Uncover your posting and engagement patterns — A year of data journeys
introduction
The leading professional networking platform today is LinkedIn. I started my journey there a few years ago sharing information about my job and position. However, I decided to focus more on creating content related to my new work experience in data and analytics over the past year. Specifically, I've posted and shared stories about leadership, team development, and geospatial analysis, including data visualization and graph theory.
LinkedIn (LI) allows you to extract various statistics such as impressions, interactions, and daily follower growth. In addition, there is also an LI API that can be used to obtain more detailed statistics. Over the past year, I've been collecting data about my LI posts. This aimed to demonstrate how data analysis can be applied to such datasets. In this article, I will share what I learned from following his LI activities for a year.
The first part covers soft elements such as audience, measurement, data collection, tools, and standards. Next, we provide a more detailed descriptive analysis, including some data-oriented results. How will your posts perform over several weeks? And how can you see how your hashtags work? These will be the topics of the final two sections. If you find it interesting, please consider clapping, following, or sharing it in the media.
Audience – Interaction – Measurement
LI allows you to measure the success of your posts through metrics such as passive impressions (the number of times your post is seen by others) and active engagement metrics such as likes, comments, and shares. As an example, I shared a post last year about code quality and readability. You can see it shown in the following screenshot. The LI algorithm influences how many people see your posts, but the number of likes you receive depends on your audience. To better understand this algorithm and audience preferences, I collected a unique dataset over the past year and analyzed it to identify patterns and trends. Let's discuss this dataset in more detail.