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        <title>Data Fusion for Better Fake Reviews Detection</title>
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        <namePart type="given">Alimuddin</namePart>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Jurek-Loughrey</namePart>
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    <name type="personal">
        <namePart type="given">Deepak</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding for mat, for fake reviews detection. We evaluate these representations with various state-of-the-art deep learning models, such as BILSTM, LSTM, GRU, CNN, and MLP. Following this, we propose to incorporate different data repre- sentations and classification models using early and late data fusion techniques in order to im- prove the prediction performance. The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp. The results demonstrate that combination of different data representations significantly outperform any of the single data representations</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.79</url>
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%0 Conference Proceedings
%T Data Fusion for Better Fake Reviews Detection
%A Melleng, Alimuddin
%A Jurek-Loughrey, Anna
%A P, Deepak
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F melleng-etal-2023-data
%X Online reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using various data representations such as n-grams or document embeddings. In this paper, we first explore the effectiveness of different data representations, including emotion, document embedding, n-grams, and noun phrases in embedding for mat, for fake reviews detection. We evaluate these representations with various state-of-the-art deep learning models, such as BILSTM, LSTM, GRU, CNN, and MLP. Following this, we propose to incorporate different data repre- sentations and classification models using early and late data fusion techniques in order to im- prove the prediction performance. The experiments are conducted on four datasets: Hotel, Restaurant, Amazon, and Yelp. The results demonstrate that combination of different data representations significantly outperform any of the single data representations
%U https://aclanthology.org/2023.ranlp-1.79
%P 730-738

Markdown (Informal)

[Data Fusion for Better Fake Reviews Detection](https://aclanthology.org/2023.ranlp-1.79) (Melleng et al., RANLP 2023)

• Data Fusion for Better Fake Reviews Detection (Melleng et al., RANLP 2023)

ACL

• Alimuddin Melleng, Anna Jurek-Loughrey, and Deepak P. 2023. Data Fusion for Better Fake Reviews Detection. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 730–738, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.