StopSpamX: A multi modal fusion approach for spam detection in social networking
Social networking platforms like Twitter, Instagram, Youtube, Facebook, Whatsapp have completely changed people's daily routine. Users of these social media networks have total freedom to upload anything that has political, commercial, or entertainment value. The data collected from these sourc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000743 |
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| author | Dasari Siva Krishna Gorla Srinivas |
| author_facet | Dasari Siva Krishna Gorla Srinivas |
| author_sort | Dasari Siva Krishna |
| collection | DOAJ |
| description | Social networking platforms like Twitter, Instagram, Youtube, Facebook, Whatsapp have completely changed people's daily routine. Users of these social media networks have total freedom to upload anything that has political, commercial, or entertainment value. The data collected from these sources can be genuine or fake. There are no concerns or problems if the data published is true and relevant. The main difficulty arises while we deal with the spam data. So, this problem of spam data should be properly handled. In order to achieve a spam free environment, researchers have proposed numerous methods and algorithms for spam detection. Out of them few algorithms are implemented to detect the spam data in twitter. • We compare the outcomes in each scenario using various state-of-the-art word embedding techniques, such as Word2Vecv, GloVe, and FastText. • To account for the restrictions, two deep learning hybrid fusion classifier techniques—Text-based classifier and Combined classifier—are used in this work. These classifiers are built using deep learning techniques including GRU, LSTM, and CNN. • These methods will be evaluated using a range of measures, including F1-score, accuracy, recall, and precision. These actions could enhance the performance of the hybrid fusion approach. |
| format | Article |
| id | doaj-art-a7c0d3bfd9744b6890f85ec1cd0b5de2 |
| institution | Matheson Library |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-a7c0d3bfd9744b6890f85ec1cd0b5de22025-06-27T05:51:03ZengElsevierMethodsX2215-01612025-06-0114103227StopSpamX: A multi modal fusion approach for spam detection in social networkingDasari Siva Krishna0Gorla Srinivas1Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India; Corresponding author.Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, IndiaSocial networking platforms like Twitter, Instagram, Youtube, Facebook, Whatsapp have completely changed people's daily routine. Users of these social media networks have total freedom to upload anything that has political, commercial, or entertainment value. The data collected from these sources can be genuine or fake. There are no concerns or problems if the data published is true and relevant. The main difficulty arises while we deal with the spam data. So, this problem of spam data should be properly handled. In order to achieve a spam free environment, researchers have proposed numerous methods and algorithms for spam detection. Out of them few algorithms are implemented to detect the spam data in twitter. • We compare the outcomes in each scenario using various state-of-the-art word embedding techniques, such as Word2Vecv, GloVe, and FastText. • To account for the restrictions, two deep learning hybrid fusion classifier techniques—Text-based classifier and Combined classifier—are used in this work. These classifiers are built using deep learning techniques including GRU, LSTM, and CNN. • These methods will be evaluated using a range of measures, including F1-score, accuracy, recall, and precision. These actions could enhance the performance of the hybrid fusion approach.http://www.sciencedirect.com/science/article/pii/S2215016125000743Text-based classifier and Combined classifier |
| spellingShingle | Dasari Siva Krishna Gorla Srinivas StopSpamX: A multi modal fusion approach for spam detection in social networking MethodsX Text-based classifier and Combined classifier |
| title | StopSpamX: A multi modal fusion approach for spam detection in social networking |
| title_full | StopSpamX: A multi modal fusion approach for spam detection in social networking |
| title_fullStr | StopSpamX: A multi modal fusion approach for spam detection in social networking |
| title_full_unstemmed | StopSpamX: A multi modal fusion approach for spam detection in social networking |
| title_short | StopSpamX: A multi modal fusion approach for spam detection in social networking |
| title_sort | stopspamx a multi modal fusion approach for spam detection in social networking |
| topic | Text-based classifier and Combined classifier |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000743 |
| work_keys_str_mv | AT dasarisivakrishna stopspamxamultimodalfusionapproachforspamdetectioninsocialnetworking AT gorlasrinivas stopspamxamultimodalfusionapproachforspamdetectioninsocialnetworking |