Uncovering Patterns in My Posts and Engagement — A data related one-year journey
Introduction
The leading professional networking platform today is LinkedIn. I began my journey there several years ago sharing information about my work and job title. However, I decided to focus more intensely on creating content related to my new work experience in data & analytics over the past year. Specifically, I have been posting and sharing stories about leadership, team development, and geospatial analytics, including visualization of data and graph theory.
From LinkedIn (LI), you can extract various statistics like impressions, interactions and daily follower growth. Additionally, there is a LI API that can be used to obtain more detailed statistics. Over the past year, I have collected data on my own LI posts, with the aim of demonstrating how data analytics can be applied on such datasets. In this article, I will share what I have learned through one year of tracking my LI activity.
In the first part, I will discuss soft factors such as audiences, measurements, collecting data, tools and standards. Then, I will provide a more detailed descriptive analysis with several data-oriented outcomes. How will a post perform over weeks and how can one find out how hashtags work? These will be the topics for the last two sections. If you find this interesting, please consider clapping, following, or sharing it on medium.
Audience — Interaction — Measurement
On LI, you can measure a post’s success through metrics such as passive impressions (i.e., how many times your post has been displayed to others) and active engagement metrics like likes, comments, and shares. As an example, I have shared a post in the past year about code quality and readability, which you can see represented in the following screenshot. The LI algorithm affects how many people will see your post, but the numbers of likes you receive depends on your audience. To better understand this algorithm and my audience’s preferences, I have collected my own dataset over the past year and analyzed it to identify patterns and trends. Let me now describe this dataset in more detail.