Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,00...

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Bibliographic Details
Published inarXiv.org
Main Authors Yao, Wenlin, Dai, Zeyu, Huang, Ruihong, Caverlee, James
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.07.2017
Subjects
Data collection
Deception
Domains
Electronic commerce
Network analysis
Social networks
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Summary:The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.
ISSN:2331-8422