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It's not just loss of sales and reputational damage that Amazon has to worry about. The UK government recently started investigating them for possible failures to protect users from fake reviews. Not doing enough to combat review fraud is potentially breaking the law.
I wanted to understand more about the challenge facing Amazon in clamping down on this, so I chose a review fraud investigation as my first data visualization project.
I also added a control bar on the right full of useful customized options to help me analyze the data. Here's what I see when I load the data for the first time:
Glyphs on combo nodes represent the number of products inside each category
With my focus on this particular customer, I can also zoom in and see where they exist in the network chart.
Using the time bar sliders, I can navigate to the peaks when a relatively high number of Amazon reviews were posted within a short period. The timelines and network chart update dynamically to reflect what's in focus. This kind of activity may rouse suspicions – is a bot responsible? Do they relate to the same product? Did the ratings appear within minutes of the product being listed?
Pattern analysis through time
The heatmap view immediately reveals that reviewers were busiest on Sundays. There may be nothing unusual about this – do customers try out their new products over the weekend and then find time to leave a review?
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