and Privacy Risks of Self-Disclosure On Social Networking Sites During COVID-19 Pandemic
Oluwabunmi Ariyo, Jianjun Zheng · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Social networking sites (SNSs) contain a large amount of information that has been self-disclosed by users around the world because it provides a platform for millions of users to express their feelings, emotions, and even deepest thoughts. Some of these information are sensitive and private and can be used by hackers to launch social engineering attacks against the user or the company the user works for. Due to the physical restrictions imposed by the COVID-19 pandemic, more people turned to social media to stay connected with each other and they spent more time on social media and disclosed much more information than the pre-COVID pandemic. The objective of this research is to study the potential security risks and privacy concerns brought by the disclosed information on SNSs during the COVID-19 pandemic. We developed an automated tool to collect and analyze publicly accessible data from Twitter API using some personal keywords such as birthday, anniversary, mental health, suicide etc. to investigate the impact of the COVID-19 pandemic on the disclosed sensitive information.