Multi-task Ensemble Learning for Fake Reviews Detection and Helpfulness Prediction: A Novel Approach (2023)

Abstract: Research on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate research tasks. In contrast to this prevailing pattern, we address both challenges concurrently by employing a multi-task learning approach. We posit that undertaking these tasks simultaneously can enhance the performance of each task through shared information among features. We utilize pre-trained RoBERTa embeddings with a document-level data representation. This is coupled with an array of deep learning and neural network models, including Bi-LSTM, LSTM, GRU, and CNN. Additionally, we em- ploy ensemble learning techniques to integrate these models, with the objective of enhancing overall prediction accuracy and mitigating the risk of overfitting. The findings of this study offer valuable insights to the fields of natural language processing and machine learning and present a novel perspective on leveraging multi-task learning for the twin challenges of fake reviews detection and review helpfulness prediction

## introduction the proliferation of online marketplaces has significantly altered the way consumers purchase goods and services. as part of this transformation, usergenerated reviews have become a vital factor in influencing purchasing decisions. however, the increased reliance on these reviews has given rise to an unsettling phenomenon: the spread of deceptive or "fake" reviews. fake reviews, either overly positive or overly negative, can distort the perceived quality or popularity of products or services, misleading consumers and affecting businesses' reputations. simultaneously, the concept of review helpfulness has emerged as another crucial aspect of user-generated reviews. helpfulness prediction aims to rank and highlight reviews that potential consumers would find most useful. it is based on the premise that not all reviews provide the same value to consumers, and certain reviews are more informative and helpful than others. accurate helpfulness prediction can thus enhance the shopping experience by guiding consumers towards reviews that offer the most beneficial insights. an example of helpful and unhelpful review: helpful: "i purchased this phone two weeks ago and have been using it ever since. the battery life is impressive, and the screen is bright and colourful. the camera produces high-resolution images, especially in night mode, which delivers fantastic results." unhelpful: "i bought this phone as a gift for my daughter and she's happy with it. the delivery was quick and the packaging was satisfactory." recently, multi-task learning, a paradigm of machine learning, has been recognized as a promising approach to improve the performance of related tasks @xcite @xcite . multi-task learning operates on the principle that learning multiple tasks simultaneously, leveraging shared representations, can lead to improved generalization by exploiting commonalities and differences across tasks. in the context of fake reviews detection (frd) and helpfulness prediction (hp), these tasks are closely related as they both involve understanding the content and context of reviews to make predictions. this study seeks to apply the principles of multitask learning, combined with ensemble learning strategies, to the tasks of frd and hp. the objective is to harness the shared information between these tasks to enhance the effectiveness of frd and the accuracy of hp. the commonalities and inter-task correlations learned in one task can be shared and used to reinforce the feature learning of the other task, thereby boosting the overall performance of both tasks. ensemble learning is incorporated to further optimize the model's performance. it combines predictions from multiple models to generate a final prediction, thereby capitalizing on the strengths of each individual model while mitigating their weaknesses. the utilization of ensemble learning techniques further strengthens the robustness of our approach, enhancing the precision and reliability of our predictions. to the best of our knowledge, this is the first study that employs a multi-task learning approach integrated with ensemble learning for simultaneous frd and hp. this paper presents the design, implementation, and evaluation of our proposed multi-task ensemble learning model, providing a novel contribution to the field of online review analysis. ## related work fake review (fr), also referred to as fake opinions, deceptive reviews, deceptive opinions, spam reviews, or spam opinion, present a challenge in online platforms. the primary objective of frd is to determine whether a review is genuine or fraudulent. over the past decade, myriad studies have endeavored to devise more effective methodologies to uncover these fraudulent reviews. these methodologies leverage a range of techniques, each aiming to optimize the detection performance. several studies employ machine learning methodologies such as support vector machines (svm) @xcite @xcite @xcite @xcite , naive bayes @xcite , logistic regression @xcite , and decision trees @xcite . on the other hand, some research explores the utility of deep learning techniques. these include long short-term memory (lstm) networks @xcite , convolutional neural networks (cnn) @xcite , bidirectional long short-term memory (bi-lstm) networks @xcite , and gated recurrent units (gru) @xcite . research on online reviews encompasses not just the detection of frs, but also the evaluation of review helpfulness @xcite , and even the use of reviews for rec-ommendation or ranking based on the helpfulness @xcite . the examination and understanding of online reviews provide a wealth of insights that can be harnessed to enhance user