I like that this design analysis explicitly treats “accessibility to more people” as not automatically morally good if the underlying feature or platform dynamics can cause harm (e.g., unnecessary anxiety). That framing pushes us to evaluate both who benefits and who pays the costs, rather than assuming growth or engagement is neutral. It also made me think good mental-health-oriented design should be measured by outcomes like reduced harm and increased user agency—not just “time on site,” and that those metrics might differ across groups with different vulnerabilities.

The idea of platforms trying to “detect” mental health states raises a tough ethical trade-off: even well-intentioned interventions can easily become surveillance if users didn’t meaningfully consent. The section’s examples make it clear that detection systems can be repurposed by employers or other actors in harmful ways, and they also risk false positives/negatives that may escalate anxiety or stigma rather than help. Because mental health signals are so context-dependent, I think platforms should be extremely cautious about automation here—prioritizing user control, transparency, and strict limits on how any detected “risk” can be used or shared.

The “virality vs. intention” discussion really helped me see that going viral isn’t a single outcome—content can spread in ways that align with the creator’s intent, or in ways that are antagonistic or humiliating. Ethically, that shifts some responsibility onto sharers and platforms: sharing is not “neutral,” and platform features (like duets/quote-tweets) can amplify misinterpretations or pile-ons that the original creator never consented to.

What I took from this chapter is that recommendation algorithms create structural outcomes that can’t be reduced to “just make better personal choices.” When platform owners frame algorithmic effects as purely user behavior (e.g., blaming people for interacting with content), it feels like responsibility is being shifted away from the system design—even though the system is shaping what interactions are even possible or likely.

The comparison between Universal Design and ability-based design helped me see two different ways of shifting the burden: either build multiple options into the environment, or make the system adapt to each user. I’m curious about the trade-off when the system “detects and adapts”—it could improve inclusion, but it might also introduce privacy risks or misclassification, so it seems important that users can control and override those adaptations.

I like the framing that disability is not just an individual trait, but something created by a mismatch between a person and the assumptions built into an environment (e.g., stairs-only buildings or picture books assuming sight). This makes accessibility feel like an ethical design obligation: when platforms assume one “default user,” they silently decide who gets full participation and who has to work around barriers.

This section makes the privacy vs. security distinction feel very concrete: users may accept some privacy trade-offs, but they still expect platforms to protect the data they collect. The examples about password storage and breaches underline that security failures aren’t just “technical accidents”—they’re governance choices (process, incentives, access controls), and it’s why protections like unique password hashing and 2-factor authentication matter in practice.

I like how this section lists different reasons people want privacy, especially “context collapse,” because it shows privacy is often about controlling the audience rather than hiding wrongdoing. The point about “private” messages still being stored and searchable by the company is also a useful reminder that many privacy features are more like interface promises than true confidentiality—platform design should make those boundaries clearer and give users stronger control over retention and access.

The discussion of spurious correlations is a great caution that data mining can “find” patterns that look convincing but are actually coincidences or driven by hidden variables. It makes me think ethical data work should require more than technical correctness—researchers (and platforms) should be explicit about uncertainty, avoid overclaiming causality, and consider how misleading correlations can shape public narratives or policy decisions.

This section made me realize how “social media data” includes far more than what users explicitly post—things like what we click, what we pause on, location, and even direct messages can all become part of the data pipeline. Ethically, that feels like a consent and transparency problem: even if the platform’s terms mention data collection, users rarely understand the scope or downstream uses, and the fact that platforms may also collect information about non-users makes the boundary of consent even blurrier.

The contrast between “don’t feed the trolls” and the critique that ignoring doesn’t stop persistent harassment really highlights a burden-shifting problem: it asks victims to do the work of preventing abuse. This makes me think the more scalable solution is platform governance and design: skilled moderation, clear rules, and meaningful enforcement, instead of relying mainly on individual self-defense strategies.

I appreciate how the chapter defines trolling as inauthentic posting aimed at disruption or provoking an emotional reaction, and then breaks down motivations like “lulz,” gatekeeping, power, and even “making a point.” What feels tricky is that “disruption” can sometimes be framed as satire or protest, so the ethical evaluation probably has to consider who bears the cost (targets vs. bystanders) and whether the disruption increases accountability or mainly produces harm.

I like the point that code-switching and “putting on a persona” can still be authentic because different communities have different norms for what sincere expression looks like. Context collapse on social media seems like a platform-caused pressure toward a single “flattened” self; it would be interesting to discuss which design features (audience controls, friction for resharing, clearer context cues) could reduce that pressure without isolating people into echo chambers.

What stood out to me is how “authenticity” here is not only about factual truth, but about whether the kind of relationship being offered matches what the audience thinks they’re getting (e.g., lonelygirl15 and the idea of being “duped”). It makes me wonder if platforms should treat authenticity as a design problem of signaling—for example, clearer disclosures for staged/fictional/AI-mediated content—so users can calibrate trust without banning playfulness or performance.

The affordances vs. friction framing is really helpful for noticing “hidden” design choices. Infinite scroll is a great example: it removes natural stopping points, so it can quietly push people to keep consuming content longer than they intended. I like the idea of adding intentional friction for high-impact actions (like sharing), because it can reduce impulsive spread without banning the action.

I appreciate the note that this history is very US-focused. A more global timeline could also highlight early/parallel social media ecosystems outside the US (for example, major messaging + social platforms that shaped “super-app” behavior and different norms around identity). It would be interesting to compare how business models and regulation in different regions changed what features became “standard.”