Gatekeepers

This course has helped given me a different perspective on digital literacy. Looking at the speed at which technology is being created, I anticipate I will lose my touch if I were to even step away for a second. I can also imagine there will be much to talk about with the repeal of net neutrality in the next course.

For my final paper I chose to focus on social media and how it can be used to improve disaster relief situations. In my paper I started by revisiting the argument between Andrew and David, and looked their argument on Gatekeeping vs. Amateurs. I found that certain processes in disaster relief thrive better with amateurs and some better with gatekeepers.

In one paper I found, a crowdsourcing software implementation, similar to Uber, helped match people who were in need of help with people who needed help (Murali et al., 2016). This can be especially useful when disaster relief may not even be scheduled, but people are able to offer assistance to each other. The most interesting thing I found, though, was that in using crowdsourcing software, we mostly focus on people who are amateurs using the system, but the dynamic of a gatekeeper still does exist within the software. In the case I found the software punishes or rewards people who behave as expected. Additionally, people can be rated and this rating can be viewed by others. This is all to deter misuse and exploitation of the system. At this point we rely on whether or not the design and functionality actually work well enough to maintain a proper workflow so that as many victims get help from volunteers as possible.
I also tried to focus on how social media in the papers I looked at used different levels of communication as stated by Rheingold. I specifically looked at different levels of collective action and how certain applications may support networking, coordination, cooperation, and collaboration (Rheingold, 2014, pp. 153-154). I found that most applications these days are achieving a collaborative level of collective action.

I also wanted to quickly share some of the data from my case study. I did my case study on Equifax and used Twitter and Google’s Natural Language API to generate some meaningful data for my study. The Google API focuses on Sentiment which is basically how positive or negative the words used in a sentence are. I calculated average sentiment per tweet. I then used a free tool called Tableau to visualize tweets made by Equifax over time. I recommend Tableau for anyone who needs to make a chart and share it quickly, I found it about as good as any paid ones I have used in the past.

TwitterEquifax

Twitter Equifax Data

https://public.tableau.com/profile/miriam6169#!/vizhome/EquifaxData/Story1

References

Murali, S., Krishnapriya, V., Thomas, A. (2016). Crowdsourcing for disaster relief: A multi-platform model. 2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 264-268. doi: 10.1109/DISCOVER.2016.7806269

Rheingold, H. (2012). Net Smart. Cambridge, Massachusetts: The MIT Press.

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Posted on December 17, 2017, in Digital, Literacy, Social Media, Technology. Bookmark the permalink. Leave a comment.

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