Vox article on how William Carlos Williams's poem 'This Is Just To Say' became a Twitter meme in 2017. Social media can bring older literature back to life, and others rewritten and remixed the poem into humorous posts. Even the shift from 140 to 280 characters on Twitter left more room to play with these jokes. The essay discusses how internet culture allows users to make creative references to existing works and introduce them to new audiences.

I like how chain letters played upon people's fears or rewarded them for passing it along. It reminds me of social network posts asking people to repost something for good luck or to avoid bad luck. Maybe the technology varies, but such tricks still work on people today.

According to the article, cable news is more likely to divide people on political issues than social media or online news. An interesting thing I learned was that about 17% of Americans were politically polarized because they watched TV news, which was three to four times as many as those who read news online. The researchers also found that cable news viewers tend to stick with sources that match their political opinions. This implies that social media is not always the primary source of political echo chambers, and that traditional media may have an even greater influence.

I think it's interesting how much of TikTok's success might be down to it's design and not so much it's actual recommendation algorithm. The ability to fast-forward through videos makes it easier for users to find content they enjoy with little effort. Which leads me to wonder if other platforms could improve their recommendations by simplifying the process for users to dismiss content they're not interested in.

The article discusses the ethical concerns of researchers collecting public social media data without users' knowledge or consent. One specific detail I found interesting was that nearly two-thirds of surveyed Twitter users didn't realize researchers could use their tweets. This shows how people might consider their posts public without understanding who could actually access them. I think researchers should consider privacy and potential harm rather than assuming that publicly available information is automatically ethical to use.

I find it interesting how companies can collect information about someone without them directly providing it. Shadow profiles seem especially concerning because people might not even know that companies are building these profiles about them. It raises the question of whether companies should be allowed to make assumptions about users based on their online activity, especially when those assumptions could be inaccurate or reveal private information.

According to the story, seven housing companies were charged with discriminating against older people by restricting who may view housing prospects through Facebook adverts. The companies allegedly targeted younger audiences, which made it more difficult for older people to find out about available apartments. This demonstrates how discrimination and unequal access to opportunities can be exacerbated by online advertising algorithms.

This example, in my opinion, demonstrates the need for caution when evaluating statistics. Two events do not necessarily cause one another just because they occur at roughly the same time. Spurious correlations are intriguing to me since they can easily trick others into thinking something is false. These days, this is particularly crucial as data are frequently used online to bolster claims. To ascertain whether a genuine relationship exists, we should examine further information and take into account other explanations before accepting a conclusion.

I never considered that automation could be used for protest, or more morally inclined good reasons. Sean Black created code that made automatically made accounts, which filled out personal information and uploaded fake resumes to automatically apply for Kellog. Is it ethically justified if you use automation for a “morally good reason”.

It’s interesting how trolling was originally meant to distinguish itself from ordinary, and “professional” people. It shows how much online communities still create hierarchical structures within.

Qanon is a conspiracy theory emerging in far right associated spheres. It holds many beliefs such as there are many powerful politicians and celebrities who secretly run the world (how original). They believe that Donald Trump, is fighting a secret war against them and there is an insider named Q who has revealed "classified" information about it. It mentions ties to the pizza gate conspiracy theory, and has ties to 4chan. Multiple people said that Qs posts were written by several others, after analyzing style of writing, etc. It mentions many other events that followers took part in, such as Jan 6th. Q made multiple predictions like Trump would win the 2020 election, which didn't happen. One researches stated that it appealed to people because they wanted to solve the mystery, however they needed one to exist. Also for a sense of belonging or being a part of something.

A surprising method that enabled far more content creation among users. Recently however, I had a famous film critic come into one of my classes and talk about how he dislikes the democratization of critiques, especially pertaining to social media. Now with the ability to mass produce bots and modify algorithms, there is an argument to be made that not every person should have an opinion or say, especially when their viewpoint can be amplified 10 fold on factors not concerning actual quality or critical thought placed into the viewpoint.

According to CHEQ, 75.85% of traffic from X to client advertising sites was fake. CHEQ is a firm that specializes in cybersecurity and tracking bots according to the article. The article mentions that recently many users on X are worried about the increase in inauthentic activity. Other studies showed that compared to apps like Instagram, or Tiktok, X fared in general far worse according to CHEQ. It also mentions that this problem wasn't as prevalent when Musk wasn't running it, mentioning how he laid of 80% of the companies trust and safety engineers, and how advertisers paying for advertisements on the platform could be advertising to bots.

Happiness is a vary vague concept, with many hedonists arguing for it as a primary reason to partake in illicit substances. I do like the idea of evaluating from the concept of utility, but even then it depends heavily on what type of framework you use. If u take utility from others, but their value system differs, I can very easily see points of conflict emerge. I feel like making a rigid system which can easily differ from a lot of other people's value systems would be more unproductive

Star Wars the last Jedi movie had a lot of hate received towards it was actually political in nature as Morten Bay, the primary researcher of the academic study would argue. He believes that the movie promoted messaging both the alt right and Russian federation would disagree with, and proves that there were Russian troll accounts pushing the hate on the movie. I find this to be pretty concerning that people would go so far for political gain as to bot or push negative reviews on a movie that loosely alludes to modern day politics.

I wonder how much verification processes actually help towards identifying fake accounts? I find it disconcerting how a lot of peoples opinions on the internet could be skewed via algorithms that could show mass produced bot content.

I personally wouldn't intervene at this stage. If something big happens, maybe I would consider it but in general I respect their wishes. If something big did happen and their safety is proven to be jeopardized, I would put them in a home.

Another framework that could be added is epistemological nihilism, which doubts that we could ever have knowledge that is completely certain. Our beliefs are based on things like perception, memory, reasoning, but all of these can be wrong. The problem with taking this too far, however, is that “nothing can be known” sounds like a knowledge claim in itself. I think a more useful version is that we can know things, but we should always be open to the possibility that we are wrong, or could be wrong.