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Data & Behavioral Surplus

Data Collection | Statistical Analysis | Qualitative Research

My Role



Systems that make decisions about people based on their data can severely limit their choices and opportunities. These systems intervene in our experiences to shape behavior in ways that benefit capitalists. While using the internet, we unknowingly give out personal data, known as data exhaust. This data fuels the targeted ads industry and allows for the study of user behavior.

Data Collection

This thesis research aims to validate the theory of interpreting behavioral data to make inferences about users. The research was conducted using information from the  Co-Investigator’s

(Aman Singh) LinkedIn account’s connections which were publicly available and had been consented to before sharing. Names of all connections were masked with the use of pseudo names. A written consent was taken from all connections prior to the start of the research to give them a free choice of opting out.

LinkedIn Data Exhaust

Crucial Insights

Recognized interaction patterns from participants:

  • One participant always liked a post and then shared it on their wall.

  • One participant consistently used the same set of hashtags on all their LinkedIn posts.

  • Some participants had specific reactions to posts depending on who had posted them.

  • Learned about the interaction habits of participants' connections by reviewing their posts.


Noted certain changes suggested by LinkedIn:

  • LinkedIn suggested adding ‘Open To Work’ to their profile, even though they had recently updated their job.

  • Inferred that this suggestion was due to frequently viewing different profiles while working on the project.

My Learnings

By the end of this study, I gained a deeper understanding of how various prediction algorithms function for users across different online platforms. This included studying the underlying data processing techniques and logic behind these systems. Additionally, how these algorithms personalize content, advertisements, and recommendations by examining user behavior, preferences, and interaction patterns.

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