Seybold Report ISSN: 1533-9211
S.Indarjeet Singh
Professor, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India, drindar2020@gmail.com
Kondlada Shirisha
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India
Kondur Nikitha
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India
Singala Tejaswini
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India
Vol 17, No 07 ( 2022 ) | DOI: 10.5281/zenodo.6875673 | Licensing: CC 4.0 | Pg no: 30-36 | Published on: 25-07-2022
Abstract
Spam plays a major role in social media, like Twitter. Twitter is the major platform for spreading the news all over the world. So the users always choose Twitter as a platform to convert in to a target by sharing fake posts, fake news, etc. So, the spammers are used to spreading an enormous amount of false and deleterious data. Twitter is an online site where people can give their opinions, news, and everything. Furthermore, the ability to spread false information via fake identities results in the spreading of hazardous materials. To identify this spam, we are using machine learning algorithms.
Keywords:
Spammer’s identification, Machine learning algorithms, Social media, Spam detection.