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- W4319430387 abstract "Online reviews have a significant impact on consumer purchase decisions. This clout invites opportunities for fraudulent behavior to help a business improve its online presence or hinder a competitor. We apply a method to detect fraud in a collection of 608k Yelp restaurant reviews using bi-directional Long-Short Term Memory (LSTM) networks: one that examines 12 linguistic features and another that uses 6 behavioral features. We then add a third network with a K-L divergence feature to measure the term differences for each review type. We combine these three networks to form an ensemble approach, producing an AUC (average precision) of 0.8930, surpassing existing state-of-the-art systems applied to similarly labeled Yelp datasets." @default.
- W4319430387 created "2023-02-09" @default.
- W4319430387 creator A5048219247 @default.
- W4319430387 date "2022-10-01" @default.
- W4319430387 modified "2023-09-25" @default.
- W4319430387 title "Combining Linguistic and Behavioral Clues to Detect Spam in Online Reviews" @default.
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- W4319430387 doi "https://doi.org/10.1109/icebe55470.2022.00017" @default.
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