Simple Research Study – Mining Facebook’s Rexburg Garage and Yard Sales

Comment Count Distrubution

Introduction

Background

A few years ago, a group started to show up on Facebook called Rexburg Garage and Yard Sales. This Facebook group is not unique only to Rexburg. These groups started to show up all over, for many cities, especially the larger cities. Since these groups started coming live, I thought, can we learn something with the data inside these groups, thus starting this study. This micro-research study will be analyzing and mining Facebook’s Rexburg Garage and Yard Sales data.

Statement of Purpose

The purpose of this research study is to show what kind of information we can get from mining Facebook’s Rexburg Garage and Yard Sales group.

Methodology

About Rexburg Garage and Yard Sales

Name: Rexburg Garage and Yard Sales

Total Members: 25,532

About the Data Set

Snapshot: 4/19/2017 2:00pm

Total Posts Pulled: 1,000

1,000 posts were pulled as of when they were updated as of the time stamp above.

The raw data set can be downloaded from here.

Results

Date Posted

We can see with this graph, the most recent posts not only include posts from this month, but we include posts from October till April.

Total Posts Over Time

However, with this part of the graph, we can see the number of Facebook posts that were created during this month contains a large chunk of the data set. From the graph above, we can see that over 70% of the Facebook posts were posted within the past day or two.

Helper Total Posts Over Time

ISOs

ISO on Facebook stands for “in search of.” Often times people will post ISO in their title, which will cause others to respond with suggestions. For this data set:

Total ISOs: 86

Total Non-ISOs: 914

We can see that the total ISO posts were posted over the past couple of days.

Total ISO Posts

Posts by Users

Of the 689 users that made up the 1,000 posts, the graph below is a distribution of these posts, sorted by quantity. We can see that there are a few users with a lot of posts. In fact, 91% of the users post 3 times or less.

Total Posts by User

“Like” Count Distribution

We can see that most posts have 0-1 likes. In fact, 7 out of 10 posts won’t receive a like.

Like Count Distribution

Comment Count Distribution

We can see by this, 2 out of 5 posts won’t receive a comment.

Comment Count Distrubution

Conclusion

From the data and the research above, we can conclude a few things:

  1. 2 out of 5 posts won’t receive a comment
  2. Nearly 7 out of 10 posts won’t receive a like
  3. 91% of the posts will be from users who posted 3 times or less

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