This article explains how social media rewards whatever grabs attention, even if it's shocking or negative. It connects really well with this chapter's idea of selection: the posts that get the most reactions are the ones that survive and spread. I thought it was interesting but also a bit depressing that success online often means being louder, not smarter. It made me realize how easily the attention economy can shape what we see and believe.

I like how this section compares social media to evolution, and it actually makes a lot of sense. A post can reproduce when people share it, mutate when someone adds a caption or emoji, and then survive if it goes viral. It's funny but a little scary to realize how fast ideas can change and spread online. Sometimes the meaning of the original post completely disappears after being shared so many times. It made me think that maybe memes evolve faster than anything in nature!

This article talks about how the Fair Sentencing Act tried to tix unfair drug laws that punished some people much more harshly than others. I didn't know how big the gap used to be. It's shocking that the system allowed that for so long. It reminded me that algorithms can repeat the same kind of bias if they learn form unfair data. Both law and technology show how neutral systems can still treat people unequally.

I never realized how many things go into deciding what shows up on my feed. It's little scary to think that apps might use my friends' searches or my location to guess what I want. Sometimes the recommendations are really accurate, but other times they feel creepy, like the app knows too much about me. It makes me wonder how much of what I see online is actually my choice.

This article expands on the same idea presented in Chapter 10 that disability is not caused by an individual's physical or mental condition, but by barriers built into society. What stood out to me is how the model shifts focus from fixing people to fixing environments. This connects that exclusion often comes from design choices rather than personal limitations. It also mede me think about my own digital and academic spaces; how often are they designed for neurotypical, able-bodied users by default? The social model reminds us that inclusion is not charity; it's justice through design.

I really appreciate how this section reframes disability as a social design issue rather than an individual problem. The examples about stairs and color vision made me realize how often our environments are built for a narrow idea of normal. I've never thought about how something as simple as shelf height or screen brightness can include or exclude people. This reminds me that accessibility isn't just a technical feature; it's an ethical responsibility. If design creates disability, them redesign can also remove it. It makes me wonder how many limitations in our world are actually design failures, not human ones.

This article outlines how direct messaging systems have evolved across social media platforms, but what stood out to me is few of these systems are truly private. Even when messages are labeled as direct or personal, they are still stored on centralized servers that companies can access for moderation, data analysis, or even advertising purposes. This connects directly to 9.1's discussion of privacy illusions. How private online communication is often just a matter of perception. It makes me question whether privacy online is ever absolute, or if it's always conditional on the platform's policies and profit motives.

I find this section very relatable because it captures how fragile our sense of privacy really online. The example of private messaging makes me think about how I often assume my DMs are confidential, even though they are stored and possibly analyzes by the platform itself. What feels private to users is often just conveniently invisible. I think this blurring between private and public spaces is what makes digital privacy so psychologically complex. It's not only about hiding information but about controlling context and audience. The idea that company can read what I write to a friend reminds me that privacy online is less of a right and more of a temporary permission.

This article explains how websites collect user data through cookies, browser fingerprinting, and other invisible scripts to monitor behavior across the internet. What struck me most is how this type of tracking creates an enormous secondary market for personal data. It connects directly to Ch8's discussion of data mining, because the same "mined" information used for analytics can also be used for manipulation, such as targeted advertising or misinformation campaigns. Reading this makes me realize that web tracking isn't just a technical issue, it's a question of digital consent and autonomy. When algorithms know more about us than we do about them, the balance of power becomes deeply uneven.

I found this section both fascinating and unsettling. It shows how data that seems harmless, like who our friends are or what we buy, can be mined to infer extremely private information about us. As someone who often shares content online without much thought, it's alarming to realize how easily patterns can reveal aspects of our identity that we never explicitly disclose. The example of groups reviving racist pseudoscience through AI is especially disturbing. It reminds me that technological innovation can still recycle old forms of discrimination. It makes me question whether data mining is truly about knowledge discovery or if it's pften about reinforcing existing power and bias under a new technical name.

This article describes how a comedian pretended to be the official FBI account on Twitter and posted a joke about Martin Luther King Jr's assassination. While the intention may have been humor, the impersonation clearly crossed ethical boundaries by exploiting both historical trauma and public trust. I think this example connects directly to Chapter 7's discussion of trolling and "bad faith". The comedian's defense that it was "just a joke" mirrors the Schrodinger's asshole concept, using humor as a shield to deny accountability when the audience reacts negatively. It raises an important question: when dose satire stop being social commentary and start becoming harm disguised as entertainment.

