As the popularity of free-form usergenerated reviews in e-commerce and review websites continues to increase, there is a growing need for automatic mechanisms that sift through the vast number of reviews and identify quality content. Online review helpfulness modeling and prediction is a task which studies the factors that determine review helpfulness and attempts to accurately predict it. This survey paper provides an overview of the most relevant work on product review helpfulness prediction and understanding in the past decade, discusses gained insights, and provides guidelines for future research.

Recent years have seen significant advances in machine perception, which have enabled AI systems to become grounded in the world. While AI systems can now "read" and "see", they still cannot read between the lines and see through the lens, unlike humans. We propose the novel task of hidden message and intention identification: given some perceptual input (i.e., a text, an image), the goal is to produce a short description of the message the input transmits and the hidden intention of its author, if any. Not only will a solution to this task enable machine perception technologies to reach the next level of complexity, but it will be an important step towards addressing a task that has recently received a lot of public attention, political manipulation in social media.

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