We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter.
- Authors
Alsubaei, Faisal S.
- Abstract
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.
- Subjects
X Corp.; ACCOUNTING fraud; MICROBLOGS; RANDOM forest algorithms; FAKE news; DESIGN science; MACHINE learning
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 5, p3013
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13053013