When I looked at the “Free Market” source in the bibliography, what surprised me was how neutral the Wikipedia definition sounded compared to how the term is used in real debates. The page describes the free market almost like a clean, ideal system where supply and demand naturally balance. But reading it after the chapter made me notice the gap between that ideal and the way companies like Meta actually operate. In theory, free markets are supposed to create competition and benefit consumers, but in reality we often see the opposite—big platforms trying to reduce competition and lock users in. It made me wonder whether the “free market” idea still makes sense in industries where network effects basically guarantee that only one or two companies will dominate.

Reading about how Meta increases profit by getting more users, more user time, and more personal data makes me think about how invisible these incentives are in my own daily social-media habits. I notice that whenever I open Instagram “just for one minute,” the platform always manages to keep me scrolling. After learning that this isn’t accidental—but a direct way to maximize ad revenue—I feel more critical of the design decisions that shape my behavior. It makes me wonder: if a company’s fiduciary duty is to maximize profits, are they ever able to prioritize user well-being over engagement? Or is the system designed so that our attention will always be treated as the product rather than something that deserves protection?

One detail that stood out to me is the argument that adults often underestimate the intensity of a child’s emotional world, especially when the child feels betrayed by someone they trust. This connects directly to the chapter’s themes of shame and public humiliation, because the prank essentially puts children in a position where their emotional reaction becomes entertainment for millions of viewers. I found myself feeling uncomfortable realizing how easily humor can cross into exploitation when the audience is distant from the person being laughed at. Seeing this example made me think more critically about how “public shaming” can happen even in small, everyday moments—not just in cancel culture or social media scandals. It makes me question whether our culture has become too comfortable treating others’ vulnerability as content.

I find the Kickstarter source interesting because it reflects how crowdfunding has changed the way people launch creative projects. Instead of relying on traditional investors, creators can pitch directly to the public, which makes the process feel more democratic. But at the same time, looking at this platform also reminds me that many projects never deliver what they promise, and backers take on risks without real protection. This makes me think about how trust works online and how platforms should balance creativity with accountability.

This example made me realize how powerful crowds can be when working on complex problems like protein folding. I like how Fold-It shows that human intuition can sometimes beat computers, and that ordinary players can contribute to real scientific discoveries. At the same time, it makes me wonder whether crowdsourcing platforms give enough credit to the people doing the work.

This Reddit post raises an important ethical question about unpaid labor on large online platforms. It’s surprising that Reddit, a company worth billions, depends so heavily on volunteer moderators who receive no compensation. I think this shows a contradiction between the platform’s profit and the unpaid effort that keeps it running. It reminds me of how Wikipedia also relies on unpaid editors, but in Reddit’s case, the moderators face more pressure and even harassment. Personally, I believe moderators should receive at least some form of payment or recognition for their work. Without them, Reddit would not function — so their contribution deserves more respect and support.

I find it fascinating that Wikipedia is built entirely by volunteers and that anyone can edit it, yet it has become such a reliable and influential source of information. However, I was surprised to read that most Wikipedia editors are male — around 80–90%. This imbalance could definitely lead to bias in which topics get attention or how they are written about. For example, the case of Donna Strickland not having a Wikipedia page before she won the Nobel Prize really shows how gender bias can affect visibility in knowledge-sharing spaces. Personally, I think Wikipedia should do more outreach to encourage more diverse contributors, since it plays such a big role in shaping public understanding. It also makes me wonder — how much of what I read online might reflect hidden biases from who is editing it, rather than the truth itself?

Mia Sato’s article on YouTube’s incorrect copyright claims highlights how automated copyright detection systems can harm content creators. It’s surprising that millions of claims were made wrongly within only six months, showing how flawed and unfair these automated systems can be. This connects to the “Legal Concerns” section of the chapter, which talks about how platforms like YouTube try to avoid legal issues by being overly strict. I think this article shows a real-world example of how algorithms can create ethical problems, punishing innocent creators while trying to stop illegal content. It makes me wonder if the balance between protecting rights and supporting creativity can ever be achieved when automation is involved.

I think the discussion about legal concerns on social media platforms is very realistic and important. I’ve noticed that sites like YouTube or TikTok sometimes remove videos suddenly, and creators feel frustrated because they think it’s unfair or mistaken. This shows the challenge of balancing copyright protection with freedom of expression. I personally think platforms should be more transparent about their content moderation systems—sometimes the rules feel unclear or unevenly enforced. It also makes me wonder whether automated systems, which flag copyrighted or illegal material, are really accurate enough to make these decisions without human review.

I read Anya Kamenetz’s article “Selfies, Filters, and Snapchat Dysmorphia: How Photo-Editing Harms Body Image” from Psychology Today. I think it explains very clearly how social media filters make people focus too much on their looks and compare themselves to unrealistic images. Personally, I find this idea very true because many people, especially young users, start to lose confidence when they see perfect edited photos online. This connects to the chapter’s discussion about how social media affects mental health — showing that technology can shape our self-image in both positive and negative ways.

