I selected my data from the beginning of February, February 1st to be exact, since my topic was heavily discussed around this time, thus there will be a significant amount of more relevant tweets to my topic regarding proposals made by the government. The number of tweets I am using is around 4,000. When selecting #1 on the t2g.py file, I am expecting to see a great number of retweets and their contents dominating the map since the majority of my tweets are retweets. After running the python script, I have 7586 lines.
When opening the Le Mis example file, i noted 77 nodes and 254 edges. Undirected graph would work better than a directed graph to express relationships between characters rather than conversations, allowing to see the amount each character relates to each other rather than just their conversations to one another.
Valjean has the ID #11. Fantine has ID #23. Valjean targets ID #’s 0,2,3,and 10. Fantine targets ID# 11,12,16,17,18,19,20,21, and 22.
compared to the previous graph, it appears that the groups have become more clustered and centered around each other rather than intermixed with other nodes.
When setting the betweenness centrality, I believe it will increase the size and weight of the nodes based off of relevance.
For my own gephi graph, I chose to use a repulsion of 10,000 since there is a large amount of nodes represented. My average path length was 4.752, which I believe means the relevance between nodes and the confidence factor of each pathway. The connected components is 375, meaning I have 375 connected groups. The modularity is .816, meaning the strength of the ties the nodes have within each group.
It is hard to see the connections in this graph since there are a lot of small nodes with a degree of only one or two. surprisingly, this is still the graph after changing the degree to at least 5! Analyzing the Big purple group (NRA) and the blue group (SenatorSessions), I can see a lot of commonality with the tweets between these two twitter handles. perhaps during this period of time Senator Sessions was advocating for something relating to gun control? It seems as if the majority of my groups are big retweeters in the twitter community since the majority of group names are specific twitter handles.
Hey Harrison,
Cool figure, looks like those splatter paint images you see in museums or held up by a magnet on your fridge at home. It makes a lot of sense to me that the NRA is the big dot in your gephi. I would assume that they are always tweeting about #2A, seeing they have a lot at stake for this amendment. If my well being had to do with the support of a rule, I would also tweet about it positively 24/7. I don’t know much about Senator Sessions, but it does make sense that he may be trying to pass a bill. That is a very thoughtful insight. We should look into this further in the future. Maybe over a nice drink and a bagel.
Best Wishes,
Tim
Harrison, I am surprised that you do not have as much edges in your graph that I did (in my graph, nearly everyone was connected) since I assumed that #2a was a very specific topic and that people who tweet #2a are included in a specific community. However, your shortness of your path length shows that though people may not talk to another, they are still relatively connected in some way. I also find it interesting that your graph consists of other nodes that are still quite large and compete in size with the NRA, your largest node. People often think of the NRA when it comes to gun control, but your graph demonstrates that other significant organizations and individuals also discuss gun control that are more mainstream than the media portrays them as.
For future analysis, I am interested in learning more on how the community that tweets #2a perceives Jeff Sessions and the U.S. Justice Department and how their tweet trends change as Jeff Sessions tweets or receives more spotlight in the media. I want to see the labels on the graph as well. Your graph demonstrated that though there exists many individual tweeters, they are still connected or those who do talk to each other are much more connected (due to your short average path length).