I can see the value in learning from different cultures, but I still wonder how far we can go with “respecting differences” before we risk excusing some probably harmful practices as just "cultural variation".

I believe care ethics will be especially useful for thinking about social media, because it highlights how automated systems can either strengthen or undermine the relationships we depend on.

The history of the Luddite movement provides an interesting perspective on the ethical dilemmas of technological progress. While Luddites are often mischaracterized as simply anti-technology, their protests were actually about how industrial automation was displacing skilled workers and worsening labor conditions. This connects to modern debates about AI and automation—are we facing a new wave of "digital Luddites" as workers fear job loss due to AI-driven systems?

This passage highlights how even well-intentioned technological advancements can have unintended and often harmful consequences. It raises an important ethical question: should inventors be held responsible for the negative impacts of their creations, or is it impossible to predict how technology will evolve? This dilemma is especially relevant in modern tech, where innovations like AI and social media algorithms are shaping society in ways their creators may not have fully anticipated.

The Merriam-Webster definition of capitalism provides a concise explanation, but it doesn’t capture the complexities of how capitalism operates in digital economies. For example, platform capitalism, as discussed by Nick Srnicek in Platform Capitalism, highlights how tech giants like Meta extract value not just from financial transactions but from user data. Should definitions of capitalism evolve to explicitly account for digital labor and data exploitationThe Merriam-Webster definition of capitalism provides a concise explanation, but it doesn’t capture the complexities of how capitalism operates in digital economies. For example, platform capitalism, as discussed by Nick Srnicek in Platform Capitalism, highlights how tech giants like Meta extract value not just from financial transactions but from user data. Should definitions of capitalism evolve to explicitly account for digital labor and data exploitation

Meta’s strategy of rolling out changes gradually to avoid mass user backlash is a fascinating example of how companies manage user dissatisfaction in a capitalist system. It reminds me of how streaming services adjust pricing—rather than increasing prices for all users at once, they introduce higher tiers gradually. This raises an important question: Should companies be required to include users in decision-making processes beyond passive feedback mechanisms? What would a model of 'digital democracy' within a platform like Meta look like?

This resource offers a compassionate, practical framework for understanding how trauma can lead to feelings of shame—and provides useful strategies for healing.

Public shaming often relies on schadenfreude—a perverse pleasure in witnessing someone’s downfall. The Onion’s 2009 satire brilliantly mocks this tendency, making us question if our outrage is about genuine accountability or just entertainment.

The NPR article by Anya Kamenetz highlights an important issue—how social media companies like Facebook (now Meta) conduct internal research on mental health but don't always share conclusive findings. It’s concerning that while some studies suggest Instagram negatively impacts teen mental health, especially for young girls, the data isn’t always transparent or definitive. This makes me wonder: should social media companies be required to release all their internal research on mental health impacts? If these platforms acknowledge their influence on mental well-being, shouldn't they also be more accountable for addressing the harm?

"One point that stood out to me was how social media can be a powerful tool for finding supportive communities, as mentioned with Professor Casey Fiesler’s experience. While social media is often criticized for its negative effects on mental health, it’s important to acknowledge that it can also provide a refuge for those who feel isolated in their offline lives. For example, LGBTQ+ youth who may not have a supportive environment at home or school can find online spaces where they feel accepted and understood. However, this also raises the question of how platforms can foster these positive spaces while mitigating the risks of toxic interactions. Should platforms do more to promote healthy online communities, or is that primarily the responsibility of individual users

it’s interesting to see the citation of ‘Evolution of Cetaceans’ in a discussion about virality. While it may seem unrelated at first, it actually makes sense—just as species evolve over time through natural selection, viral content evolves through user-driven modifications and social media algorithms. The concept of replication with inheritance (12.3.1) mirrors biological evolution, where small variations in content (like meme edits or remixes) determine which versions spread the furthest. I wonder if this evolutionary perspective could help us predict what kinds of content are more likely to go viral

The concept of replication with inheritance really resonates with how memes evolve over time. A single image format or video can take on countless variations as people remix it with their own cultural references or humor. For example, the ‘Distracted Boyfriend’ meme started as a stock photo but has been continuously modified to represent different jokes and ideas, making it a prime example of inheritance in social media virality.

The Social Model of Disability article aligns with the chapter’s argument that disability is socially constructed. It highlights how barriers come from societal design rather than individual impairments. The chapter’s color vision example reflects this—trichromacy is "normal" only because society is built around it.

