基于真实世界数据汇集的在线欺骗检测
Online Deception Detection Refueled by Real World Data Collection
摘要：
大型真实数据集的缺乏是在线欺骗检测研究的一个瓶颈。在本文中，我们应用数据汇集方法的基础上社会网络分析，以快速识别高质量的欺骗性和真实的在线评论从亚马逊。该数据集包含超过10，000条欺骗性评论，并且在产品领域和评论者中存在差异。使用这个数据集，我们探索有效的一般功能，在线欺骗检测，以及跨域。我们证明，与广义功能-广告说话和写作的复杂性分数-欺骗检测性能可以进一步提高，通过添加额外的欺骗性评论，从各种领域的培训。最后，审稿人水平评估提供了一个有趣的洞察不同的欺骗性审稿人的写作风格。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
