Identification of cyber harassment and intention of target users on social media platforms

Abarna, S.; Sheeba, J.I.; Jayasrilakshmi, S.; Devaneyan, S. Pradeep

doi:10.1016/j.engappai.2022.105283

Cited by 20 publications

(20 citation statements)

References 36 publications

(41 reference statements)

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“…The landscape within the subdomain has undergone a significant shift, transitioning from a focus primarily on network security (in the case of logic-based approaches) to encompassing a broader range of cases [10,27]. Additionally, researchers have delved into identifying intents related to social media utilization, as explored by [25,26]. Notably, the work by T. Li et al [27] stands out as it operates at a higher level of plan recognition, while the remaining studies primarily address intent or goal recognition.…”

Section: Discussionmentioning

confidence: 99%

“…The article by S. Abarna et al [26] presents an algorithm for detecting cyber harassment and intention from text on social media platforms, using Instagram comments as a case study. The paper utilizes a conventional scheme that analyzes the lexical meaning of the text using natural language processing techniques, and a Fast Text model that captures the word order of the text.…”

Section: Classical Machine Learningmentioning

confidence: 99%

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Intention Recognition in Digital Forensics: Systematic Review

Kassa,

James,

Belay

2024

Preprint

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In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

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Section: Discussionmentioning

confidence: 99%

Section: Classical Machine Learningmentioning

confidence: 99%

Intention Recognition in Digital Forensics: Systematic Review

Kassa,

James,

Belay

2024

Preprint

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“…Although this allows for a wider range of opinions and viewpoints to be shared, however it provides a platform for mal-info. These channels can be legitimately criticized for allowing hate speech, harassment (Abarna et al 2022), and misinformation to spread unchecked. In this domain of new no-restrictions social networks, three seem to emerge as frequently used channels, namely Gab, Gettr and Bitchute.…”

Section: New Social Network With Reduced Moderationmentioning

confidence: 99%

Modes of Tracking Mal-Info in Social Media with AI/ML Tools to Help Mitigate Harmful GenAI for Improved Societal Well Being

Skumanich,

Kim

2024

AAAI-SS

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A rapidly developing threat to societal well-being is from misinformation widely spread on social media. Even more concerning is ”mal-info” (malicious) which is amplified on certain social networks. Now there is an additional dimension to that threat, which is the use of Generative AI to deliberately augment the mis-info and mal-info. This paper highlights some of the ”fringe” social media channels which have a high level of mal-info as characterized by our AI/ML algorithms. We discuss various channels and focus on one in particular, ”GAB”, as representative of the potential negative impacts. We outline some of the current mal-info as an example. We capture elements, and observe the trends in time. We provide a set of AI/ML modes which can characterize the mal-info and allow for capture, tracking, and potentially for responding or for mitigation. We highlight the concern about malicious agents using GenAI for deliberate mal-info messaging specifically to disrupt societal well being. We suggest the characterizations presented as a methodology for initiating a more deliberate and quantitative approach to address these harmful aspects of social media which would adversely impact societal well being. The article highlights the potential for ”mal-info,” including disinfo, cyberbullying, and hate speech, to disrupt segments of society. The amplification of mal-info can result in serious real-world consequences such as mass shootings. Despite attempts to introduce moderation on major platforms like Facebook and to some extent on X/Twitter, there are now growing social networks such as Gab, Gettr, and Bitchute that offer completely unmoderated spaces. This paper presents an introduction to these platforms and the initial results of a semiquantitative analysis of Gab’s posts. The paper examines several characterization modes using text analysis. The paper emphasizes the developing dangerous use of generative AI algorithms by Gab and other fringe platforms, highlighting the risks to societal well being. This article aims to lay the foundation for capturing, monitoring, and mitigating these risks.

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“…In the global context, the extant literature on harassment could be seen in different contexts, such as the workplace (Abarna et al, 2022;Steven et al, 2021), educational institutions and schools involving bullying (Basu et al, 2021), cyberspace (Scarduzio, 2022), and sports (Sitheravellu, 2022). However, the main focus of the sexual harassment literature is the employment setting and attempts to define such conduct.…”

Section: Conceptualising Sexual Harassmentmentioning

confidence: 99%

Recent Development in Sexual Harassment Law in Malaysia: Whither the Victim's Protection?

Hamin¹,

Hussain²,

Rani³

et al. 2022

IJARBSS

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Before 2012, there was no law on sexual harassment in Malaysia. However, when the Employment Act 1955 was amended in 2012, employers must inquire into sexual harassment complaints, failing which a criminal penalty will be imposed. Unfortunately, the new law did not allow a complainant to claim damages from the harasser or the employer. After more than two decades of campaigns by women's rights groups and NGOs, the Anti-Sexual Harassment Bill 2021 (hereinafter 'the ASHB 2021') was finally enacted and passed in July this year. Given the novelty and absence of any academic research, this paper seeks to examine the provisions of the Bill critically to understand its scope, coverage and broader implications for sexual harassment victims. The paper employs a qualitative research methodology, adopting a doctrinal approach and content analysis of the primary source, the ASHB 2021, that would provide a deeper understanding of the legislation. The authors contend that despite being regarded as a victim-centric law, the Bill is half-baked and inadequate to protect sexual harassment victims due to its failure to include significant provisions on the sexual harassment definition, the protection against victimisation and employers' obligations to prevent and address such conduct.

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Identification of cyber harassment and intention of target users on social media platforms

Cited by 20 publications

References 36 publications

Intention Recognition in Digital Forensics: Systematic Review

Intention Recognition in Digital Forensics: Systematic Review

Modes of Tracking Mal-Info in Social Media with AI/ML Tools to Help Mitigate Harmful GenAI for Improved Societal Well Being

Recent Development in Sexual Harassment Law in Malaysia: Whither the Victim's Protection?

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