From arguments and reviewers to their simulation reproducing a case-study

We propose an exploratory study on arguments in Amazon.com reviews. Firstly, we extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. We accomplish this information extraction manually, scanning all the related reviews. Secondly, we link extracted arguments to the rating score, to the length, and to the date of reviews, in order to undertand how they are connected. As a result, we show that negative arguments are quite sparse in the beginning of such social review-process, while positive arguments are more equally distributed along the timeline. As a final step, we replicate the behaviour of reviewers as agents, by simulating how they assemble reviews in the form of arguments. In such a way, we show we are able to mirror the measured experiment through a simulation that takes into account both positive and negative arguments.