In Maria Popova’s article, “In a new world of informational abundance, content curation is a new kind of authorship”, she brought to light an interesting point about twitter, “The point is that new tools in general, and Twitter in particular, greatly challenge the binary dichotomy of attention as something that is either given or taken away, distracted. Instead, these tools allow us to direct attention to destinations where it can be sustained with more concentration and immersion”. I found this quote particularly interesting because it challenges the negative reputation social media has for distracting people and making them obsessed with their phones. Popova rather explains that social media, specifically twitter in this example, are used to feed people information and further connect them to more information. I recently wrote an argumentative essay that distraction doesn’t exist because it is difficult to determine what is a distraction and what is a valuable use of time, and anything can be categorized as a distraction. This quote by Popova furthers my claim because she argues that social media isn’t a distraction but rather a different facet which one concentrates upon. What makes social media great is the diverse ways in which it can be used as Popova implies, social media links people to new sources and feeds curiosity. There are accounts made for educational, promotional, personal, and commercial purposes which creates many possibility for what can be gained from concentrating on social media. Maria Popova states in this quote that attention is neither given or taken away meaning that any use of time is of value so no-matter how your time is used you gain something from the experience. Popova’s view is truly insightful because it opens up the possibilities and understandings that can be gained from social media.
DTC Homework #8
After reading “Your ‘Anonymous’ Browsing Data Isn’t Really Anonymous” by Daniel Oberhaus, I became aware of all the data advertising companies have access to. I don’t often post on social media, but I do frequently visit social media to remain updated on who I’m following. I’ve noticed ads pertaining specifically to me more recently on Instagram, I follow a lot of rappers and local rappers and recently I’ve gotten many rap related ads. On Facebook I often see ads related to things I’ve looked for on Amazon. Though Facebook and Amazon are two separate sites I’ve noticed they share user data since all my ads are directly specified to what I’m interested in on Amazon. An interesting flaw in this data is since I share an Amazon account with my family, the advertisements I receive on Facebook don’t always pertain to me, but instead are directed towards my brother but Facebook doesn’t know we share the account. Snapchat is different than Facebook or Instagram since I only use it to follow personal friends, not celebrities, and on Snapchat you don’t create a personal profile that others can see to learn about you. Interestingly I still receive personalized advertisements on Snapchat. Snapchat does track location, and I chose to keep my location on so I could use geographically related filters. Snapchat has used my location to direct me to ads for businesses in the Pullman and Moscow area. Even Facebook has done this same thing even though I didn’t update my profile to say I am living in Pullman. Advertisers have access to an abundance of personal data, in some ways I can change settings to avoid this but often this takes away options within apps that are useful.
DTC Homework 6: Data Visualizations
http://www.visualcomplexity.com/vc/project_details.cfm?id=980&index=980&domain=
This diagram displays the areas most walked in downtown Boston, it uses lines to demonstrate a path of travel, the darker and denser the line, the more popular that street is walked. The map is much darker in the center and diffuses out, streets near the water are also more populated. We can learn from this that the city center and water ways must have something attracting people likely food, jobs, and activities that cause more people to want to be there. We can also tell from this that the city was likely designed to have these things closer together so people could easily go from place to place. The areas by water are likely attracting people because it’s more scenic for walking and running, for those living in the city it’s better to walk or run away from the center of the city where there is a lot of traffic. This is useful for people in Boston to see where most activity is so they can find places, or avoid highly trafficked areas. This is interesting because it differs from a normal map which only shows the streets, instead it actually shows the areas on the streets that are populated. The data here is interesting because this also demonstrates a pattern, if we were to look at other city maps showing places of travel we would likely find similar results, a dense city center and coast.
https://www.reddit.com/r/dataisbeautiful/comments/9ddzey/oc_amount_of_music_listened_to_vs_time_of_day/?utm_content=title&utm_medium=hot&utm_source=reddit&utm_name=dataisbeautiful
The bar graph demonstrates the number of songs listened to per given time of day. There are large peaks at 3pm and 9pm, we can use these trends to see why people are listening to more music at these points in the day. We can assume the 3pm peak is from students getting out of school and listening to music and the 9pm peak is from people listening before bed or partying. There is also a smaller peak at noon, we can assume this is because most people are at their lunch break around this time. The graph is in no way symmetrical, the left side is empty from 3am-8am while the rest of the day is packed with music, this is likely because most people are asleep from 3-8am and aren’t listening to music. This data can be used by companies to know when they should put in ads to make sure they reach a larger audience. The trends in this data is interesting to understand not only when people like to listen to music, but also to understand society. People often listen to music independently today on their phones with headphones, this data shows us when people are more independent during the day. It also demonstrates when people usually wake up and go to bed. Data can represent more than just what the variables show nbecause data trends demonstrate other trends in society.