Katharine Sipio!Assignment #5!March 5, 2015!!Part A: Facebook data collected using Netvizz; visualizations created through Gephi.!!Part B: Connect Your Data to Your Visualization!

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Graph  #1  

Spa*aliza*on:  Force  Atlas  2Size:  BetweennessColor:  Communi4es  by  modularity.  

Graph  #2  

Spa*aliza*on:  Radial  Axis  Layout Size:  BetweennessColor:  Sex  

The clusters present in graph #1 reflect some of the communities I saw in my last assignment where I used Wolfram to collect my data. In these visualizations made by Gephi, there are 613 nodes (friends) and 16,199 edges (linkages). In this graph, there is a clear separation between my communities (hometown, high school, college, and graduate school). I used the community detection algorithm (communities by modularity), which successfully calculated the communities. I then colorized the communities to more easily differentiate them. My identical twin sister is also placed at the center of the graph again, with our two groups of Boston friends above and below her on the graph. With this visualization, I can see more of the edges and how they blend into multiple communities (high school friends going to the same college as me, etc.) I also think it’s interesting that I can see how dense each cluster is and the visualization created from these communities is almost a triangle shape. !!In graph #2, my sister is once again clearly represented as the largest node on the visualization, which I used the Radial Axis algorithm to spatialize. Instead of being clustered and colorized by communities, this visualization is colorized using gender. My Facebook network is fairly close to being split almost evenly with genders (57% female, 41% male). A lot of the edges cross one another and make the inside of the visualization appear more dense and interconnected. However, both my college and graduate school have a higher female student population, and I think that is what tips the scale in favor of females on this graph.!!An advantages of these visualizations, specifically in graph #1, is having more information about the communities and edges, as well as being able to view the .cvs files that can be exported and ordered based on degree, modularity, betweenness, etc. My sister is the largest node in my network and has a degree number of 439, which shows how connected we are in this network. Limitations of these graphs could be me not being sure where I sit within this network, although I can make an educated guess that it’s near my sister. I also thought the first graph with the communities was easier to understand and interpret than my second graph.

Part C: Export Data — Data exported from Gephi (label, sex, degree, betweenness, modularity) to a .csv file, sorted by betweenness, and uploaded and linked to my gorilladragon.com gallery.!!Part D: Groups— Queer Exchange Facebook Network — 7,855 members as of 12/1/13!Geiskeking (2013) claims that “rather than a top- down culture, Queer Exchange repeats the interwoven and overlapping descriptions of queer spaces and lives that have described lgbtq life across cities, states, and times. In other words, many cultures often demonstrate relationships and dynamics that show some dominant voices overtaking others, or friends being connected to only one other person so they wander on the periphery. Instead this graph shows an interwoven society” (p. 1).!!

!Queer Exchange Facebook Network!Spatialization: Yifan Hu!Size: Betweeness!Color: Communities by modularity !!!!!!!!!!Queer Exchange Facebook Network!Spatialization: Yifan Hu!Size: Degree!Color: Sex!!!!!!!!Queer Exchange Facebook Network!Spatialization: OpenOrd!Size: Betweeness!Color: Communities by modularity !!!!!!!Queer Exchange Facebook Network!Spatialization: OpenOrd!Size: Degree!Color: Sex!!!!!!

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Geiseking commented that the Queer Exchange Facebook Network was a very interwoven network, and while that may be true in some regard for a portion of its communities, it does not hold true for the entire network. There are a lot of individuals who are not in communities (the graph has separated many into groups with just one individual). The different communities are not labeled either, so it’s difficult to understand what the colors mean and where these communities sit in the entirety of the network (with the numbers provided for these visualizations). In the visualizations I created, one can see the communities (one person) as the outlines of the circles with the Yifan Hu algorithm, and also in the OpenOrd algorithm. They surround the cluster of communities that sit inside of the visualization.!!Because the communities were not sorted in an identifiable way (all users were anonymous), sorting these nodes by gender gave some more insight into the population. For example, Gephi tells me that 49% of the nodes collected represent females, while 39% represent males. There are 4,698 nodes in the visualization total with 10,789 edges. About 12% of the nodes are not labeled as male or female. When this graph was created there was a total of 7,855 members in the Queer Exchange Facebook Network, and I’m curious to see what the gender of this population was overall. In these visualizations and by the sample of nodes in this network, one can see a influence of the female population on the LGBTQ community. However, I’m still unsure of why so many single individuals were categorized into separate groups.

Part E: S-Shaped Social Networks!!Tumblr!! !Tumblr is a blogging platform that was launched in 2007 founded by David Karp, and is currently estimated to have 420 million users in 2015. From the growth stats that my partner and I could locate, it is clear that there is a steady growth of the platform over time. Tumblr has a very unique user base, and from our research we found that most users are between the ages of 18-29. Tumblr has a lot of niche users and communities (think musicians/bands, LGBTQ, fandoms, viral content, etc.) Mapping out this data with technology, such as Gephi, might reveal some further insights about the platform and its community of users. Below, we have graphed Tumblr’s growth so far:!

!Innovators and early adopters of Tumblr who have stayed loyal since its creation have seen the platform and its own functions/aesthetic go through growth and have remained fairly loyal. However, in an article published by Forbes in 2014, they claim that Tumblr’s traffic isn’t growing. Yahoo, which bought Tumblr for $990 million in May 2013, is cited as one of the potential reasons behind this lack of growth (the platform is not following the rise in numbers/trajectory that Yahoo originally anticipated). The corporate feel that Yahoo brings to Tumblr with its use of ads, and the way the company extracts/exploits data from users to monetize the platform, can leave users feeling like Tumblr has been invaded by unwelcome guests crashing the party.

Does this purchase by Yahoo signal that Tumblr is potentially reaching its critical mass? !!Forbes also states that, “Tumblr’s traffic flattening corresponds with a period of torrid growth for viral content sites, particularly Buzzfeed and Upworthy. Those sites deal in the same sorts of content — funny gifs and memes, inspirational photos and videos — that’s popular on Tumblr.” This could be another potential correlation to the platform’s slowing growth. However, despite these reports and claims, It is still too early to predict what will happen with the platform in the long run. !!

Tumblr remains a bit of a social media enigma when it comes down to examining and analyzing its data. Its future is in the developing culture of the site, and its users’ ultimate decision to remain loyal or abandon it. Right now, the platform appears to be experiencing a steady growth of users despite Yahoo’s disappointment, and it remains a prominent platform of cultural exchange for its youthful users.

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