| Autor: |
Yi-Wei Ma, Chun-Yao Chang, Yen-Neng Chiang, Jiann-Liang Chen, Chih-Hung Chen, Wen-Tsung Chang, Shun-Ching Yang, Ying-Hsun Lai |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 10, Pp 131386-131393 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3228113 |
| Popis: |
In the age of information explosion, it is easy for people to receive many messages, and it is difficult to verify the authenticity of each message. Therefore, people often quickly and forward the acquired information and share anonymous information, rather than absorbing it after verification. The study questions current Artificial Intelligence (AI) detection methods, arguing that a simple dichotomy cannot distinguish between true and false information. This study proposes a mixed method to analyze events based on the dissemination and interaction of false information in online enterprise communities from the perspective of an observer. The event explores multiple features based on various characteristics, such as motivation, purpose, intention, and behavior. Experimental results show that the proposed method can effectively identify false information with high risk. Additionally, this study discusses the effectiveness and response strategies of the enterprise cyber warrior based on the mixed multi-layer analysis. This study provides a preliminary study of mixed cognitive warfare identification and immediate response behavior for corporate Internet rumors. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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