ECI 589 SNA Independent Project 2 Speckart
James Speckart
2023-02-26
Introduction and Research Question
I thought it would be interesting to see if online discussions about movies showed interesting patterns of discussion that could be discovered through social network analysis even as genres and subjects of discussion changed. To look into this question, I looked to downloading discussions from the media sharing and discussion site Reddit (www.reddit.com), which hosts thousands of user-moderated discussion groups on almost any topic. It has a number of “subreddits” devoted to movies and movie-making, from discussing cinema in general to discussing cinematography.
I looked at list of movie related subreddits to find candidate subreddits to sample from: https://www.reddit.com/r/movies/comments/wxee2/the_big_list_of_movie_related_subreddits_2/. I selected subreddits with heavy comment activity–many subreddits have few posts and few comments. For example, r/horror was selected because it is dominated by movie-related comments, as opposed to r/scifi, another heavily frequented subreddit, which covers books and other art forms heavily, and therefore is not as focused on movies. The list of subreddits selected was r/movies, r/film, r/classicfilms, r/bollywood, r/documentaries, r/badmovies, and r/horror. I used https://camas.unddit.com/ reddit search engine to gather 1000 comments from 3 month period of January 1 to April 1, 2022. Each subreddit’s comments were saved as JSON files and imported into R.
Data Processing
Each subreddit’s comments were saved as JSON files and imported into R. Select columns were combined into a single dataframe containing author names, thread links, relevant dates, and other information.
I tried to create recipients for each thread so that I could get any in-degree above 0 and transitivity above 0, but after many struggles, it seems that I don’t have the original authors for all of these threads, which means I could not create complete networks that list all authors. But because each thread does have a permanent link that acts as a “home base” for the discussion, I’m using the permalink to each thread as a stand in for the original author. But because a permalink is generated once by Reddit, and it does not respond to any comments, these permalinks can only receive incoming ties, and cannot send them out like a human author can. A consequence of this is that the human authors cannot receive reciprocal ties from the permalink, although they can from other authors. This means that this data generates a network with 0 in-degree and 0 transitivity. However other measures can still be calculated and insightful plots can still be generated.
Generating a network plot for all subreddits in the data
The data was put into a network object using the tblgraph() function of the tidygraph package for R.
## [1] 6989## [1] 6989The network has 6989 vertices and 6989 edges, which I believe is a reflection of the structure of the data, where the links to threads were used as recipients rather than authors. Let’s take a look at the resulting network using a stress plot:
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That’s kind of pretty, but isn’t terribly informative. Each subreddit gets a dedicated color, and we can see that there are multiple levels to some threads, but let’s try a Fruchterman-Reingold plot to see if it’s any more clear.
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It looks like there’s a lot going on, an it’s mostly unclear. There are some threads that trail off as single response chains, but the overall dynamics of the network are unclear. What do our centrality measures tell us?
