FAQ Readers Redux
Another year, another HackMIT FAQ readers experiment. From May 31 to August 7, we had the following item in HackMIT’s FAQ:
Like last year, the experiment wasn’t particularly scientific. We just wanted to give people the opportunity to email us random stuff.
Last year, we did a qualitative analysis of the emails we received, so for a change, this year, we took a very quantitative approach.
We had 227 unique individuals email us (compared to 493 last year). We responded to 220 of these people, giving us a response rate of 97% (up from 80% last year).
The distribution of response times was pretty good:
A handful of team members were responsible for the majority of responses:
Some team members got pretty competitive trying to be the fastest to respond to emails:
Even though the experiment began on May 31, we received a lot more email once registration opened on July 1st, receiving the greatest number of emails on July 2nd:
People did sent us email at all hours, but the morning seemed to be the least popular time to send messages:
Unsurprisingly, gmail.com and mit.edu were the most popular email domains among FAQ readers:
Emoji were quite popular in our emails. Among all the emails that were sent and received, here are the most used emoji:
Apparently, the HackMIT team really loves emoji, being responsible for 67% of the total emoji use:
Luckily, the majority of emails we received were positive (according to a sentiment analysis engine):
Here’s the most negative email we received:
And here’s the most positive:
Okay, so most of this data analysis is pretty silly. It’s not meant to be taken too seriously! We had a great time going overboard and making pretty graphs.
In case anyone is curious about how we did the analysis, here’s a short summary. We archived all the emails that were sent to email@example.com, and after the conclusion of the experiment, we loaded all the data into a Jupyter notebook using Python’s mailbox library. We analyzed the data using NumPy, pandas, NLTK, TextBlob, talon, and emoji, and we made the graphs using matplotlib and Seaborn.