Disinformation can alter or manipulate our values, opinions, and rational decisions toward any life event because disinformation, such as fake news or rumors, is propagated rapidly and broadly in online social networks (OSNs). Gametheoretic models can help people maximize the benefits from dynamic social interactions. This work presents an opinion framework formulated by repeated, incomplete information games that model OSN users' subjective opinions. The users may update their opinions using various criteria, such as uncertainty, homophily, encounter, herding, or assertion. We demonstrate how Subjective Logic, a belief model explicitly handling opinion uncertainty, can be employed to model attackers' deception strategies, users' opinion update models, and the influences of propagating disinformation through the interactions between users. Through extensive experiments, we investigated how an individual user's information processing type can introduce different impacts on the extent of disinformation propagation. We compared the performance of the five different opinion update models under OSNs characterized by two real OSN datasets. We analyzed their impact on the choices of best strategies, their utilities, and network/opinion polarization. We also examined how the player's choices of best strategies under uncertainty are different from Nash Equilibrium strategies based on correct beliefs towards their opponents' moves.

Existing defensive deception (DD) approaches apply game theory, assuming that an attacker and defender play the same, full game with all possible strategies. However, in deceptive settings, players may have different beliefs about the game itself. Such structural uncertainty is not naturally handled in traditional game theory. In this work, we formulate an attackdefense hypergame where multiple advanced persistent threat (APT) attackers and a single defender play a repeated game with different perceptions. The hypergame model systematically evaluates how various DD strategies can defend proactively against APT attacks. We present an adaptive method to select an optimal defense strategy using hypergame theory for strategic defense as well as machine learning for adaptive defense. We conducted in-depth experiments to analyze the performance of the eight schemes including ours, baselines, and existing counterparts. We found the DD strategies showed their highest advantages when the hypergame and machine learning are considered in terms of reduced false positives and negatives of the NIDS, system lifetime, and players' perceived uncertainties and utilities. We also analyze the Hyper Nash Equilibrium of given hypergames and discuss the key findings and insights behind them.

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of

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