<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Purpose</h3><p>This paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.</p></sec><sec><h3>Design/methodology/approach</h3><p>Based on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.</p></sec><sec><h3>Findings</h3><p>Our findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.</p></sec><sec><h3>Research limitations</h3><p>The analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com.</p></sec><sec><h3>Practical implications</h3><p>The results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.</p></sec><sec><h3>Originality/value</h3><p>To the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.</p></sec></abstract>

PurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.Design/methodology/approachBased on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.FindingsOur findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.Research limitationsThe analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com.Practical implicationsThe results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.Originality/valueTo the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Effective Opinion Spam Detection: A Study on Review Metadata Versus Content"}

PurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam...

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Article Category: Research Paper

Data publikacji: 20 maj 2020

Zakres stron: 76 - 110

Otrzymano: 21 sty 2020

Przyjęty: 23 kwi 2020

DOI: https://doi.org/10.2478/jdis-2020-0013

Słowa kluczowe
Opinion spam, Behavioral features, Textual features, Review spammers, Spam-targeted products

© 2020 Ajay Rastogi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Effective Opinion Spam Detection: A Study on Review Metadata Versus Content

Article Category: Research Paper

Data publikacji: 20 maj 2020

Zakres stron: 76 - 110

Otrzymano: 21 sty 2020

Przyjęty: 23 kwi 2020

DOI: https://doi.org/10.2478/jdis-2020-0013

Słowa kluczoweOpinion spam, Behavioral features, Textual features, Review spammers, Spam-targeted products

© 2020 Ajay Rastogi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Opinion spam, Behavioral features, Textual features, Review spammers, Spam-targeted products