Name
Title | ||
|---|---|---|
Social Network Spam Detection Based On Albert And Combination Of Bi-Lstm With Self-Attention |
Abstract | ||
|---|---|---|
Social networks are full of spams and spammers. 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, F-1-score, etc. The results show that the model has better performance than others. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1155/2021/5567991 | SECURITY AND COMMUNICATION NETWORKS |
DocType | Volume | ISSN |
Journal | 2021 | 1939-0114 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guangxia Xu | 1 | 42 | 9.46 |
| Daiqi Zhou | 2 | 0 | 0.34 |
| Jun Liu | 3 | 235 | 68.22 |