Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

Network Dynamics of COVID-19 Fake and True News Diffusion Networks

Social media platforms have become an integral source to spread and consume information. Twitter has emerged as the fastest medium to disseminate any information. This blind trust on social media has raised the concern to quantify the truth or fakeness of what we are consuming. During COVID-19, the usage of social platforms has dramatically increased in everyone's life. It is high time to distinguish between the type of users involved in spreading fake and true news content. Our study aims to answer two questions. First, what is the complex network structure of users involved in spreading any news? How two types (i.e. Fake and True) of networks are different in terms of network topology. Second, what is the role of influential users in spreading both types of news? To answer these, the fake and true news of COVID-19 are collected which have been classified by fact-checking websites. Diffusion networks have been created to perform the experiments. Network topological analysis revealed that despite having differences, most properties show similar behaviour. Though, it can be stated that during COVID-19, behaviour of users remained the same in spreading fake or true content. Resilience analysis discovered that fake networks were more densely connected than true ones. There were more centric nodes or influential users were present in Fake news networks than True news networks. © 2022 World Scientific Publishing Co.

Complex Network and Source Inspired COVID-19 Fake News Classification on Twitter

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception. It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy. Therefore, it is of paramount importance to detect and contain unreliable information. Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators. Generating human-like content allows deceiving content-based methods. Network-based methods rely on the complete graph to detect fake news, resulting in late detection. User profile-based techniques are effective for bots or fake accounts detection. However, they are not suited to detect fake news from original accounts. To deal with the shortcomings in existing methods, we introduce a source-based method focusing on the news propagators’community, including posters and re-tweeters to detect such contents. Propagators are connected using follower-following relations. A feature set combining the connectivity patterns of news propagators with their profile features is used in a machine learning framework to perform binary classification of tweets. Complex network measures and user profile features are also examined separately. We perform an extensive comparative analysis of the proposed methodology on a real-world COVID-19 dataset, exploiting various machine learning and deep learning models at the community and node levels. Results show that hybrid features perform better than network features and user features alone. Further optimization demonstrates that Ensemble’s boosting model CATBoost and deep learning model RNN are the most effective, with an AUC score of 98%. Furthermore, preliminary results show that the proposed solution can also handle fake news in the political and entertainment domain using a small training set. Author