I think this chapter shows a very positive side of social media — that it can actually help people build a sense of belonging and emotional support. Personally, I agree with this idea because I have also found online communities that make me feel connected and understood. For example, during stressful times in school, I sometimes read or post on social platforms where people share similar struggles, and it helps me feel less alone. This connects to what we discussed earlier in this course about “digital empathy,” where technology can create emotional understanding among strangers.

I found The Selfish Gene really thought-provoking because it completely changes how we think about evolution. Instead of seeing living things as the main focus, Dawkins makes us see genes—and even ideas—as the real “survivors.” Personally, I think this idea is still very relevant today, especially when we think about how memes and online trends spread. It’s amazing how something written in 1976 can explain how the internet works now.

Reading this section about The Selfish Gene really amazed me. I never realized how deeply connected biology and culture could be. The idea that memes evolve in the same way as genes made me think differently about how fast ideas spread on social media today. Personally, I find it both exciting and a little scary—exciting because creativity can spread so quickly, but scary because misinformation can too. It made me realize how powerful our sharing behavior is in shaping modern “evolution.”

This post perfectly illustrates a systemic feedback loop: when ranking models optimize for engagement (clicks, replies, dwell time), any interaction— even hate-watching or “dunking” — becomes a positive signal. The system can then amplify exactly what you say you dislike, because your behavior says “I’m interested.” A concrete example: if I constantly quote-tweet an account to criticize it, the recommender learns that content keeps me on the platform and will surface more of that account and similar ones, deepening an echo chamber of outrage. We all should be caution for this.

I think alogorithms recommendation creates filter bubbles and echo chambers: people see more of the same views, products, or communities, while different or challenging content gets hidden. They will then only see what they want to see. Over time, this can reduce diversity of information, reinforce stereotypes, and polarize groups—even if no one person wanted that outcome.

Using the GDPR Wikipedia page is fine for a quick overview, but it’s shaky for details—pages change and legal wording is picky. If the chapter is arguing about privacy “rules” being unclear, it would be stronger to cite the actual text of GDPR (esp. Article 5 on purpose-limitation/data-minimization and Article 6 on lawful basis) and maybe an EDPB guideline. Quick win: keep the Wikipedia link for context, but add primary sources to back any claims about what GDPR requires.

This part reminded me that the scariest privacy leaks aren’t big hacks—they’re everyday stuff. Work chats feel “private,” but admins can read them. Most people don’t know that. And parents posting kid pics? It feels loving, but the photo can carry location/time info that gives away routines. My quick rules: assume work tools are reviewable, turn off location on your camera, ask before posting other people (especially kids), and apps should make it super clear who can see your post and auto-strip metadata by default.

Great, accessible explainer of “shadow profiles”—how contact uploads, embedded pixels/Like buttons, and partner data let Facebook infer identities beyond users. Its strength is concreteness; its limitation is age: it predates GDPR enforcement UX changes, Facebook’s “Off-Facebook Activity” tool, and Apple ITP/ATT. The pipes largely persist even if consent flows look nicer. For class, pair it with a mini-lab: check “Off-Facebook Activity,” download your data archive, and watch requests to facebook.com/tr in DevTools to see tracking in the wild. Ethically, it spotlights “consent by proxy”: your friends’ uploads can deanonymize you—an argument for collective privacy rights, not just individual settings.

I think these tell us a dangerous shift that from expressed identity to inferred identity. When platforms guess traits like sexuality, politics, or “addiction risk” from friends and clicks, they create shadow labels you never consented to—labels that can trail you into credit, insurance, hiring, or campus discipline. I think platform should give notification for this actually.

Phillips argues that trolling isn’t some fringe glitch—it feeds on the same attention economy that mainstream media uses, which is why hoaxes and outrage travel so well. A helpful detail from her piece is how “it’s just a joke” functions as a shield: ambiguity lets trolls toggle between sincerity and irony to avoid accountability while still harvesting attention. That framework reframes cases like the “Forever Alone Flashmob” as not only individual cruelty but also a media-system problem: amplification (retweets, headlines, livestreams) is the fuel. My takeaway is that platform and newsroom practices—e.g., not linking to troll content, slowing virality for unverifiable claims, and de-incentivizing engagement spikes—are as important as user education for reducing harm.

I notice how the instructions weaponize ordinary tools (dating apps, webcams, public livestreams); nothing here is “high-tech,” which suggests that prevention is more about norms and platform rules than about advanced security. If we treat this as a design problem, I think dating sites could rate-limit new accounts, require stronger photo verification, and flag patterns like many “new users” inviting different men to the same place and time. Culturally, the meme also normalizes group bonding through dehumanizing outsiders; it is worth asking whether some online communities teach people that empathy is weakness. Personally, I feel angry and a bit scared, because the prank depends on making a private hope (a date) into public shame; it punishes people for trying to connect.

