I learned that dictionaries are perfect for storing labeled data, like a user's "handle" or "profile picture," because they map specific keys to values. By nesting these dictionaries inside a list, researchers can organize information for thousands of different social media users in one structured format.

I find the discussion about the ambiguity of yesterday particularly insightful because it highlights how objective data like a timestamp is actually dependent on the observer's context, if a social media platform's automated system flags behavior based on a specific day, but that day starts and ends at different times for the user and the server. This creates a data friction that can lead to unfair outcomes, which indicates that information systems aren't neutral tools, they are specific temporal assumptions, and things might change quickly and might not reflect the lived experience of global users.

The use of loops and conditionals in social media bots highlights a major ethical challenge regarding scale and context. While a human can manually block a few trolls, a bot using a loop can automatically block hundreds of users in seconds based on a simple conditional like an iphone or android tag. This demonstrates how automation doesn't just make tasks easier but changes the power dynamics of online interaction by allowing a single user to exert massive influence without manual effort.

It is striking how specific design features like the location tag ('Hillingdon, London') transformed a digital post into a real-world manhunt. This case proves that platform features are never neutral; by automatically attaching metadata, Twitter provided the tools necessary for users to track Sacco’s flight in real-time, blurring the line between online discourse and physical safety.

I would like to add the framework of Utilitarianism (Jeremy Bentham and John Stuart Mill) to this section. Utilitarianism focuses on the outcomes of actions, aiming for 'the greatest good for the greatest number.' This is particularly relevant to social media algorithms that prioritize overall engagement and user satisfaction metrics, though it often raises ethical concerns when the 'minority' users' experiences are sacrificed for the majority's data trends.