experiences, refine products and services, and inform business strategies. the advent of machine learning and deep learning techniques has significantly amplified the potential for extracting meaningful information from these reviews. such information serves as a valuable resource for customers, aiding them in making informed decisions @xcite . @xcite effectively identified helpful reviews by employing three distinct approaches: a supervised approach (fasttext, svm, bi-lstm, cnn, rcnn), a semi-supervised approach (rcnn), and a pre-trained model approach (bert and roberta), using an amazon dataset across four domains. their comparative analysis revealed that among all the approaches, the rcnn model demonstrated superior performance. although there has been extensive research on online reviews, particularly in the areas of frd and hp, to the best of our knowledge, no existing work has undertaken the task of combining these two areas of study. multi-task learning (mtl) have the potential to outperform those focused on single tasks learning (stl). the effectiveness of mtl can be attributed to its capacity to leverage a larger volume of data from various learning tasks, compared to stl models. with access to a more diverse dataset, mtl models are capable of learning more robust and universally applicable patterns for multiple tasks, resulting in the development of more powerful models. in the realm of mtl for frd, @xcite have made significant contributions for mtl for frd for multiple domain datasets. they devised an mtl-logistic regression (mtl-lr) model and an advanced variant known as semi-supervised multitask learning through laplacian regular logistic regression (smtl-llr). this latter model was designed to improve performance with unlabeled data, and it indeed outperformed its mtl-lr counterpart as well as other conventional models such as svm, lr, and semi-supervised positive-unlabeled (pu) learning. @xcite utilized mtl for review helpfulness prediction and star rating regression. they achieved this by employing a cnn model to simultaneously perform two tasks: helpfulness identification and star rating regression. their approach incorporated two kinds of input: character-level embeddings and word-level embeddings, extracted from two separate amazon datasets, namely amazon clothes and electronics. in a similar work, @xcite proposed a multi-task dual attention recommendation model (darmh) for both review helpfulness and rating prediction. this work utilized word embeddings and user id embeddings from a specific amazon dataset. the researchers demonstrated that darmh exhibited a 3.9%-5.4% performance improvement compared to other rating prediction algorithms. from our investigation, it is apparent that only a limited number of studies have ventured into the application of mtl for the dual challenges of frd and review helpfulness. our research stands out by uniquely integrating mtl with ensemble learning, a strategy that simultaneously addresses these two tasks. we innovatively utilize document-level embeddings-a type of data representation-to exploit shared information and correlations inherent in these tasks, thereby boosting both the detection accuracy of frs and the prediction precision for review helpfulness. to the best of our understanding, this research is pioneering in its exploration of an ensemblebased mtl framework, specifically tailored for frd and hp using document-level embeddings. consequently, our study marks a significant contribution by comparing results across diverse multitask ensemble models, thereby highlighting the unique advantages of this novel combination. ## methodology in this section, we propose multi-task learning (mtl) of frd and hp. in this research, we run mtl on five different algorithms (bi-lstm, lstm, gru, cnn, and mlp). two objectives are focused on mtl: implementation of mtl for frd and hp and mtl-ensemble. to evaluate the performance of our proposed method, we utilize k-fold cross-validation with k values of 15. we report the final average f1 score for each model. ## experimental results and discussion in this study, we want to investigate whether mtl for frd and hp may provide better performance. there are three research questions that will be explored. to ensure the robustness of our results, we implement k-fold cross-validation with k set at 15 for all models. additionally, for the random forest and svm models used in the ensemble learning approach, we apply the same 15-fold cross-validation strategy. ## conclusion in conclusion, this research offers an in-depth evaluation of the application of mtl for the simultaneous detection of frs and prediction of review helpfulness. by focusing on document-level embedding as the sole data representation, a departure from conventional methods, this study presents a streamlined and efficient approach. the findings suggest that mtl consistently outperforms stl in these tasks, illuminating the potential benefits of this method in real-world applications. in addition to the main task, this study also investigates the use of ensemble learning, based on prediction scores, as a means to enhance the results. the comparative performance of stl and mtl under different ensemble methods underscores the robustness of mtl in this context. the findings of this study open a promising path for future work, which could explore further optimization of data representations or model architectures. for example, more sophisticated attention mechanisms or transformer models could be employed to better capture and utilize the semantic richness in the reviews. additional features, such as user and product information, could also be integrated into the model to potentially provide deeper insights and further improve performance in both frd and hp.