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Although social network platforms have established a variety of strategies to prevent the spread of spam, strict information review mechanism has given birth to smarter spammers who disguise spam as text sent by ordinary users. In response to this, this paper proposes a spam detection method powered by the self-attention Bi-LSTM neural network model combined with ALBERT, a lightweight word vector model of BERT. We take advantage of ALBERT to transform social network text into word vectors and then input them to the Bi-LSTM layer. After feature extraction and combined with the information focus of the self-attention layer, the final feature vector is obtained. Finally, SoftMax classifier performs classification to obtain the result. We verify the excellence of the model with accuracy, precision, F1-score, etc. The results show that the model has better performance than others.<\/jats:p>","DOI":"10.1155\/2021\/5567991","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T20:09:41Z","timestamp":1617912581000},"page":"1-11","source":"Crossref","is-referenced-by-count":12,"title":["Social Network Spam Detection Based on ALBERT and Combination of Bi-LSTM with Self-Attention"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9010-2443","authenticated-orcid":true,"given":"Guangxia","family":"Xu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-2575","authenticated-orcid":true,"given":"Daiqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7390-8958","authenticated-orcid":true,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-2755-7_41"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55354-2_10"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/lcomm.2010.01.100830"},{"first-page":"219","article-title":"Opinion spam and analysis","author":"J. 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Ahmed","key":"18","article-title":"An MCL-based approach for spam profile detection in online social networks"},{"key":"19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.knosys.2018.04.025","article-title":"Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts","volume":"153","author":"A. Zoubi","year":"2018","journal-title":"Knowledge Based Systems"},{"issue":"3","key":"20","article-title":"User identification via neural network based language models","volume":"29","author":"P. Tien","year":"2018","journal-title":"International Journal of Network Management"},{"author":"J. Ma","key":"21","article-title":"Detecting rumors from microblogs with recurrent neural networks"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-009-0440-2"},{"author":"L. Z. 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