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Background: Vaccination programs are effective only when a significant percentage of people are vaccinated. However, vaccine acceptance varies among communities around the world. Social media usage is arguably one of the factors affecting public attitudes towards vaccines. Objective: This study aims to identify if the social media usages factors can be used to predict attitudes and behavior towards the COVID-19 vaccines among the people in the Arab world. Methods: An online survey was conducted in the Arab countries and 217 Arab people participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior towards the COVID-19 vaccines. Results: Of the 217 participants, 56.22% of them were willing to accept the vaccine and 41.47% of them were hesitant. This study shows that none of the social media usages factors were significant enough to predict the actual vaccine acceptance behavior. Whereas the analysis showed few of the social media usage factors can predict public attitudes towards the COVID-19 vaccines. For example, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 is being exaggerated (OR=2.85, 95% CI=0.86-9.45, p=0.046) than infrequent social media users. Whereas participants having more trust in vaccine information shared by their contacts are less likely to agree that decision-makers have verified that vaccines are safe (OR=0.528, 95% CI= 0.276-1.012, p=0.05). Conclusion: The use of social media and information shared on it may affect public attitudes towards COVID-19 vaccines. Therefore, disseminating correct and validated information about COVID-19 and other vaccines on social media is important for increasing public trust and countering the impact of incorrect and misinformation.

When used in requirements processes and tools, personas have the potential to identify vulnerabilities resulting from misalignment between user expectations and system goals. Typically, however, this potential is unfulfilled as personas and system goals are captured with different mindsets, by different teams, and for different purposes. If personas are visualised as goal models, it may be easier for stakeholders to see implications of their goals being satisfied or denied, and designers to incorporate the creation and analysis of such models into the broader RE tool-chain. This paper outlines a tool-supported approach for finding implicit vulnerabilities from user and system goals by reframing personas as social goal models. We illustrate this approach with a case study where previously hidden vulnerabilities based on human behaviour were identified., Comment: SECPRE 2020 workshop pre-print

Goal-models (GM) have been used in adaptive systems engineering for their ability to capture the different ways to fulfill the requirements. Contextual GM (CGM) extend these models with the notion of context and context-dependent applicability of goals. In this paper, we observe that the interpretation of a goal achievement is itself context-dependent. Thus, we introduce the notion of Pragmatic Goals which have a dynamic satisfaction criteria. We also developed and evaluated an algorithm to decide the Pragmatic CGM's achievability. Finally, we performed several experiments to evaluate and to compare our algorithm against human judgment and concluded that the specification of context-dependent goals' applicability and interpretations make it hard for domain stakeholders to decide whether the model covers all possibilities, both in terms of time and accuracy, thus showing the importance and contribution of our algorithm.