{"id":2811,"date":"2017-03-27T09:36:49","date_gmt":"2017-03-27T14:36:49","guid":{"rendered":"http:\/\/commons.trincoll.edu\/amst-data-driven\/?p=2811"},"modified":"2017-03-31T14:57:51","modified_gmt":"2017-03-31T19:57:51","slug":"standwithpp-gephi-analysis","status":"publish","type":"post","link":"http:\/\/commons.trincoll.edu\/amst-data-driven\/2017\/03\/27\/standwithpp-gephi-analysis\/","title":{"rendered":"#StandwithPP Gephi Analysis"},"content":{"rendered":"

I decided to use the data that I had collected from the time period of Thursday March 23-Monday March 27(Today).\u00a0 Besides for the obvious factors that this data was both the most recent and spanned around the amount of tweets (2,000-3,500) that would be necessary for this lab, I also chose these specific days of the week due to my observations from previous weeks.\u00a0 When looking into my data scraping history, I have often found that there are most tweets generated on my hashtag during the end of the week\/the weekend.\u00a0 Thus, I have decided to use my data that would be from Thursday-Monday.<\/p>\n

When viewing my tweets it soon becomes apparent that there are some columns that are left blank. \u00a0This would make sense when understanding the dataset in terms of source and the person mentioned. \u00a0In this case that would mean that although there must always be a person who is generating the tweet, they do not necessarily always have to be mentioning another user.<\/p>\n

When opening this data in Gephi it displays an Undirected Graph of 77 Nodes and 254 Edges. \u00a0This would be considered a undirected graph because there is no cause and effect at play. \u00a0In this data we are observing mutual connections amongst the data. \u00a0It is assumed that all the relationships within this data are mutual. \u00a0Although there are some characters who display to have different social and economic standing within the novel, we are looking at interactions on a basic level, thus this should be viewed in an undirected graph. \u00a0When viewing the data it soon becomes apparent that Valjean\u2019s ID is 11 and Fantine\u2019s ID is 23. Using their ID, Valjean talks to 4 different characters while Fantine talks to 9. \u00a0I think this is interesting because Valjean is the main character, so I would expect that he would have more interactions than Fantine. \u00a0Although he is the main character, this could be explained due to the fact that Fantine worked in a factory and then became a prostitute, so she would have more overall interactions. \u00a0This also makes sense because Valjean was an prisoner\/slave who had escaped, thus he spends the entire movie hiding- limiting his social interactions.<\/p>\n

It becomes apparent that after changing the centrality settings the node size immediately changes. \u00a0When doing this Valjean’s node becomes significantly larger due to his presence within the novel as the main character. \u00a0Being the main character, Valjean interacts with the most individuals, causing his node size to increase. \u00a0After running a statistical analysis it soon becomes clear that the APL is 2.64 and the diameter was 5.<\/p>\n

\"les<\/p>\n

\"Untitled\"<\/p>\n

For my personal data I chose to use a repulsion strength of 10,000. \u00a0I chose this after playing around with many different numbers and seeing which would best represent the main user interactions while still displaying smaller nodes. \u00a0My average path length was 3.614 and my CC number was 214. \u00a0When considering the fact that\u00a0Average Path Length is defined by “the average number of connections between people in this dataset”, this would show that the average number of connections between users tweeting using my hashtag is 3.614. \u00a0This would make sense due to the fact that\u00a0there are many individuals who are interacting with major users such as ppact, but when taking an average one must consider all of the users that have no mutual connections.<\/span><\/p>\n

\"standwithppgephi\"<\/p>\n

When looking at my graph overall it became apparent that the two main users that were being interacted with were ppact and ppfa. \u00a0When looking up these users I found that PPFA stands for planned parenthood federation of america and PPACT stands for planned parenthood action fund. \u00a0Below I have copied both of these twitter accounts. \u00a0When viewing these two accounts on twitter it first becomes clear that they have the same symbol as their icon. \u00a0After looking this symbol up I found that this is the official symbol of planned parenthood. \u00a0The next thing that becomes apparent when looking at these two twitter users is the fact that they are both verified. \u00a0This points at the fact that these are the official twitter accounts of planned parenthood. \u00a0With this in mind, it now makes sense as to why these would generate the most interactions within the hashtag #StandwithPP, causing them to display the biggest note.<\/p>\n

\"PPFA\"\u00a0\"ppact\"<\/p>\n

Although these are both the biggest nodes displayed on my graph, it is interesting to note the fact that ppact is almost double the size of the general planned parenthood federation of america account. While one would assume that the general planned parenthood account would be interacted with more, I believe this points at the nature of the tweets that are being composed using this hashtag. \u00a0When understanding the hashtag #StandwithPP it\u00a0is important to note that those who are tweeting using this hashtag are doing so to actively create change. \u00a0In this they are writing with the intention of standing with planned parenthood during a time in which its position is at risk due to current political decisions being made. \u00a0With this in mind, it would make sense that they are interacting more with the action fund. \u00a0They are most likely trying to raise money for planned parenthood in hopes that it will aid in supporting it from being removed.<\/p>\n","protected":false},"excerpt":{"rendered":"

I decided to use the data that I had collected from the time period of Thursday March 23-Monday March 27(Today).\u00a0 Besides for the obvious factors that this data was both the most recent and spanned around the amount of tweets (2,000-3,500) that would be necessary for this lab, I also chose these specific days of…<\/p>\n","protected":false},"author":1969,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2811"}],"collection":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/users\/1969"}],"replies":[{"embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/comments?post=2811"}],"version-history":[{"count":7,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2811\/revisions"}],"predecessor-version":[{"id":3115,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/posts\/2811\/revisions\/3115"}],"wp:attachment":[{"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/media?parent=2811"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/categories?post=2811"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/commons.trincoll.edu\/amst-data-driven\/wp-json\/wp\/v2\/tags?post=2811"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}