Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter
Open accessAbstract
It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fake accounts. To develop this detector, previously published tweets serve as the datasets for the training session. This data comes from Twitter and are obtained through the REST API. The technique of machine learning with random forest (RF) is then used to train the data. The high levels of accuracy (99.4%) obtained from the RF detection results served as the foundation for the development of the bot account. This detector tool, developed using this model, can be utilized by individuals, businesses, and government agencies to assist in the detection and prevention of Twitter problems related to fake news and fake accounts.
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Social media platforms have implemented authentication systems and detection algorithms, but the problem of fake accounts remains persistent.
“To address these issue, social media platforms have implemented various measures, including authentication systems for prominence users and algorithms designed to detect and remove suspicious accounts. However, this problem remains persistent, and the ongoing growth of new methods to identify and block fraudulent accounts remains a urgency for both researchers and platform operatives19.”
From Instagram fake profile detection using an ensemble learning method (2025) · cites this paper as [19]
The Introduction (page unavailable) says Twitter has made numerous attempts to enforce its policy against fake accounts, yet users continue to evade those efforts. The paper also develops a fake-account detector, but it does not establish that social media platforms have implemented authentication systems for prominent users or algorithms to detect and remove suspicious accounts. (LLM, conf=0.86)
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- Authors
- Alsubaei, F. S.
- Affiliations
- University of Jeddah (SA)
- Corresponding author
- Alsubaei, Faisal S.
- ORCID
- Alsubaei, Faisal S.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-02-26
- Volume
- 13
- Issue
- 5
- Pages
- 3013
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4322503172
- Cited by
- 9
- References
- 41
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 1
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Jeddah, Jeddah, Saudi Arabia (UJ-22-DR-70)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- fake account detection, bot detection, Twitter, fake news, random forest, social media abuse
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