This article argues that big platforms follow a predictable “enshittification” cycle: they start out serving users, then shift benefits to business customers, and finally squeeze both to maximize shareholder returns. Using examples like Amazon, Facebook, and TikTok, he shows how opaque algorithms, pay‑to‑play feeds, and lock‑in are structural outcomes of platform capitalism, not random mistakes.

This quote makes clear that, under capitalism, companies ultimately answer to investors, not to users, workers, or the broader public. For social media, that means platform policies are judged mainly by how they affect shareholder value, which helps explain why user safety and well‑being often lose out to growth and profit goals.

The article argues that Jimmy Kimmel’s Halloween candy prank is not harmless fun for young kids, but confusing and emotionally upsetting because they take the lie literally and feel real betrayal. The psychologist notes that children under about 10 don’t share adults’ sense of humor, so they can’t easily see it as a joke. He also says it’s usually not “trauma” in the clinical sense, since the parent quickly reveals the truth, but it still shows poor judgment to turn a child’s distress into entertainment.

This guideline nudges us to target systems and powerful actors instead of random individuals. Shaming groups (like companies or agencies) can push institutional change without necessarily destroying a single person’s life. Yet Jacquet doesn’t completely rule out individuals, especially when they hold significant power or influence. That tension between structural critique and personal accountability runs through many real world debates about “calling out.”

This article sits right at the intersection of crowdsourcing, harassment, and public shaming. It shows how a Twitter account mobilized a distributed crowd to identify Charlottesville marchers, with very real offline consequences like people losing jobs. I found it useful as an example of how “doing good” (calling out overt white supremacy) can still raise ethical questions about misidentification, due process, and what we want punishment to look like.

This distinction between private and public harassment is important because the harm shifts when there’s an audience. Public attacks don’t just hurt the target; they also shape how other people see them, and they implicitly recruit bystanders as witnesses or even participants. It reminds me of times I’ve seen posts on social media where the real damage wasn’t just the words, but the fact that hundreds/thousands of others were liking and sharing them.

This article is a good example of ad‑hoc crowdsourcing used in a positive way rather than as harassment or doxxing. A single tweet (“HELP ME FIND THIS COUPLE!”) effectively defines the “problem,” and then thousands of Twitter users collectively solve it by boosting and sharing until the couple surfaces. It shows how social media can turn what would otherwise be a private moment into a kind of public, collaborative project, but in this case the couple consented and appreciated the attention.

This draws a connection between crowdsourcing and organized deception online. I appreciate that it distinguishes between “orchestrated,” “cultivated,” and “emergent and self-sustaining” disinformation, because that shows falsehoods do not always come from a single top‑down source. Platforms can unintentionally provide ideal conditions for users to collaboratively create and amplify misleading narratives. This really complicates the idea of the “wisdom of crowds,” since the same collective processes that can solve problems can also generate and legitimize inaccurate or harmful information.

This article basically argues that Facebook can’t really solve its misinformation and hate speech problem without changing its engagement‑first business model. Even when they created a “Responsible AI” team, leadership steered it away from tackling the core recommendation algorithms, so a lot of the work feels like PR rather than actually changing what users see.

I’ve seen people argue that moderating or banning hate just “drives it underground” and doesn’t actually solve anything, so this result surprised me. It lines up more with my experience that when a site actually enforces rules, it does somewhat work. It makes me more skeptical of platforms that claim they “can’t” moderate because it won’t work; this suggests it can work if they’re willing to take the hit.

This article breaks down what the new 988 number actually does and why it matters beyond “just another hotline.” It shows how 988 is supposed to fix a big problem with mental health crises going through 911, where people often end up in chaotic ERs or in dangerous encounters with police instead of getting real support. What I found most useful is how it connects the simple idea of a 3 digit number to the huge amount of work behind the scenes: building up local and backup call centers, funding more staff, and creating mobile crisis teams so someone can actually show up and help in person.

I like this because it connects social anxiety to the mechanics of feeds and likes. You’re not just doing something, but you’re also pre-imagining how it will look online and how people will react. That constant “watching yourself being watched” seems like it could intensify anxiety and make it harder to just be present.

I found this example of hand soap dispensers not recognizing darker skin tones particularly striking. It’s shocking to realize how deeply power imbalances can manifest in everyday design, often in ways that are invisible to those who aren't affected. This critique highlights the ethical responsibility of designers to consider who might be excluded by their choices. The idea that some designs should not exist if they inherently exclude people is bold but necessary, as it challenges the assumption that technological progress is always inherently good. It also reframes the designer's role as a facilitator, which feels like a much more equitable and collaborative approach to creating solutions.