Online Deception Detection Refueled by Real World Data Collection
This work addresses the problem of dataset scarcity for researchers in online deception detection, though it is incremental as it builds on existing methods with new data.
The paper tackled the bottleneck of lacking realistic datasets for online deception detection by collecting over 10,000 deceptive reviews from Amazon using social network analysis, and demonstrated that generalized features like advertising speak and writing complexity improve detection performance when trained on diverse domain data.
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.