Twitter and research: A systematic literature review through text mining

A Karami, M Lundy, F Webb, YK Dwivedi - IEEE access, 2020 - ieeexplore.ieee.org
Researchers have collected Twitter data to study a wide range of topics. This growing body
of literature, however, has not yet been reviewed systematically to synthesize Twitter-related …

Phishing environments, techniques, and countermeasures: A survey

A Aleroud, L Zhou - Computers & Security, 2017 - Elsevier
Phishing has become an increasing threat in online space, largely driven by the evolving
web, mobile, and social networking technologies. Previous phishing taxonomies have …

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

S Cresci, R Di Pietro, M Petrocchi… - Proceedings of the 26th …, 2017 - dl.acm.org
Recent studies in social media spam and automation provide anecdotal argumentation of
the rise of a new generation of spambots, so-called social spambots. Here, for the first time …

Malicious URL detection using machine learning: A survey

D Sahoo, C Liu, SCH Hoi - arXiv preprint arXiv:1701.07179, 2017 - arxiv.org
Malicious URL, aka malicious website, is a common and serious threat to cybersecurity.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure …

Twibot-20: A comprehensive twitter bot detection benchmark

S Feng, H Wan, N Wang, J Li, M Luo - Proceedings of the 30th ACM …, 2021 - dl.acm.org
Twitter has become a vital social media platform while an ample amount of malicious Twitter
bots exist and induce undesirable social effects. Successful Twitter bot detection proposals …

Fame for sale: Efficient detection of fake Twitter followers

S Cresci, R Di Pietro, M Petrocchi, A Spognardi… - Decision Support …, 2015 - Elsevier
Fake followers are those Twitter accounts specifically created to inflate the number of
followers of a target account. Fake followers are dangerous for the social platform and …

URLNet: Learning a URL representation with deep learning for malicious URL detection

H Le, Q Pham, D Sahoo, SCH Hoi - arXiv preprint arXiv:1802.03162, 2018 - arxiv.org
Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is
imperative to detect them in a timely manner. Traditionally, this is done through the usage of …

Social network security: Issues, challenges, threats, and solutions

S Rathore, PK Sharma, V Loia, YS Jeong, JH Park - Information sciences, 2017 - Elsevier
Social networks are very popular in today's world. Millions of people use various forms of
social networks as they allow individuals to connect with friends and family, and share …

[HTML][HTML] Fake news outbreak 2021: Can we stop the viral spread?

T Khan, A Michalas, A Akhunzada - Journal of Network and Computer …, 2021 - Elsevier
Social Networks' omnipresence and ease of use has revolutionized the generation and
distribution of information in today's world. However, easy access to information does not …

BotRGCN: Twitter bot detection with relational graph convolutional networks

S Feng, H Wan, N Wang, M Luo - Proceedings of the 2021 IEEE/ACM …, 2021 - dl.acm.org
Twitter bot detection is an important and challenging task. Existing bot detection measures
fail to address the challenge of community and disguise, falling short of detecting bots that …