{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T16:43:01Z","timestamp":1769704981063,"version":"3.49.0"},"reference-count":45,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:p>The large flux of online products in today\u2019s world makes business reviews a valuable source for consumers for making sound decisions before making online purchases. Reviews are useful for readers in learning more about the product and gauge its quality. Fake reviews and reviewers form the bulk of the review corpus, making review spamming an open research challenge. These spam reviews require detection to nullify their contribution to product recommendations. In the past, researchers and communities have taken spam detection problems as a matter of serious concern. Yet, for all that, there is space for the performance of exploration on large-scale complex datasets. The work contributes towards robust feature selection with derived features that provide more details on malicious reviews and spammers. Ensemble and other standard machine learning techniques are trained and evaluated over optimal feature sets. In addition, the Metapath-based Graph Convolution Network (M-GCN) framework is proposed, which is an implicit knowledge extraction method to automatically capture the complex semantic meaning of reviews from the heterogeneous network. It makes analysis of triplet (users, reviews, and products) relationships in e-commerce sites through examination of Top-n feature sets in a mutually reinforcing manner. The proposed model is demonstrated on Yelp and Amazon benchmark datasets for evaluation of efficacy and it is shown outperforming state-of-the-art techniques with and without graph-utilization, providing an accuracy of 96% in the prediction task.<\/jats:p>","DOI":"10.3233\/jifs-223136","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:11:44Z","timestamp":1686651104000},"page":"3005-3023","source":"Crossref","is-referenced-by-count":2,"title":["Spam review detection with Metapath-aggregated graph convolution network"],"prefix":"10.1177","volume":"45","author":[{"given":"P.","family":"Jayashree","sequence":"first","affiliation":[{"name":"Department of Computer Technology, MIT campus, Anna University"}]},{"given":"K.","family":"Laila","sequence":"additional","affiliation":[{"name":"Department of Computer Technology, MIT campus, Anna University"}]},{"given":"Aara","family":"Amuthan","sequence":"additional","affiliation":[{"name":"Department of Computer Technology, MIT campus, Anna University"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-223136_ref1","unstructured":"Ram, Nikhil Sai Chandra , Vakati Gowtham , Nadimpalli Jagadesh Varma , Sah Yash and Datla Sai Karthik , Fake Reviews Detection Using Supervised Machine Learning."},{"key":"10.3233\/JIFS-223136_ref2","first-page":"1","article-title":"Deceptive opinion spam based on deep learning","author":"Anass","year":"2020","journal-title":"In 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS)"},{"key":"10.3233\/JIFS-223136_ref4","first-page":"281","article-title":"Review spam detection using semi-supervised technique","author":"Narayan","year":"2018","journal-title":"Progress in Intelligent Computing Techniques: Theory, Practice, and Applications"},{"key":"10.3233\/JIFS-223136_ref5","unstructured":"Akram, Abubakker Usman , Khan Hikmat Ullah , Iqbal Saqib , Iqbal Tassawar , Munir Ehsan Ullah and Shafi Muhammad , \u201cFinding rotten eggs: A review spam detection model using diverse feature sets, (2018)."},{"issue":"7","key":"10.3233\/JIFS-223136_ref6","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/TIFS.2017.2675361","article-title":"NetSpam: A network-based spam detection framework for reviews in online social media","volume":"12","author":"Shehnepoor","year":"2017","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"1","key":"10.3233\/JIFS-223136_ref7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1049\/cje.2015.01.009","article-title":"Finding deceptive opinion spam by correcting the mislabelled instances","volume":"24","author":"Ren","year":"2015","journal-title":"Chinese Journal of Electronics"},{"key":"10.3233\/JIFS-223136_ref8","doi-asserted-by":"crossref","first-page":"42934","DOI":"10.1109\/ACCESS.2019.2908495","article-title":"Learning to Detect Deceptive Opinion Spam: A Survey","volume":"7","author":"Ren","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-223136_ref9","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1145\/2783258.2783370","article-title":"Collective opinion spam detection: Bridging review networks and metadata","author":"Rayana","year":"2015","journal-title":"Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining"},{"key":"10.3233\/JIFS-223136_ref10","unstructured":"D\u2019Onfro J. , A whopping 20% of Yelp reviews are fake. https:\/\/www.businessinsider.com.au\/20-percent-of-yelp-reviews-fake-2013-9). 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