Article 1 Title : A taxonomy of cyber-harms: Defining the impacts of cyber-attacks and understanding how they propagate.

This article talks about how several organizational functions have become digitized as a result of technological improvements. This article highlights how this can increase revenues and success but also exposes them to cyber-attacks and other catastrophes. The threat landscape of cyber-attacks is always evolving, making it difficult for enterprises to stay up and protect themselves successfully. This topic of organization being exposed to cyber attacks relates to social sciences through significant social and psychological consequences.Employees and clients, for instance, may experience stress, anxiety, and dread as a result of a cyberattack. This may have an adverse effect on their trust and confidence in the company, harming both their reputation and their relationships with their stakeholders. Moreover, cyberattacks may have broader social repercussions, such the rise of cybercrime or a negative influence on national security. Also, they may lead to moral conundrums with data security, privacy, and protection.Interdisciplinary approaches that combine social sciences, such as sociology, psychology, and political science with technical skills are needed to research cyber attacks and their effects on companies and society. Cyber attacks can be effectively mitigated by using policies, standards, and practices that take into account the social and psychological effects they have. The research question of this article is on how companies can approach risk of cyber attacks and why taxonomy of cyber harm should be available for organizations.In this article the

authors used observational research to get a better understanding of how cyber attacks affect organizations and how we can help organizations stay in the loop about cyber attacks. This article contributes to society to help organizations fend and recover from imposing cyber attacks throughout the world. Helping organizations in the recovery process is a big step in how organizations being in the know for cyber attacks can benefit marginalized groups.it is essential to consider the challenges, concerns, and contributions of marginalized groups in the recovery process from cyber attacks. This can include providing targeted support and resources to help them recover from the impact of a cyber attack and addressing any social inequalities that may be exacerbated by such incidents. Additionally, it is crucial to involve and empower marginalized groups in developing and implementing cybersecurity policies and practices to ensure their concerns and perspectives are adequately represented.

Article 2: Classifying social media bots as malicious or benign using semi-supervised machine learning

In this article the author is trying to classify why social media bots should be known as “malicious or semi-supervised machine learning”. This article’s research question is, can the same features used in previous studies to successfully distinguish between malicious bots and humans be useful in classifying benign and malicious bots? What features found in the metadata of OSNs indicate anomalous behavior between benign and malicious bots?  Can semi-supervised machine learning (ML) models be used to classify malicious and benign bots, given a limited labeled dataset of such bots? This article relates to social sciences by seeing the impact of social media bots on society can also inform policies and guidelines to mitigate their harm effectively. This can involve addressing issues such as privacy, security, and the ethics of using bots for social and political purposes.The type of research method used in this article was quantitative because they gathered data from OSNs to try to prove if social media bots were similar to malicious bots. This article uses archival research to prove that social media bots are similar to malicious bots through the activity recorded on the OSNs and going in depth of what the purpose of social media bots by doing research on them via social media. This article uses mass amounts of diagnostic data analysis by pulling information from not only form OSNs but also using the GMM algorithm and comparing data from real social media bots and malicious bots. Social media bots being classified as malicious bots can help determine how serious the issue is and this can help marginalized groups( such as people affected by the digital divide) to be aware of the links and comments social media bots can place down that can do harm to your system.Also Marginalized groups, such as minorities, LGBTQ+ individuals, and people with disabilities, are often targets of discrimination and harassment on social media platforms. Bots can exacerbate this problem by amplifying negative messages or engaging in coordinated harassment campaigns. This can have a severe impact on the mental health and well-being of marginalized individuals. By comparing social media bots to malicious bots, it becomes possible to recognize the harm caused by bots in spreading misinformation and perpetuating harmful messages. This can lead to increased awareness and attention to the negative impact of bots on marginalized groups .Moreover, understanding the impact of bots on marginalized groups can inform the development of policies and guidelines to mitigate their harm effectively. For example, social media platforms can implement measures to detect and remove bots that engage in coordinated harassment campaigns or amplify harmful messages. Overall, comparing social media bots to malicious bots can raise awareness of the negative impact of bots on marginalized groups, and inform strategies to mitigate their harm effectively. By addressing the concerns of marginalized groups, we can create a more inclusive and equitable online environment.This article contributes to society by spreading awareness to social media bots and allowing people to not take them lightly; and how social media bots should be seen more as a cyber threat than a nuisance.

Works Cited

Ioannis Agrafiotis, Jason R C Nurse, Michael Goldsmith, Sadie Creese, David Upton, A taxonomy of cyber-harms: Defining the impacts of cyber-attacks and understanding how they propagate, Journal of Cybersecurity, Volume 4, Issue 1, 2018, tyy006, https://doi.org/10.1093/cybsec/tyy006

Innocent Mbona, Jan H P Eloff, Classifying social media bots as malicious or benign using semi-supervised machine learning, Journal of Cybersecurity, Volume 9, Issue 1, 2023, tyac015, https://doi.org/10.1093/cybsec/tyac015