I find it fascinating that trolling began as a kind of "inside joke" among early Internet users. What started as playful testing of newcomers' knowledge later evolved into behaviors that intentionally cause harm or humiliation. Reading about the shift from lighthearted pranks to toxic online cultures like 4chan made me realize how easily community norms can slide when cruelty becomes entertainment. It also connects to Sartre's idea of "bad faith" mentioned earlier, the troll's refusal to take words seriously allows them to avoid responsibility. It makes me wonder whether trolling is less about anonymity and more about disengagement from empathy.

This article analyzes how tweets posted from Donald Trump's Android device were significantly more negative and aggressive than those from his campaign's iPhone, which were more neutral and polished. The researchers used sentiment analysis to identify emotional differences between the two sets of tweet. I found this especially relevant to the discussion of inauthenticity in Chapter 6, because it shows that even when a social media account seems to belong to one person, multiple voices or personas may actually be behind it. This raises important ethical questions about authenticity and accountability online.

This story about Dr. McLaughlin and the fake @Sciencing_Bi account really shocked me. It made me realize how easily emotional trust can be built, and then betrayed, on social media. As someone who often reads personal stories online, I've never thought deeply about whether those stories could be fabricated. What feels most unethical here is not only the lying itself, but how it exploited people's empathy for marginalized identities and real victims of harassment. It's frightening that authenticity online now requires skepticism, and that our compassion can be used as a tool for manipulation.

The Wikipedia article on Social networking services traces how early online communities evolved from simple bulletin boards to complex social ecosystems. What stood out to me was how the article highlights the shift from chronological, user-driven spaces to algorithmic, engagement-driven platforms. This connects directly to the discussion in Section 5.6 about "friction" and "affordances"--as platforms became more automated, they started shaping our behavior through invisible design choices. Reading this made me realize that social media's design evolution isn't just technical progress; it's a history of how digital architectures have gradually gained power over attention and emotion.

I think the example of "friction" is fascinating because it shows how small design choices can completely change user behavior. The Twitter pop-up that asks users to read the article before retweeting might seem minor, but it introduces a brief comment of reflection that slows down impulsive sharing. As someone who often scrolls through social media quickly, I realized how little time I spend verifying what I see online. Adding friction can be an ethical design choice--not to frustrate users, but to protect them from misinformation and emotional manipulation. It makes me wonder whether platforms should use more friction, not less, in areas related to mental health or political content.

I found this section particularly thought-provoking because it shows how neutral design decisions can quietly define who belongs in a system. As someone who has filled out many online forms as an international student, I've often experienced exactly what this paragraph describes--forms that assume every user lives in the U.S. or has a "first" and "last" name that fits English conventions. It reminds me that "fitting into the data" isn't just about usability but also about representation and identity. The example of address fields illustrates how technical defaults can privilege one group's reality while making others invisible. It makes me wonder how many times I've unconsciously adapted myself to technology, rather than technology adapting to me.

Hoffmann's concept of "data violence" resonates strongly with this chapter's discussion of how data practices encode social power. She argues that when engineers treat datasets as neutral, they overlook how design decisions--such as labeling conventions or exclusion of marginalized voices--can reproduce harm. I found this perspective particularly striking after reading the earlier part of chapter 4 on data collection and bias: it reframes technical errors not just as mistakes, but as moral and structural failures. It makes me think that ethical data work requires both tecinical literacy and historical awareness of inequality.

The reading lists Tweedie's (2015) article with that photo of a worker controlling tons of iPhones. That example really hit me -- it shows how rankings and popularity online can be totally fake. It connects to the chapter's point that bots or fake accounts don't just add numbers, they can change what appes or voices get noticed in the first place.

Reading about how bots are often used to amplify certain voices on social media made me think about my own experience on Twitter/X. Sometimes I notice trending hashtags that feel "unnatural," almost as if too many accounts are repeating the same message. It makes me wonder whether genuine user interest is actually being represented, or if it's the result of coordinated bot activity. This connects to the ethical concern raised in the chapter about authenticity: if bots distort what looks like public opinion, should platforms be responsible for filtering them out, or should users just learn to be skeptical? Personally, I feel it undermines trust in social media when I can't tell if I'm interacting with a real person or an automated script.

I find it interesting that the definition of "social media" shifts depending on who is using it -- researchers, companies, and everyday users. From my own experience, I sometimes think of Youtube more as entertainment than "social media", which makes me question whether strict definitions are even possible.

While utilitarianism highlights the importance of maximizing happiness, I wonder is this approach can justify sacrificing minority rights on social media platforms. For example, could a company claim it is ethical to collect sensitive data from a small group if majority benefits?