## $res
## [1] 2 637 2 2 5 2 3 3 3 2 2 2 2 4 2 2 2 3
## [19] 2 2 2 2 3 2 3 3 2 2 3 2 2 3 2 2 3 2
## [37] 2 2 2 2 3 2 2 2 2 2 15 3 2 2 3 2 2 2
## [55] 3 2 2 2 2 4 2 4 2 2 2 2 2 2 2 7 2 3
## [73] 2 2 2 2 2 2 4 2 3 3 2 2 2 4 2 2 2 4
## [91] 4 2 2 2 3 2 2 9 2 2 5 2 2 2 2 2 4 2
## [109] 4 2 2 2 2 4 2 2 2 2 4 2 3 2 2 4 5 3
## [127] 2 2 3 2 3 4 3 2 2 2 2 2 2 2 4 2 3 2
## [145] 2 3 2 48 5 6 2 2 4 2 2 2 3 2 6 2 3 2
## [163] 2 2 2 6 7 2 2 2 2 3 2 3 2 2 2 2 2 2
## [181] 2 3 3 3 3 2 2 3 2 3 2 2 2 2 3 2 2 2
## [199] 2 2 2 2 2 20 2 2 2 2 3 2 3 2 4 2 2 2
## [217] 2 2 3 2 3 2 2 16 5 2 4 2 2 2 2 2 2 2
## [235] 2 2 2 2 2 2 3 2 3 3 2 2 2 4 110 2 2 4
## [253] 2 2 2 3 5 4 2 2 2 3 2 2 2 2 2 2 3 5
## [271] 2 4 2 2 3 2 3 2 2 2 3 3 3 2 2 9 2 2
## [289] 7 2 4 2 2 2 2 2 2 3 2 4 6 2 2 2 2 2
## [307] 2 3 2 2 2 3 2 2 2 5 3 13 2 2 3 2 2 3
## [325] 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2
## [343] 2 2 6 4 2 2 2 2 3 2 2 2 7 3 2 2 4 7
## [361] 4 5 2 2 3 6 2 2 2 3 3 4 2 2 3 2 2 2
## [379] 2 2 2 2 3 2 5 2 5 3 3 2 2 2 2 7 2 2
## [397] 2 3 2 3 2 3 2 2 6 2 2 4 2 4 4 2 2 2
## [415] 3 16 7 3 2 2 3 2 14 3 2 2 4 2 2 3 2 7
## [433] 2 2 2 2 2 2 2 4 2 2 2 3 2 2 2 2 7 2
## [451] 2 2 2 3 2 2 2 3 4 2 2 2 2 3 2 3 2 2
## [469] 2 2 10 2 4 2 2 2 2 2 5 3 4 5 5 2 5 3
## [487] 2 3 2 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2
## [505] 2 2 2 2 3 3 2 2 3 2 2 2 2 3 2 2 2 3
## [523] 3 11 2 4 2 2 4 2 2 2 3 2 2 2 2 2 2 2
## [541] 2 2 8 10 2 2 7 2 2 2 3 2 2 2 4 5 2 2
## [559] 3 2 2 2 2 2 2 5 2 2 2 2 2 2 2 6 2 2
## [577] 2 8 2 2 2 2 3 4 2 5 2 2 2 2 2 6 2 4
## [595] 2 3 2 2 2 2 24 2 2 2 2 2 2 5 2 2 2 2
## [613] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2
## [631] 7 2 3 3 2 2 2 2 2 2 4 2 2 3 4 2 2 2
## [649] 2 3 2 52 2 2 2 2 2 4 2 2 2 2 2 2 2 2
## [667] 2 2 3 2 2 3 2 2 2 2 2 2 2 12 2 2 5 2
## [685] 3 3 12 4 2 2 2 2 2 2 2 2 3 2 3 2 3 2
## [703] 2 2 2 2 3 2 2 3 3 2 2 2 2 2 2 2 2 4
## [721] 2 2 2 2 2 2 3 2 2 2 3 3 2 2 3 2 2 20
## [739] 2 2 2 2 3 2 2 2 2 2 2 2 3 2 5 2 4 13
## [757] 3 2 4 2 2 2 2 3 2 2 3 2 2 2 2 4 3 2
## [775] 2 2 3 3 3 5 2 2 4 2 2 2 3 4 2 2 3 2
## [793] 3 2 2 2 4 