ABSTRACT
Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022
ABSTRACT
With the recent outbreak of the COVID-19 pandemic, major industries and academic institutions throughout the world have moved into a work-from-home situation, which resulted in a huge demand for networking resources. In particular, several home users are demanding simultaneously high data rates to support online video streaming applications, like Webex and Zoom. Therefore, it is expected that in these situations the quality of experience of online users may be severely affected or deteriorated, especially as the number of simultaneous active online users increases. In this paper, we use the Neyman-Scott Cluster Process from Stochastic Geometry to model the spatial patterns of online home users and analyze the throughput that could be achieved by a various numbers of online home users who are accessing the internet concurrently. © 2021 IEEE.