This piece unpacks the @sciencing_bi hoax—where a well-known activist fabricated a “Native American professor” who supposedly died of COVID—and shows how the lie unraveled through open-source verification (timestamps, language patterns, overlapping social graphs). What struck me is how a sympathetic identity can still be weaponized to mobilize outrage and donations, which blurs “authentic vs. inauthentic” far beyond simple anonymity. The article also documents the platform response (account suspension after community sleuthing), underscoring a reactive moderation gap: detection lag lets harmful narratives peak before correction. For me, it strengthens the case for reputation signals on pseudonyms and lightweight provenance checks on high-impact claims, so empathy isn’t exploited by manufactured personas.

I feel this too. When people think there’s no consequence, some go extreme—hate raids, doxxing help-threads, or “sock-puppet” pile-ons feel way too easy under full anonymity. But I’ve also seen anonymity protect the right people: a student reporting harassment, a queer kid seeking help, a worker blowing the whistle. So I don’t want a blanket ban; I want guardrails: stable pseudonyms with reputation, stronger friction for brand-new throwaways, and a clear, due-process path to unmask only in severe cases (credible threats, coordinated harm). That balance keeps space for the vulnerable while making it harder to weaponize the mask.

Reading the Wikipedia entry on AIM (AOL Instant Messenger) brought back a lot of nostalgia. AIM was my first experience with real-time online communication, and it shaped how my generation learned to talk online. The concept of having a screen name, an "away message," and even custom sounds for incoming messages was revolutionary at the time. What really stood out from the source was how AIM not only popularized online chatting, but also introduced early forms of status updates and even emoticons—features we now take for granted on modern platforms like WhatsApp, Discord, or Slack. It’s fascinating to see how many of today’s messaging habits were born out of tools like AIM. It makes me appreciate how even "outdated" tech plays a role in shaping current digital culture.

One thing that really stood out to me in this chapter was the discussion about how social networking platforms evolved from early services like Friendster and MySpace to the dominant platforms we know today, like Facebook and YouTube. It made me think about how quickly digital trends shift and how easily one platform can completely overtake another. Personally, I remember having a MySpace account in middle school, and it was a big part of how we socialized online. But within just a few years, everyone moved to Facebook, and MySpace basically disappeared. It makes me wonder: will the same thing happen to today's dominant platforms like Instagram or TikTok? Are they just temporary until the next big shift happens?.

I checked out the W3Schools page on Python Lists ([d7]) and it actually helped me understand lists way better than before. It showed that lists can hold different data types at the same time, like strings, numbers, and even other lists. I didn’t realize you could mix them like that! It also explained how you can use list methods like append() and remove(), which I had seen before in examples but never fully got. The examples were simple but made it easier to connect what the book said about indexing and list operations.

I used to think it was weird that Python starts counting from 0 instead of 1. Like, why not just start with 1 like normal people? But after reading that it’s because of how programming languages were developed, it actually makes a bit more sense now. I also didn’t realize strings are kind of like lists too—that’s pretty cool. The example with the authors and the word “ethics” really helped me see how indexing works in real code.

The reasons that felt most compelling to me were from Care Ethics and Consequentialism. Care Ethics emphasizes responsibility in close relationships, which makes me feel that intervening is an act of love for my parents. Consequentialism reminds me that while intervention may upset them now, it prevents more serious harm later.

I especially feel this way because of my grandfather’s story. He delayed surgery, and we respected his choice. He might delay cause of fear or other conerns but we agree with his choice. Later, when his condition worsened, the chance of survival was much lower, and we regretted not intervening earlier. That experience makes me believe that sometimes respecting wishes can also mean avoiding responsibility since for me i think part of the reason that i agree with my grandfather is i am ear of losing him on surgery. Then, due to my experience, i will must intervening since i believe intervening is better for their wellbeing in long term.

The framework that best supports intervening is Care Ethics, because it emphasizes the responsibility of love and the moral duty to protect those who cannot fully protect themselves. The framework that best supports not intervening is Natural Rights, since it prioritizes respecting an individual’s freedom and decision-making, even when those decisions may carry risks.

I find it interesting that Confucianism and Taoism, as ancient ethics frameworks, often seem like opposites. Confucianism emphasizes order, ritual, and fulfilling social roles to create harmony in society, while Taoism emphasizes naturalness, spontaneity, and living in harmony with the Dao. This contrast made me think of the poet Tao Yuanming. He entered official life five times, reflecting the Confucian ideal of service to society, but he also resigned because of political corruption and turned to a simple rural life. His gentle and carefree poems, such as those on drinking wine and enjoying nature, show strong Taoist influence. In this way, Tao Yuanming embodies how the two traditions could coexist in one person’s life.