2 2 2 2 2 2 3 3 2 2 4 2 2
## [811] 20 2 3 2 4 2 8 2 2 2 3 2 2 3 2 2 2 3
## [829] 2 2 3 2 5 2 2 2 4 3 2 2 2 4 2 2 2 2
## [847] 2 3 3 2 3 2 2 2 2 2 3 2 2 2 4 3 2 2
## [865] 2 2 2 2 3 2 2 2 2 3 2 2 2 2 4 19 2 2
## [883] 2 2 2 2 2 3 2 2 2 3 2 5 2 2 2 2 2 2
## [901] 2 2 3 2 3 2 2 2 5 3 3 2 2 4 2 2 2 2
## [919] 2 2 2 2 3 4 2 3 2 3 2 2 2 2 3 74 2 2
## [937] 2 2 4 3 2 2 2 2 2 2 5 4 2 2 2 2 4 2
## [955] 3 6 2 3 2 2 7 2 6 2 2 3 2 3 2 2 2 2
## [973] 2 2 2 2 3 2 2 2 6 2 6 4 2 2 4 2 2 2
## [991] 2 4 2 2 4 2 2 4 2 2 2 3 2 2 7 2 2 2
## [1009] 4 5 2 2 91 2 3 2 2 2 2 2 2 2 2 2 2 2
## [1027] 2 2 2 9 2 2 2 3 2 2 8 34 2 3 3 2 2 2
## [1045] 8 2 2 2 2 2 7 2 8 2 2 2 4 3 2 2 2 3
## [1063] 2 2 2 2 2 2 3 3 2 6 4 2 2 2 2 2 3 2
## [1081] 4 2 6 3 3 2 2 2 2 2 2 2 3 2 2 2 2 3
## [1099] 2 2 2 2 2 2 2 2 3 2 2 2 2 5 2 4 2 2
## [1117] 5 5 2 4 2 2 2 2 3 2 3 2 2 3 2 2 2 2
## [1135] 6 2 3 2 2 3 2 2 2 5 2 3 3 2 2 2 7 2
## [1153] 4 2 2 2 2 2 2 2 2 2 3 3 2 2 2 4 2 2
## [1171] 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 8
## [1189] 2 2 2 2 2 11 2 5 2 2 3 2 2 2 2 4 2 2
## [1207] 9 3 3 2 2 2 2 3 2 2 5 2 2 2 2 3 8 2
## [1225] 2 2 2 3 2 2 2 2 3 2 12 2 3 2 2 4 2 2
## [1243] 3 2 2 2 2 2 2 2 2 2 3 2 2 4 2 2 2 2
## [1261] 2 2 3 2 2 2 2 2 2 3 2 2 4 2 2 2 2 2
## [1279] 2 2 2 3 2 2 4 2 2 2 2 2 4 3 2 2 2 2
## [1297] 2 2 2 5 3 3 2 2 5 2 2 2 2 2 3 2 4 2
## [1315] 2 3 2 2 4 4 2 5 3 4 2 2 3 2 2 2 7 2
## [1333] 7 2 3 2 2 29 2 2 2 2 2 2 2 6 2 9 2 3
## [1351] 2 2 2 2 2 2 3 2 2 2 2 2 2 7 3 2 2 2
## [1369] 2 2 5 6 2 6 2 2 2 6 6 8 21 2 2 2 2 3
## [1387] 3 2 2 2 2 2 2 2 2 3 2 5 2 2 5 2 2 3
## [1405] 2 3 2 3 2 2 2 4 3 2 2 3 3 2 2 2 2 3
## [1423] 2 3 2 2 2 2 2 4 2 2 4 3 2 2 2 4 2 2
## [1441] 2 2 3 2 2 3 2 2 2 4 2 2 2 2 2 2 2 2
## [1459] 4 3 2 3 3 3 4 13 3 2 2 2 2 2 3 2 2 2
## [1477] 2 4 7 2 2 5 2 12 2 2 2 2 2 3 2 3 6 2
## [1495] 2 2 2 2 2 2 2 6 2 2 2 2 2 2 2 2 2 2
## [1513] 2 2 2 6 2 2 2 2 6 3 3 3 3 2 2 2 2 4
## [1531] 2 2 3 2 2 2 3 2 3 2 2 2 2 2 3 2 2 11
## [1549] 2 2 2 2 2 2 2 2 2 2 12 2 2 2 26 2 2 2
## [1567] 3 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1585] 2 2 2 2 2 2 25 2 3 3 12 3 2 2 9 