Social media is an ever growing platform for people to share their thoughts and opinions, but it is also becoming the best way for some research to be done. Because posters don’t think about researchers looking at their post in the form of meta data, many different biases are avoided. This gives a newfound accuracy to the data we collect through this medium. That doesn’t mean it is a perfect source for information though. Because the research is being done online, there is no way to tell who it is that you are getting your information from. There can also be a bias, because not everyone has access to the internet, or posts their thoughts on social media.

In “Blog Post 8: What you can, can’t and shouldn’t do with Social Media Data” by CN, they also reference the positive and negative outcomes of using this method of data collection. It seems like this should just be another tool researchers use, and not replace all forms of data collection.

Writers Stephen Ramsey and Geoffrey Rockwell collaborate in an article for DH Debates discussing the criticisms towards digital work and it’s scholarly credit. It can be difficult to clearly define what scholarly work really is in the humanities, especially when it varies depending on the context of the work. For example, digital work in the humanities is performed across professional environments as well as academic. Comparing any type of publishing, the online platform has proven to result in a greater amount of resistance and criticism. The reasons for this are still being explored, but they include the extreme ease the average person with an internet capable device has when searching for an article to read about any matter. I think it is interesting how professions that entail undoubtable scholarly work such as text editors, literary critics, librarians, historians, and archeologists often use complicated computing software that has been developed and worked on first professionally in the digital humanities yet the tools they use are looked down upon as unscholarly. As Ramsey and Rockwell mention, however, I agree that questions such as this one are going to be given more attention and focus as time goes on, especially as new media and digital work continues to develop and take prominence across every field of professional scholarly work.

What you CAN Do:

1. Collected Data MUCH faster and more accurately than before
2. Use Word Mapper App, very good for linguistic data collection
3. Gets rid of…
   • Bradley effect: People tend to tell researchers what they think they want to hear
   • Response bias: The sample of people willing to do an experiment/survey differ in a meaningful way from the population as a whole
   • Observer’s paradox/Hawthorne effect: People change their behavior when they know they’re being observed

What you CANT Do:

1. Can’t be sure what the source of your data is: Have no clue what general demographic categories your sample represents
2. Inherent Sampling Bias: Social media users tend to be from wealthy, educated, industrialized, rich and democratic societies.
3. Unable to violate developer’s agreements: Developer’s agreements vary between platforms, but most limit the amount of data you can fetch and store, and how and if you can share it with other researchers. (ex. 50000 Tweets for Twitter)

What you SHOULD Do:

1. Respect the wishes of users. There are three principles of ethical human subjects research:
   1. Respect for Persons: People should be anonymous and be guaranteed protection
   2. Beneficence:  Maximize possible benefits, minimize possible harms (NEVER harm)
   3. Justice: Both the risks and benefits of research should be distributed equally.
2. Safeguard their best interests: Be careful of the data you take, make sure that it won’t hurt ANY of the subjects personally.

My favorite piece of data visualization that we were given this semester was the video made by Hans Rosling “200 Countries, 200 Years, 4 minutes.” This is an awesome video that really showed what data visualization can be. It has so much information but was able to display it in an easy to understand and see visualization. It is also fascinating that information that was involved in the video. When he explained what was going on in a country that accounts for their big burst of income or life expectancy. I couldn’t agree more with my colleague JH as they express many of the same idea that I do. They go more into depth about the specifics of the video but overall say it was intriguing and effective.

Although it is not a reading, I found the video about counter mapping very interesting and engaging. I had seen this video before in a class that I took in high school, but I saw it in a totally different light after taking this course. This video displays how visualization can mean more than one might first think. To outsiders these pieces of art just look like pictures of the landscape thrown together but to the Zuni it was maps of the places that they had been millions of times. But the question is presented if this can really be classified as a map. This idea is explained greatly by my colleague RC which compares the value to evoke feelings and histories for the Zuni people rather than being an accurate representation of the area. This is still an interesting visualization, but we have to question in accuracy for everyone.

