English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Online reviews play a crucial role in deciding the quality before purchasingany product. Unfortunately, spammers often take advantage of online reviewforums by writing fraud reviews to promote/demote certain products. It may turnout to be more detrimental when such spammers collude and collectively injectspam reviews as they can take complete control of users' sentiment due to thevolume of fraud reviews they inject. Group spam detection is thus morechallenging than individual-level fraud detection due to unclear definition ofa group, variation of inter-group dynamics, scarcity of labeled group-levelspam data, etc. Here, we propose DeFrauder, an unsupervised method to detectonline fraud reviewer groups. It first detects candidate fraud groups byleveraging the underlying product review graph and incorporating severalbehavioral signals which model multi-faceted collaboration among reviewers. Itthen maps reviewers into an embedding space and assigns a spam score to eachgroup such that groups comprising spammers with highly similar behavioraltraits achieve high spam score. While comparing with five baselines on fourreal-world datasets (two of them were curated by us), DeFrauder shows superiorperformance by outperforming the best baseline with 17.11% higher NDCG@50 (onaverage) across datasets.

Spotting Collective Behaviour of Online Frauds in Customer Reviews