Human Trafficking in Social Networks: A Review of Machine Learning Techniques

Bermeo, Mike; Escobar, Silvana; Cuenca, Erick

doi:10.1007/978-3-031-45438-7_2

Cited by 2 publications

(1 citation statement)

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“…A comprehensive understanding of existing patterns and tendencies in utilizing social media platforms as tools for advancing human trafficking necessitates the analysis of vast amounts of data. According to Bermeo et al (2023), machine learning (ML) algorithms have demonstrated considerable efficacy in recognizing patterns indicative of human trafficking activities on such media. Among others, data mining is one of the most feasible approaches to scrutinizing big data.…”

Section: Data Mining As An Analytical and Preventative Toolmentioning

confidence: 99%

Algorithmic Exploitation in Social Media Human Trafficking and Strategies for Regulation

Moore

2024

Laws

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Human trafficking thrives in the shadows, and the rise of social media has provided traffickers with a powerful and unregulated tool. This paper delves into how these criminals exploit online platforms to target and manipulate vulnerable populations. A thematic analysis of existing research explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation. Furthermore, the paper examines the limitations of current regulations in tackling this online threat. The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation. By harnessing data analysis and machine learning, proactive strategies can be developed to disrupt trafficking networks and protect those most at risk.

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Section: Data Mining As An Analytical and Preventative Toolmentioning

confidence: 99%

Algorithmic Exploitation in Social Media Human Trafficking and Strategies for Regulation

Moore

2024

Laws

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Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

Diaz,

Luzuriaga,

Salazar

et al. 2024

Preprint

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The 2023 Ecuadorian presidential elections, contested between Daniel Noboa and Luisa González, marked a significant event in the nation's democratic journey. Amidst the fervor of campaigns and political debates, the role of social media, particularly Twitter, emerged as a focal point of interest. This study delves into the Twitter activities of both candidates, aiming to discern the potential use of automated actors, commonly known as ''bots", in shaping online narratives and influencing public opinion. By employing state-of-the-art bot detection methodologies, we analyzed the tweet patterns, engagement metrics, and content dissemination strategies associated with the official accounts of the candidates. Our findings shed light on the extent and implications of bot-driven interactions, offering insights into the evolving dynamics of political communication in the digital age. The results underscore the importance of ensuring transparency and authenticity in online electoral campaigns, pivotal for preserving the sanctity of the democratic process.

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Human Trafficking in Social Networks: A Review of Machine Learning Techniques

Cited by 2 publications

References 41 publications

Algorithmic Exploitation in Social Media Human Trafficking and Strategies for Regulation

Algorithmic Exploitation in Social Media Human Trafficking and Strategies for Regulation

Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

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