Combining Textual and Visual Features to Identify Anomalous User-generated Content

Anomaly detection has extensive use in a wide variety of applications, such techniques aim to find patterns in data that do not conform to expected behavior. In this work we apply anomaly detection to the task of discovering anomalies from user-generated content of commercial product descriptions. While most of the other works in literature rely exclusively on textual features, we combine those textual descriptors with visual information extracted from the media resources associated with each product description. Given a large corpus of documents, the proposed system infers the key features describing the behavioral traits of expert users, and automatically reports whenever a newly generated description contains suspicious or low quality textual/visual elements. We prove that the joint use of textual and visual features helps in obtaining a robust detection model that can be employed in an enterprise environment to automatically mark suspicious descriptions for further manual inspection.