This post will focus on the post by Peter Saunders about the hashtags. I absolutely agree with the point that hashtags sorted various information into certain categories and “handle the dirty work of aggregating” for the users. It is really interesting that Peter points out the inefficiency caused by the misspelling of hashtags. If you really think about how hashtags work, you will keywords, or tags, used in some blog websites including WordPress work the exactly same way. If you put the network into graphs, you might be able to see tags as endpoints that link to the endpoints on the other side, which are the posts. It resonates with the other reading, “Graphs” from Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg.
For instance, on instagram, you post a photo with #dataculture. The hashtag serves as a hyperlink between the category and the photo and thus the photo is the endpoint. The other way around, if you search the hashtag #dataculture, the hashtag becomes an endpoint with leads to the category. Just like the graph shown below:
If you have multiple of photos with multiple hashtags, the graph will go more complicated. And then with a large user group, the graph goes even crazier. This is where Cloud technology came in.
To draw a conclusion for this response, the hashtags are certainly inefficient sometimes because of the misspelling, but, instead of creating different databases, the hashtags create multiple hyperlinks to sort information into categories. Comparatively, hashtags are very efficient.
Graphs credit to:
http://en.wikipedia.org/wiki/Planar_graph
http://en.wikiversity.org/wiki/Web_Science/Part2:_Emerging_Web_Properties/Emerging_Structure_of_the_Web
http://en.wikipedia.org/wiki/Linked_data