I really appreciated the inclusion of Whitney Phillips’s “Internet Troll Sub-Culture’s Savage Spoofing of Mainstream Media” (Scientific American, 2015) — it gives a grounded, cultural perspective on trolling that complements the ethical discussions nicely. I wonder whether there are more recent studies that explore how trolling has shifted into coordinated “political influence” efforts that would update or extend Phillips’s observations.

I think the section on Trolling and Nihilism raises an important point that some trolling communities don’t just push boundaries playfully but actually seem to treat ethics as irrelevant. What struck me is how this can lead to a kind of moral vacuum where the harm to people or groups is dismissed as part of the “game” of disruption.

The varianceexplained.org analysis of Trump’s tweets is a great example of how data science can uncover patterns behind online personas — it really connects to this chapter’s discussion of authenticity. I found it fascinating how statistical language analysis can expose who’s “really” speaking behind a public account, showing that authenticity online can sometimes be measured rather than just perceived.

I was struck by the tension in the chapter between authenticity as honesty and authenticity as the matching of presentation and interaction. The idea that social media personas can “feel” authentic even when they’re partly performed makes me wonder: in platforms driven by algorithms and engagement metrics, do we discourage real authenticity because performative authenticity “wins” more often?

It’s cool to see Tom Standage’s Writing on the Wall: Social Media — The First 2,000 Years cited here. His historical framing helps us see that social media isn’t entirely new, only evolved. I’d also suggest adding Shoshana Zuboff’s The Age of Surveillance Capitalism as a counterpoint source: it connects design history with power, data extraction, and economic incentives, deepening the discussion of how “design” choices embed commercial values.

I appreciated the tension you raised around friction vs. frictionless design — it really made me think, on one hand, low friction feels like “good usability,” but as you note, design can deliberately add friction to push users toward more thoughtful behavior. I wonder: is there a risk that too much deliberate friction becomes paternalistic or manipulative (assuming users can’t be trusted)?

I’m glad to see Design Justice: Community-Led Practices to Build the Worlds We Need by Sasha Costanza-Chock in the bibliography. This work deeply connects design choices with power, oppression, and equity. It would be interesting to more explicitly surface how “data simplification” (in 4.2) is a design choice that can perpetuate injustice.

I really appreciated how it highlights that any dataset is already a selection — a simplification— and that those choices shape the story the data can tell. And I wonder: in practical terms, how might we audit or compare different simplification choices across platforms to surface which versions produce more (or less) ethical distortions?

It’s clever because it both acknowledges that bias is baked into datasets and algorithms, yet still tries to offer practical design strategies to deal with harms. I’d love to see people push further by testing or critiquing Jeong’s prescriptions in real bot designs: do they scale? Do they fail under adversarial inputs?

I liked the donkey metaphor — it really drives home how bots (or other tools) can act without “understanding,” which loosens the connection between intention and consequence. It makes me wonder: when a bot does something harmful (even unintentionally), should we treat that more like an accident or a responsibility failure — and how much should we hold the creator, operator, or even the platform accountable?

I really liked the metaphor of ethical frameworks as “tools” for seeing different aspects of a situation. It emphasizes that no single moral theory will show everything. One question I had: how do you choose which pair of frameworks to apply in a given scenario, and what criteria should guide that choice?

One additional ethics framework that is worth including is Discourse Ethics. This framework emphasizes the ethics of communication: acting only under norms that all affected can accept in rational discourse. It helps especially in social media settings, because these platforms involve many stakeholders (users, platform designers, advertisers...) engaging via mediated communication. Discourse Ethics adds tools for evaluating whether moderation policies, recommendation algorithms, or community norms are acceptable to all affected rather than simply optimizing outcomes or following abstract rules.