Fake Reviews Detection In Real-Time Business Big Data

Commercial Conflict: Some companies pay spammers to spread false information in order to harm competitors' online ratings.

Imbalance Classification: A fraudster and eligible users could be close together in Euclidean distance, but their labels would be different. However, this will lead to different class assignments every time we repeat the learning iteration.

Lake of Required Fraudulent Information: In financial contexts, fraudsters would avoid dealing with each other to avoid detection.

Redundant Link of Harm Users: spammers would use origin accounts to leave feedback on their abusive comments, so there would be a lot of links between the spam review and the real users' accounts.

Fake Reviews Identification Challenges: the real Dataset is less accurate than other review datasets, and it is more difficult to identify fake reviews in the real scene

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