This model also applies to digital accessibility. For example, social media platforms without alt text or captions create barriers for visually impaired users, not because of their impairment but due to poor design. The social model stresses the need for inclusive technology rather than expecting individuals to adapt to an exclusionary system.

This section of the chapter raises an interesting point about how disability is socially constructed rather than purely biological. The example of color vision is particularly compelling because it challenges the typical binary of "able-bodied" versus "disabled." It makes me wonder how many other "disabilities" exist simply because of societal expectations rather than inherent limitations. For example, if our society were structured around sign language rather than spoken language, hearing impairment might not be considered a disability at all. This also connects to the broader conversation about neurodiversity—many cognitive differences, like ADHD or autism, are often labeled as disabilities mainly because society is designed around neurotypical standards. What if we built environments that accommodated a wider range of abilities? Would fewer people be considered disabled?

Wagner’s article highlights the controversial practice of "shadow profiles," where Facebook collects data on individuals who don’t even have an account. This raises serious privacy concerns, as it suggests that opting out of a platform doesn’t necessarily mean escaping its data collection. Even those who consciously avoid social media can still be tracked through their interactions with websites, friends, or devices linked to Facebook’s network. This brings up an important question: Should companies be required to allow individuals to fully opt out of data collection, even if they don’t use their services?

Here are two substantive comments based on the provided excerpt from the chapter:

Comment 1: Ethical Concerns in Data Inference The ability of social media data to infer personal attributes—such as political leanings or susceptibility to addiction—raises serious ethical concerns. While this information can be useful for targeted advertising or public health insights, it can also be exploited in harmful ways. For example, political campaigns have used psychographic profiling to manipulate voters, and financial scammers could exploit people identified as vulnerable. How do we ensure that these inferences are used ethically, rather than to manipulate or exploit users?

I noticed the reference to Tom Standage’s book, "Writing on the Wall: Social Media - The First 2,000 Years" ([e1]). Standage provides a comprehensive historical perspective on social media, tracing its roots back centuries before the digital age. His exploration of how humans have always sought ways to communicate and share information highlights that social media is not just a modern phenomenon but an extension of our innate desire for connection. This context enriches our understanding of Web 1.0 platforms by framing them as part of a long continuum of social communication tools. It also raises interesting questions about how the fundamental principles of social interaction have remained consistent despite technological advancements.

Reading about the early forms of social media in Web 1.0, such as Bulletin Board Systems (BBS) and AOL Instant Messenger (AIM), took me back to my high school days. I vividly remember spending countless hours on AIM, organizing my contacts into groups like "Buddies" and "Family," and eagerly waiting to see who was online. Unlike today's platforms, where interactions are instantaneous and multimedia-rich, Web 1.0 offered a more text-based and slower-paced form of communication. This makes me appreciate how far social media has evolved, providing more dynamic and engaging ways to connect with others. It also makes me wonder about the simplicity of those early interactions and whether some of that simplicity is lost in today's fast-paced digital communication.

The concept of zero-based numbering, as described in the Wikipedia article, underscores how fundamental design decisions in programming can have far-reaching consequences. It’s interesting how such conventions affect data organization and user experience, connecting to the chapter's discussion on how biases and conventions can shape technology

"I agree that transparency in data collection is crucial, but I wonder how realistic it is to expect companies to prioritize ethics over profit without stricter regulations. What do you think would incentivize them?"

The link to affordances provides a nuanced explanation of how user interfaces encourage specific behaviors. I found it insightful because it shows how even subtle design choices—like a button’s size or color—can shape user actions. For instance, social media platforms often use visual cues to make "liking" a post feel intuitive and rewarding, encouraging engagement. This source could be expanded further by discussing the ethical implications of such affordances—does encouraging engagement align with user well-being, or does it prioritize platform profit at the cost of user experience?

The variety of bot types highlighted in this section illustrates how automation can be both a force for good and a tool for harm. I was particularly struck by the example of Rian Johnson receiving coordinated tweets from Russian accounts while directing Star Wars: The Last Jedi. It raises questions about the ethical responsibilities of social media platforms to detect and mitigate such interference. While the study mentioned shows that a large portion of negative tweets were from trolls or bots, it also emphasizes the difficulty in distinguishing between genuine criticism and manufactured dissent. This makes me wonder how we can better identify and counteract antagonistic bots without suppressing legitimate expression.