Machine Learning Classifiers for Efficient Spammers Detection in Twitter OSN

Sadineni, Praveen Kumar

doi:10.2139/ssrn.3734170

Cited by 2 publications

(1 citation statement)

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“…For instance, Alarfaj et al [24] use a Support Vector Machine and Neural Network techniques for this task. On the other hand, content-based ML approaches use the textual characteristics as a focus of their analysis [25][26][27][28]. For example, Dickerson et al [29] and Loyola-González et al [30] have proven that sentiment analysis effectively distinguishes between human users and bots.…”

Section: Detection and Analysis Techniquesmentioning

confidence: 99%

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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Section: Detection and Analysis Techniquesmentioning

confidence: 99%

Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

Diaz,

Luzuriaga,

Salazar

et al. 2024

Preprint

View full text Add to dashboard Cite

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Machine learning-based social media bot detection: a comprehensive literature review

Aljabri

Zagrouba

Shaahid

et al. 2023

Soc. Netw. Anal. Min.

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In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life. The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create malicious bots. Malicious bots in these platforms are automated or semi-automated entities used in nefarious ways while simulating human behavior. Moreover, such bots pose serious cyber threats and security concerns to society and public opinion. They are used to exploit vulnerabilities for illicit benefits such as spamming, fake profiles, spreading inappropriate/false content, click farming, hashtag hijacking, and much more. Cybercriminals and researchers are always engaged in an arms race as new and updated bots are created to thwart ever-evolving detection technologies. This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. We bring forth a concise overview of all the supervised, semi-supervised, and unsupervised methods, along with the details of the datasets provided by the researchers. Additionally, we provide a thorough breakdown of the extracted feature categories. Furthermore, this study also showcases a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to explore.

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Machine Learning Classifiers for Efficient Spammers Detection in Twitter OSN

Cited by 2 publications

References 0 publications

Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

Identifying Bot Accounts on Twitter During the 2023 Ecuadorian Presidential Election

Machine learning-based social media bot detection: a comprehensive literature review

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