Five Steps to Get Facebook Engagement Indicators

• Created by The Curiosity Bits Blog (curiositybits.com)

• With the support from Dr. Gregory D. Saxton

What are Facebook Engagement Indicators?

• Facebook users interact with content through liking, sharing and commenting (L-S-C). The L-S-C represents a hierarchy of user engagement.

commenting

sharing

liking

Content lifespan

Getting ready!

• In this tutorial, you will learn how to get:• # of likes, shares and comments for all

posts on a Facebook page.• Content lifecycle: the amount of time for

how long the content can drive user attention and engagement.

Download the codehttps://

drive.google.com/file/d/0Bwwg6GLCW_IPZlpVOHZ2amZJR2c/edit?usp=sharing

Getting ready!

• Please complete all steps in our previous tutorial: Mining Facebook Fan Page – getting posts and comments (http://curiositybits.com/python-for-mining-the-social-web/python-tutorial-mining-facebook-fan-page-getting-posts-and-comments/)

• From the previous tutorial, you have generated a SQLite database that includes:• Posts: all posts on a Facebook page

• Content, posted time, included URLs, mentioned Facebook friends/pages, etc.

• Comments: comments to the posts• Sender, content, posted time, etc.

Step 1: Checklist• Do you know how to install necessary

Python packages? If not, please review pg.8 in http://curiositybits.com/python-for-mining-the-social-web/python-tutorial-mining-twitter-user-profile/

• Do you know how to browse and edit SQLite database through SQLite Database Browser? If not, please review pg.10-14 in http://curiositybits.com/python-for-mining-the-social-web/python-tutorial-mining-twitter-user-profile/

Have you installed these necessary Python libraries?

Step 1: Checklist

Step 2: creating new columns

• In previous tutorial, we have created various columns in the database. But in this run, there are THREE columns defined in the script, but are NOT existent in the current database. We need to create them manually through SQLite Database Browser.

Three columns used in the script, but are not yet created in SQLite database.

Step 2: creating new columns

Now let’s create the three columns:

• In SQLite Database Browser, choose [Edit] – [Modify Table] – [Edit] – [Add Field]

name type

Feed_id Integercontent_cycle Stringcontent_cycle_new String

Step 2: creating new columns

• Final check: make sure all columns defined in the script are existent in the SQLite database.

All columns defined in the current script.

Step 2: creating new columns

Step 3: Connecting to the existing DB

Now, Python is asked to connect to the existing database through the following block of code:

• Use a shorten file path if the current SQLite database is in the same folder with the Python code. IF NOT, use a full file path such as sqlite:///C:/xxxx/xxx/xx.sqlite

• Please save the Python code in your default Python folder (e.g. __\Anaconda\Lib\site-packages)

• Learn how to find your default Python folder? Review page.25-27 in http://curiositybits.com/python-for-mining-the-social-web/python-tutorial-mining-twitter-user-profile/

For example

Hit RUN!

Step 4: Confirming the engagement indicators are generated

Step 5: Learn how the engagement indicators are calculated

You may wonder how the two engagement indicators - content_cycle and content_cycle_new – are calculated

Simply put, content_cycle = (the time of last comment posted – the time of the post published)

Step 5: Learn how the engagement indicators are calculated

Content_cycle_new is the content lifecycle, after controlling for the amount of time of the content being live.

Specifically,

content_cycle_new = (the time of last comment posted – the time of the post published)/time_since_post

where, time_since_post = the time of the data mining – the time of the post published

Step 5: Learn how the engagement indicators are calculated