Online Deception Detection Refueled by Real World Data Collection

Wenlin Yao, Zeyu Dai, Ruihong Huang, James Caverlee

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Abstract
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 reviews1 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.
Anthology ID:
R17-1102
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
793–802
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_102
DOI:
10.26615/978-954-452-049-6_102
Bibkey:
Cite (ACL):
Wenlin Yao, Zeyu Dai, Ruihong Huang, and James Caverlee. 2017. Online Deception Detection Refueled by Real World Data Collection. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 793–802, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Online Deception Detection Refueled by Real World Data Collection (Yao et al., RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_102
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