Hoodrat Scholarship

After reading this week’s articles I realized that I still have a lot to learn as a doctoral student and that I am not too sure if I am reading to take the training wheels off. Ponds article was very dense for me and I found myself feeling frustrated as I was reading through it. I hope to see some of my other classmate’s reflections so that I can maybe understand it better explained differently.

This is how I felt reading Ponds work.

Barisione is aiming to fill a void by looking at the idea of DMO. They do this by looking at the discussion around the hashtag #RefugeesWelcome. The methodology that they are using is users’ metadata analyses, network mapping, and qualitative content analysis by using the hashtag (seeing what conversations, opinions, and ideas are forming around it).  They looked at a few things within the hashtag such as follower count, location, a public profile description, retweets of a specific message, and language. All of these were important to moving forward with full content analysis. They used hashtagify.me to monitor and collect their tweets. They did a range from Sept 2015 – April 2016 because hashtags can shift topic overtime when events may have passed or not as relevant anymore. From personal experience, I have noticed that using a smaller sample range allows you to stick to people who are using the hashtag for the actual situation vs just to get clicks/retweets for exposure. I would say their method choice was deductive because they were testing DMO. Something that already exists in reference to that particular hashtag. Based on their results they were able to show how the hashtag gained momentum and rose the DMO status early on in its conception.  Although it was mainly more known accounts they noticed that isolated groups as well were able to mobilize and engage in a common emotional reaction to this issue. The sense of community was displayed in the earlier stages of the hashtag. One thing that was interesting in the results and conclusion was how once it was seen as a permanent issue it didn’t withstand its high usage (the hashtag). I believe that this study was done well. I don’t think they lacked anything but I would like to maybe see future work that looks at the cycle of hashtags of global larger-scale happenings to see if there is a pattern of weeks, days, months, etc.

Ponds work took a different approach. Pond looked at political movements after the UK riots and compared the hashtags Ponds looked to question a particular theory. That theory is the connective action theory. They wanted to respond to its criticisms by using an analysis of software systems.  They downloaded a series of tweets surrounding the UK riots over a 2 week period. A script was created to extra hashtags from the string ‘r-o-i-t’ to see which hashtags were important and appeared more. I am not too sure if this would be considered inductive research because they were not creating a theory but challenging its shortcomings based on their findings and opinions. I found this article a bit harder to understand and process. The layout was not as plainly stated as the other in my opinion and as a budding scholar, I find that as a weakness.  As a digital media scholar in particular I am looking for a layout of research that is straightforward and mapped out in sections labeled clearly like methodology, results, etc.  I also think that maybe later on in my studies I will understand this better.

So, of course, I have a pop culture reference to start off my post. On Love and Hip Hip NY, Cardi B was fussing and cussing at one of the people on the show during the reunion and created an iconic gif/meme “WHAT WAS THE REASON?”.

Anywho, when I was creating my Venn diagram all I kept saying to myself was “WHAT WAS THE METHOD?”. Random I know.

Below I created a very easy to read Venn diagram of the overlapping methods/differences. When I say “live” people I mean that they did a survey questionnaire on a group of people they actually came into contact with. The other two studies used people, of course, but they did not necessarily have direct interaction with these people. They more so studied the content that was posted by these people on social networks such as Twitter and forums. This is important to note because when people may be unaware of their content being analyzed they are more likely to speak more freely and sometimes (as we see in the #notracist article) more disgustingly about other cultures and races. In reference to this and Gina’s article, the use of red flags as warnings for others in these groups were posted under pseudonyms. This gives them some veil of anonymity.

Both Gina and the authors of #notracist used content analysis in different ways(through python using the constructivist theory). Gina used it to prove that people use these forums to position themselves as unofficial border police. She also analyzed how those in these threads (forums) uphold polarizing beliefs that dictate their posts, engagements, and “warnings”.

With the #notracist article they looked at the hashtag #notracist using the program Chorus. They wanted to identify racial denial through the hashtag #notracist. Since this program did not have a way to decipher the connection of a tweet to a specific situation that would possibly cause this hashtag they looked into other tags as well to see how the data grabbed and categorized the tweets.

