Research Article
Identification of Spam on Social Media by Semi-supervised Learning Approach
@INPROCEEDINGS{10.1007/978-3-031-66044-3_10, author={Sudana Shashi Kiran and Sure Uday Kiran and Elluru Amrutha and E. Yasaswi and Annam Kumar Sai and Kusam Sravya and S. R. Reeja}, title={Identification of Spam on Social Media by Semi-supervised Learning Approach}, proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings}, proceedings_a={PERSOM}, year={2024}, month={8}, keywords={Semi-Supervised Learning Social Media Ham And Spam Messages}, doi={10.1007/978-3-031-66044-3_10} }- Sudana Shashi Kiran
Sure Uday Kiran
Elluru Amrutha
E. Yasaswi
Annam Kumar Sai
Kusam Sravya
S. R. Reeja
Year: 2024
Identification of Spam on Social Media by Semi-supervised Learning Approach
PERSOM
Springer
DOI: 10.1007/978-3-031-66044-3_10
Abstract
This study examines how machine learning methods can be used to identify Twitter spammers. Due to spammers’ increased use of social media platforms, it is crucial to combat their fraudulent operations. This study uses extensive Twitter data, such as user profiles, tweet content, and network connections, to create algorithms that accurately differentiate between genuine users and spammers. By successfully identifying and filtering out spammers utilizing a range of machine learning algorithms, deep learning techniques, feature extraction methodologies, and even categorizing tweets based on their emotional tone, the ultimate goal is to improve platform trust and user experience. In order to efficiently identify spammers and fake users, the article provides a solution that makes use of a unique machine learning algorithm that meticulously analyses user personal information and account history. Because of its popularity, Twitter has drawn spammers and bogus users, which has caused confusion. The study seeks to address “Twitter Spam Drift” by applying a Semi-Supervised Learning Approach (SSLA), which adjusts to changes in spam behavior and has demonstrated promising results on English datasets. To identify spam behaviors on Twitter, the project uses deep-learning classifier algorithms like LSTM, and GRU. Here, as can be seen, we use various methods to achieve accuracy ranging from 87% to 97%.
