Inspiration
Older adults (65+) are particularly susceptible to scams of various kinds. In particular, perhaps because older adults tend to be more isolated, as well as more wealthy, they are often targeted by romance scams, which develop a relationship between the scammer and victim before attempting to take money or credentials from the victim. We began research with the eventual goal of developing defenses for this population.
What it does
We created various visualizations of the scam data collected by AARP's Scam Map and conducted a survey to collect more data about the prevalence of romance scams among older adults.
How we built it
We scraped information on scams reported to AARP's Scam Map. After cleaning this data, we conducted automated analysis with Pandas in Python and conducted qualitative analysis on a random sample. We also conducted a survey with Qualtrics.
Challenges we ran into
We weren't able to simultaneously code on VSCode with Live Share due to problems with the Python Interpreter. Eventually, we had to move to Google Colab, which had its own issues with synchronization; our final solution was to code on different Colab notebooks and combine our code at the end.
We also had some trouble with reading in the data; originally, the csv was read in with unsuable datatypes.
Accomplishments that we're proud of
Being able to overcome our challenges with the dataset, and deciding what questions to answer. We're also proud of our work towards protecting older adults from scams.
What we learned
How to work as a team, how to work with multiple kinds of data, how to make inferences and conclusions, how to hypothesize, and go through the research pipeline.
What's next for Hi. I love you. Please send me gift cards”
We've drawn some conclusions from the data, which we can use to pursue new questions.
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