Overall all 3 authors’ research focused on some digital data whether it was through a traditional survey or content analysis. By using these digital formats they were all able to garner a lot of data although some data came with more digging to do (#notracist and their hashtag search). Using “live” subjects left the door open for responses to the survey to be censored due to fear of employer or distrust of the researchers.

In my own research, I have combined both content analysis, survey, and a focus group. I am interested to see, in the future, if someone will take these studies and add these other methods to dig deeper.

Although I have engaged in these readings in the past I was excited to look at them through a different lens. Reading and discussing these readings with Latryce got my wheels turning and had me thinking of so many ways that we can combine the two (reality and digital soc) and how that can be emersed into our own personal pedagogies.

Firstly, I would like to point out that I am still taken aback at the way Digital Sociology was viewed by old-school soc folks. There was a lot of shade and envy. The concept of “google envy” blew my mind the first time I read it and this time around too. One would think that people would be open and accepting of more efficient and faster ways of collecting Big Data. I know that I am a proponent of working smarter and not harder so this confuses me.

(me as confused Beyoncé)

“Some sociologists have speculated that in a context in which many diverse actors and organisations can collect and analyse social data from digital sources, the claim of sociologists that they have superior knowledge of researching social life and access to social data is challenged.” Challenging something like the works and strides in Digital Sociology is counterproductive in my eyes.Must sociologists suffer from ‘data envy’ (Back 2012: 19) or what otherwise has been termed ‘Google envy’ (Rogers 2013: 206) in this age of the corporatisation of big data?”

I want to talk about using digital sociology in my own pedagogies. After thinking about what that looks like in a classroom setting or learning community I have come up with a few things that are non-negotiable when it comes to digital sociology + classroom pedagogy.

In order to teach students what digital sociology is and how it can be utilized you first have to gauge their engagement with what a social digital community is. These communities consist of blogs, forums, open access materials, online learning communities (ex: Black Women Radical School), and social groups created on social media platforms (Academia Twitter, Black Twitter, Blackademia). Their understanding of this will make it easier to introduce these things and use them as supplemental materials to build a classroom with a foundation in digital sociology.

One way I have been doing this in my own classroom is that I encourage students to create digital learning communities in the classroom through an app they have (like GroupMe). I then encourage them to pay attention to how our classwork works with the digital, what does “digital” mean, and how we can use digital to measure things such as archiving social media posts on a specific subject to study reactions.

(me explaining how digital is everywhere, talking about surveillance, and the FBI agent in my computer to my students)

One last thing I wanted to talk about. So in Barnard’s text, they talked about the FIVE OBJECTIVES FOR THE FUTURE OF DIGITAL SOCIOLOGY. The part that I want to chat about is the first objective: Renew our analytical orientation – this includes our theories, methods, ontologies, and epistemologies – to better account for the ongoing shift toward an increasingly networked social world. I want to look at this in connection to your question in your video about theoretical and epistemological foundations.  What came to mind for me is that digital sociologists are relying on the constant renewal and reviewal of updated language and signifiers that speak to the CURRENT state of social digital communities. Because this discipline section focuses on a thing (I cannot find another word to describe what the digital/social is)  that is everchanging they have to rely on being ahead of the curve by staying abreast of what is changing and evolving in these spaces. Word usage, labeling, and data are changing because we are evolving into a digitized society.

For those who don’t know me well yet, Gina knows this, I often refer to digital sociology as tin-foil hat business. It’s a spiral that can send you all around the google machine. I am excited to read everyone’s posts and to learn more this semester.

My name is KáLyn Coghill and my pronouns are she/her/Beyoncé (yes this is my pronoun and once you get to know me it will all make sense). I am from Richmond, Virginia (Highland Park) and Antioch/Sacramento California  — I lived in both places equally off and on through my childhood starting at 6 mos. I am a second-semester doctoral student in the Media, Art, and Text program.

I have some background in HTML and CSS. It all began coding my myspace page back in the day and I kept at it leisurely as a side hustle (helping with websites and blogs). I am really looking forward to learning more about Python and really gaining/polishing some skills to make

me a more efficient researcher.

My favorite fact about myself is that I am the second oldest of 10! Yes, you read that right, ten!