Historical data text sources are often filled with metadata about where a subject or object was made and stored. There is a plethora of geographical information throughout any textual document, no matter how old. The idea of having this information embedded in textual data sounds great because you could tag any document with the corresponding data in which it includes itself, however, the challenge surrounding this is that it is difficult to extract that sort of geospatial information on a large scale. For example, sorting through hundreds and thousands of documents attempting to extract the location stated in each document has been a hard obstacle in the realm of digital work. A DH2018, Mexico City article mentions how a piece of text might say Paris. The question standing would be if the writer intended to talk about Paris, France or Paris, Texas, USA. Although with close reading through the document that would be in you could figure out it was most likely the capital of France they were talking about, it just might be Texas the writer is talking about. This sort of close reading is simply impossible when we start to discuss hundreds and thousands of documents, and for that reason digital workers use computational methods that identify and geolocate place-based data. Tools such as Named Entity Recognition (NER) and natural language processing (NLP) are used to find and label geospatial data factors, such as countries, states, and cities, at scale.

Johanna Drucker writes about graphical approaches to the digital humanities, and the challenges in doing so. The digital humanities often use data visualization methods and user interfaces that fundamentally contradict conventions of the humanities. The challenges surrounding graphical methods found in the humanities with digital work stem from the fact that they first originated in the humanities. Remembering the relative youth of the digital realm, all of these methods are being applied second hand to digital work which raises some complications. When attempting to apply graphical methods to any digital work, it is necessary to analyze the graphical method first from a humanistic approach in order to adapt it to digital matters. While using graphical methods such as spreadsheets and other grid forms, bar charts, bubble charts, and network diagrams it is so important to understand how these tools we may use in our data analysis have undergone fundamental developments in the humanities field. We need to remember to assess our use of data visualization tools based on the assumptions built into the first developments of these visualization methods, as they didn’t take place digitally.

This reading about psychohistory I found very interesting! I don’t usually think about mathematics being related to the reactions of human conglomerates to fixed social and economic stimuli. This source does mention the it assumed that the human “conglomerate being dealt with is sufficiently large for valid statistical treatment.” The size reaction is determined Seldon’s First Theorem along with the development of properties congruent to those of such social and economic forces. It is amazing the dialog that they are able to create using this technology. My colleague with J-OS hits the nail on the head when they say that this article highlights the fact that data is more than just numbers in their reading response to this article. They were also interested into how data can be used to investigate human interaction.

In this blog post, we are informed about the pros and cons of social media data. This data is quite controversial because it can be very useful at times, but can also affect people’s privacy. The article opens with the pros of social media data. Projects and data analysis that used to take almost 50 years can now be compiled in less than one year with less people working and a greater sample size. Similarly, the data can be more accurate because people do not know they are in an experiment. Often times, people tend to tell researchers what they want to hear, or change their behavior because they are being observed. Social Media data eliminates those biases.

On the other hand, social media and the data taken from it can be harmful or inaccurate. Firstly, you must be mindful of the users privacy and the platform’s agreements when taking data.  Many people are not comfortable with their social media’s being taken for data, and companies should respect that. Privacy is not the only problem with social media data. Often times, it is hard to  properly represent a group of people using social media. The article states, “Social Media users tend to be WEIRD: wealthy, educated, industrialized, rich and democratic societies. This group is already over-represented in social science and psychology research studies, which may be subtly skewing our models of human behavior.” Much of the data taken from social media can be skewed because of who is using it, and the possibility of bots and other accounts generated by computers.

Overall, the use of social media data is incredibly interesting, and something that should be heavily considered. It has many positive impacts but can also have many negative impacts. My classmate, PE, explores a positive impact of social media in a reflection. “The relationships between people, the interconnections between groups and the overarching figures allow historians to learn who the key players of history are just like today.” Today, we can compile information and determine connections between people through social media, as well as find information about our past.

This idea of social media data is extremely topical as we proceed into the era of advanced technology.

The text is about as said in it “ not to define nor suggest any technological implementation for any of these facets, but rather to define the characteristics, norms, and practices that data resources, tools, and infrastructures should exhibit in order to be considered ‘FAIR.’” .what I got from reading this text is that FAIRness is not a dichotomy but more like a scale. Also, that data FAIRness facets are that data should be Findable, Accessible, Interoperable, and Re-usable. On CM-B’s response, there were points made about how fairness is difficult to achieve because of several reasons, CM-B mentioned two main ones which are that there is “no universal storage of data that ensure that all data recorded is done with unique… identifier” and that “technology advances at different velocities in different parts of the world.”