In the first post of this series, the method of collection was laid out, biases involved in the data collected were explained, and some initial observations to start things off were offered.
As of current, the collection has temporarily stopped. It should also be noted that Connecticut has the largest number of fake profiles with regard to scammers, when looking at the charts below. This does not mean that the state has the most scammer activity in the whole US. The reason for the high number is it was the target geographical area of collection. The other states resulted in going down the rabbit hole when tracking scammers who use multiple profiles in multiple geographic locations. This is one of the reasons more data from around the country is needed. The entire sample size is 56 data points. With this information in mind, lets look at what the current data is.
Locally, Hartford is the largest geographic target with twelve fake profiles observed. Following Hartford is East Hartford with four profiles and Norwalk with three.
The question at hand is, why Hartford? It’s the fourth largest city in Connecticut. It’s also not the richest. Anything is speculation at this point and boils down to what was not collected and sample size.
The following chart is a result of following operators of several profiles that span across the US including Connecticut. Again, since Connecticut was the main target, other towns/cities were discovered by searching the screen name of the fake profile to find other fake profiles using the same images and profile summary.
Lastly is this chart by state. Following the string of fake profiles and taking Connecticut out of the mix, New York and Kentucky were the most observed target areas.
What does this all mean?
While not a statistically significant study, and more observational at this point, one can draw a conclusion that a scammer would want to choose more populated areas to increase their chances attracting a victim. This would mean cities are a good place to start.
And questions pop up when thinking about this. Are these scammers indiscriminate in their geographic targets or do they take into account population, age, economy, education, and cultural makeup of where they set their traps?
If anyone reading this is also gathering the same type of data, please contact us.
