Incorporating Multimodal Cues for Advertorial Discovery

Abstract

Commercial advertorials shared on websites are usually designed to pretend as normal social news for commercial benefits. The analysis of the commercial intents embedded in advertorials can greatly help media platforms personalize content. However, commercial intents are not only concealed in news texts but also conveyed by news images explicitly or implicitly. Consequently, how to effectively extract and incorporate the crucial cues of multiple modalities has been emerging as an important but challenging problem. Motivated by this observation, we propose a framework Multimodal Advertorial Discovery Model (MADM) to estimate the commercial intents embedded in the multimodal social news. Specifically, a novel Cross-graph Fusion (CGF) strategy is developed to achieve a soft assignment to incorporate images and text and generate comprehensive multimodal representations. The extensive evaluations demonstrate the superiority of our proposed system in multimodal-based advertorial detection and analysis.