Name
Abstract | ||
|---|---|---|
Social media networks usage is spreading but accompanied by a new shape of the social engineering attacks in which users' accounts are compromised by attackers to spread malicious messages for different purposes. To overcome these attacks, authorship verification, a classification problem for classifying a text, whether it belongs to a user or not, is needed. Moreover, the verification must be accurate and fast. Herein, an authorship verification model proposed. The model uses XGBoost, as a preprocessor, to discover functional features of the text message, which ranked using MCDM methods to build a classification model. Twitter messages are used to test the model; however, any social media's data might be used. The suggested model was evaluated against a crawled dataset from Twitter composed of 16124 tweets with 280 characters. The proposed method achieved F-score over 0.94. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-020-10361-2 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Authorship verification, Natural language processing, Machine learning | Journal | 80 |
Issue | ISSN | Citations |
9 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suleyman Alterkavi | 1 | 0 | 0.34 |
| Hasan Erbay | 2 | 11 | 5.32 |