Data Fusion for Better Fake Reviews Detection (2023)

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

## introduction the internet has become an essential tool for people in their daily lives, serving not only for workrelated purposes but also personal entertainment, particularly in searching for products or services. traditional methods of promoting businesses have become outdated, with social media and online marketing emerging as more efficient ways to engage with customers globally. as a result, organizations and businesses compete to persuade people to purchase or use their products or services, sometimes resorting to negative practices such as promoting fake reviews. these biased, manipulated and misleading activities impact both customers and businesses, as prospective buyers rely on online user-generated reviews to make informed purchasing decisions and gain insights from others' experiences with products or services of their interest. meanwhile, businesses depend on reviews for valuable feedback and maintaining a positive reputation. the presence of inauthentic and low-quality reviews raises concerns about their trustworthiness and poses challenges for consumers and businesses in the digital marketplace. malicious users frequently post fake reviews (frs) to deceive customers by promoting or demoting products or specific retailers intentionally. fr authors may manipulate customer choices in favor of companies they are affiliated with or against competitors, making frs a lucrative business. according to a harvard business school report @xcite , the percentage of fake reviews on yelp increased from 5% in 2006 to 20% in 2013, making detecting frs a crucial challenge to tackle. unlike traditional text analytics, which focuses on domains such as labeling news stories or grouping disease reports based on severity, fr mitigation methods directly confront fr authors' intentions, resulting in a unique gamification dynamic. this requires data-driven fr solutions to rely on more general or higher-level data representations instead of simple lexical ones based on words, phrases, and sentences. fr filters using higher-level, generic features are expected to be more robust and resistant to straightforward workarounds by fr authors, such as word and phrase replacements. moreover, higher-level features may display limited volatility across domains, making fr detection methods based on them more adaptable across different domains. in this study, we present a comprehensive assessment of different data representations constructed using embeddings for the critical task of detecting frs. our analysis delves into the exploration of a range of deep learning models, as well as the application of various data fusion techniques, in order to develop an effective approach to combating fr problem. the central emphasis of our study is on the utilization of different data representations to enhance performance of our detection methods. to ensure the validity and reliability of our findings, we implement and analyze four distinct datasets, each specifically designed for the purpose of detecting fr in the digital landscape. wang et al. (2018) studied n-gram combinations and test naïve bayes (nb) and support vector machine (svm) classifiers on the yelp dataset. @xcite suggested extracting noun phrases for fake review detection, arguing that spammers often modify aspect sentiments due to their limited product knowledge. in recent years, word and document embeddings have gained popularity as data representations for fr detection @xcite @xcite . @xcite proposed combining bag-of-words, emotion, and word embeddings representations for document and sentence-level representations. some work explored ensemble learning methods for detecting fr in recent years. @xcite proposed an ensemble learning framework that relied on three different models trained (cnn textual, cnn non-textual, cnn behavioral). taneja and kaur (2021) focused on fake feedback detection with ensemble classification, training three different classifiers using the labeled cloudarmor dataset and combining their results using the soft voting ensemble method. gutierrez-espinoza et al. (2020) employed three ensemble learning techniques (boosting, bagging, and stacking) with four different classifiers on their "restaurant dataset". while many studies explored different data representations, to our knowledge, no study uses various embedding data representations such as documentlevel, n-grams, emotion, and noun phrases embedding for fr detection and also in combination with different machine learning algorithms. we hypothesise that different data representations provide complementary information and hence combining them can improve the fr detection process. we explore different data fusion approaches including early fusion performed via data concatenation and late fusion with application of ensemble learning techniques. ensemble learning allows to combine the predictions of different models to reduce the impact of individual model biases and errors, resulting in more robust and reliable predictions. by employing data fusion in fr detection task, we aim to leverage the strengths of individual data representation and deep learning algorithms to improve overall performance. ## methodology in this section, we first discuss the different data representations explored in this study. these include the review document level, emotions, noun phrases, unigram, bigram, trigram, a combination of unigram and bigram (bigrams), and a combination of unigram, bigram, and trigrams. all of these are represented as embedding vectors. furthermore, we discuss the fusion techniques that are integrated with five deep learning algorithms, namely bi-lstm, lstm, gru, cnn, and mlp. to evaluate the performance of our proposed method, we utilize k-fold cross-validation with a k value of 15. we report the final average f1 score for each model. ## experimental results and discussion we assume that different data representations may contain complementary information that are useful for fr detection. we are going to investigate whether this is the case and whether combining them may provide better performance. there are two research questions that will be addressed in this study. ## conclusion in conclusion, this study provides a comprehensive evaluation of different embedding data representations for detecting fr. by employing various deep learning algorithms, we investigate the effectiveness of different embedding data representations, including document, n-grams, emotion, noun phrase. additionally, we apply ensemble learning techniques to improve the detection performance further. our experiments on four distinct datasets demonstrate that the combination of different data representations significantly enhances the performance of fr detection, outperforming single data representations. looking forward, future work can explore the integration of additional data representations and feature engineering techniques to improve the detection accuracy further. for instance, using attention mechanisms and transformers in neural networks could help to identify important parts of the review text and capture the contextual information more effectively. additionally, incorporating user and product information may provide additional insights and improve the detection of fr.