I thought it was funny that this quote is usually credited to Mark Twain. But the source says neither of them actually said it. The idea goes back to Jonathan Swift, and the wording changed over time as people repeated it. So a quote about misinformation spreading fast is itself a piece of misinformation that spread and mutated.

This one really resonated with me, because a lot of what I share isn't because I care about it, but it's for how it makes me look to other people. It makes me think a post can spread far without people actually agreeing with it. They just like what sharing it says.

What stands out about this source is that it is the owner of Twitter openly admitting the algorithm rewards hate engagement, and treating it like a funny quirk, instead of a problem. It backs up the chapter's point about pile-ons: the system doesn't separate liking something from being angry at it, it just sees interaction.

This part surprised me the most. Usually, when someone posts a bad take, the instinct is to call it out, and it feels like the responsible thing to do. But if the algorithm reads every angry quote tweet as engagement, then calling it out just helps it reach more people. You see the same thing with ragebait on TikTok and instagram, where some creators post intentionally dumb stuff because they know the hate comments will push it.

I liked this source. Twitter's automatic cropping tool, which it had used since 2018, picked which part of a photo to show in the preview. When Twitter tested it, the tool favored white people over black people. I think that is a good example of design justice; sometimes the fairest fix is giving control back to the user.

The paper towel part of this video stood out to me. The dispenser wasn't broken; it worked exactly how it was designed. Nobody on the team thought to test it on darker skin. I don't think anyone meant to exclude people, but that's the point. It is also just a bad business decision, since the product fails for a huge group of customers. It makes me think tho if companies are now testing sensor products across different skin tones, or if they still only fix it after it goes viral.

This article is about a 2021 leak where data from over 530 million Facebook users, like phone numbers and locations got posted online. What surprised me was that Facebook said it wouldn’t notify the people affected, since the info was public and users couldn’t fix it anyway.

The GPR is right to have your data erased, sound good but i feel like I don't know how it would fit in practice. When you hit delete on a post or account, most of us assume it is gone. But there are usually backups, and other people can screenshot, repost or archive. So I don't think deleting means much. It does make me think about how the law gives people a false sense of control.

The Guardian article covers a Channel 4 news investigation that got the Trump 2016 campaign's voter database, which had data on around 200million Americans. One category was literally labeled, meaning people the campaign hoped would stay home. About 3.5 million Black Americans were put in that group and shown targeted negative ads.

This section hits close to home for me. In high school, I started a small social media marketing agency, and a big part of the work was running targeted ads for clients. From the advertiser’s side, targeting feels pretty harmless. You pick an age range and some interests, and your ad only reaches people who might actually care, like the spider stuffed animal example. What stands out to me now is that the platform decides what counts as an interest, and a lot of that is inferred from behavior users never meant to share. The same tools I used to help a local business find customers could just as easily find someone who keeps watching gambling content at 2 am.

I looked into the Spaghetti-tree hoax source, and it’s a great early example of large-scale trolling before the internet. Because it came from a trusted news program, and spaghetti wasn’t a common food in the UK at the time, lots of viewers believed it, and hundreds called the BBC asking how to grow their own spaghetti tree.

I would say this is the biggest reason why people troll and I think is not good. Getting amusement from trolling other people is so bad. I try to never troll online. I feel like the biggest trolls come from instagram.

This source is a data analysis that looked at trump's tweets to see if he wrote the Android ones and his staff wrote the iPhone ones. The Android tweets were angrier and more negative. The iPhone tweets had more links and campaign posts. I thought this connected well to the chapter's point about personas. People assume everything on an account comes from the person whose name is on it, but a lot of the time it's a team. It makes me wonder if people actually care who writes it.

Moving between Dubai and the US, I learned to code-switch before I even knew what it was. The way I talked at school in Dubai, the way I talked with my family, and the way I talked with my friends were all different, but none of them felt fake. That's why I agree with the chapter: code-switching can be an authentic way of expressing yourself. Social media is where this gets complicated for me. Right now I am recruiting for consulting internships, so my LinkedIn is polished and professional, while my Instagram is way more casual and mostly friends. Both are me, but I know a recruiter could easily find my Instagram, and someone from home could see my LinkedIn posts and think I sound like a completely different person. To answer the reflection question, I think most of my changes are authentic, but the context collapse part is where my authenticity gets compromised a little. When I am posting with every possible audience in mind, I am not really expressing myself to anyone, just avoiding saying the wrong thing. My question is whether platforms should be designed to separate audiences better.

Growing up in Dubai, I dealt with this a lot. The UAE doesn’t really use ZIP codes. My family used to put in a random number, and I think that’s a good example of people having to fake their info to fit the system. The form worked, but the data wasn’t actually true.

I think the most interesting part of Agrawal’s thread is how he explains where the “less than 5%” number came from. Every quarter, Twitter took a random sample of thousands of accounts and had people check them by hand. He also said nobody outside the company could recreate the number because the reviews used private info like IP addresses and phone numbers that only Twitter had access to. I think that connects to this chapter really well.

Bay argues that much of the online fight over The Last Jedi wasn’t really fans disagreeing about a movie, but organized political influence efforts meant to make American society look more divided than it is. That shows how bots and trolls don’t need to be the majority.

I get why the book draws this line, but I’m not sure it matters much from the user’s side. . If our ethics questions are about deception and responsibility, the click farm seems closer to a bot than to a normal user. I’d argue the definition should focus on whether the account acts authentically, not on whether there’s code involved.

I don’t think this is true for Kant. He argued that the only thing good without qualification is a “good will,” and that an action only has moral worth if it’s done out of duty, not just because it happens to match the rule.

I think part of why these people had no answers is incentives. Tech workers get rewarded for shipping fast. I think the person building it is the one best positioned to see the risks, so they should carry at least some of that responsibility.