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.