6 21 2
## [1603] 7 2 2 3 2 2 6 12 2 2 2 2 2 2 2 4 2 2
## [1621] 2 3 2 2 2 2 2 7 2 2 4 2 2 2 5 3 2 2
## [1639] 2 2 2 4 2 3 2 2 2 2 2 35 2 2 2 2 3 2
## [1657] 2 2 6 11 2 3 2 2 5 13 2 2 5 2 11 2 2 2
## [1675] 2 2 5 4 8 4 3 2 4 3 2 2 3 2 2 2 2 2
## [1693] 2 2 2 2 3 5 2 2 2 3 2 2 2 2 7 2 4 3
## [1711] 2 2 2 2 2 4 21 2 2 3 2 3 2 2 2 2 2 2
## [1729] 2 4 3 2 3 2 2 5 2 2 2 3 3 2 2 2 2 2
## [1747] 3 2 4 3 5 2 2 2 3 3 2 2 2 2 2 2 2 2
## [1765] 3 2 2 10 3 3 2 2 2 3 2 2 2 2 3 2 8 2
## [1783] 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2
## [1801] 2 2 2 2 2 2 3 2 2 2 5 6 3 2 2 2 2 2
## [1819] 2 2 7 2 2 2 3 2 10 2 2 2 2 2 2 2 3 2
## [1837] 2 2 2 2 2 2 2 2 3 2 2 3 2 3 4 4 2 3
## [1855] 7 2 2 2 3 2 2 2 2 2 2 2 2 4 2 2 2 2
## [1873] 9 2 4 2 2 3 2 2 2 2 2 2 3 2 5 2 2 2
## [1891] 2 7 2 2 2 2 2 2 2 2 4 2 2 2 3 2 2 2
## [1909] 2 2 2 2 2 4 2 2 3 3 2 2 2 2 2 2 2 2
## [1927] 8 2 4 2 2 2 2 3 2 5 3 2 2 2 3 2 2 2
## [1945] 3 4 2 3 2 2 12 2 2 2 2 2 7 4 2 2 2 2
## [1963] 2 2 2 2 2 3 2 2 2 2 2 2 3 4 3 2 2 2
## [1981] 2 2 2 2 2 2 2 2 6 2 2 2 2 2 3 2 2 2
## [1999] 3 3 2 2 2 2 2 2 2 2 3 3 2 2 5 2 2 2
## [2017] 2 2 2 2 2 2 2 4 4 5 2 2 2 2 2 3 3 3
## [2035] 2 2 2 2 2 4 2 2 3 3 3 2 7 2 2 3 2 3
## [2053] 2 2 2 2 5 2 3 2 2 3 2 2 2 2 4 2 2 2
## [2071] 3 2 2 2 2 2 2 2 2 19 2 2 2 2 3 3 2 3
## [2089] 3 3 2 2 3 2 2 2 2 2 2 2 6 4 4 2 2 2
## [2107] 2 5 3 3 2 4 2 2 2 2 2 2 2 2 3 2 3 2
## [2125] 2 5 2 2 2 3 2 4 2 11 2 2 2 2 3 2 2 2
## [2143] 2 2 2 2 2 8 5 3 6 7 4 2 2 2 2 2 2 2
## [2161] 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2
## [2179] 2 2 4 2 2 2 2 2 2 2 7 3 15 3 2 2 4 2
## [2197] 2 2 2 3 2 2 2 2 2 7 14 2 2 2 3 2 2 4
## [2215] 2 2 2 2 2 2 2 2 2 2 3 2 2 3 2 2 3 2
## [2233] 2 2 2 8 2 2 2 2 2 6 2 2 2 2 2 2 2 8
## [2251] 4 2 2 2 2 4 2 3 3 3 2 2 3 8 5 2 2 2
## [2269] 3 2 2 2 2 2 4 2 5 2 2 2 3 3 2 2 2 2
## [2287] 2 2 4 2 4 3 3 2 2 2 2 2 2 2 2 2 2 2
## [2305] 2 3 2 2 2 3 2 2 2 2 3 2 2 2 3 4 5 5
## [2323] 2 2 2 3 3 2 2 4 2 2 2 2 3 3 2 2 2 2
## [2341] 2 2 2 3 2 4 2 2 3 2 2 2 2 11 2 2 3 18
## [2359] 2 2 2 2 2 2 2 2 2 2 2 4 2 2 13 2 2 2
## [2377] 2 2 2 2 12 2 2 2 2 2 4 2 2 2 2 2 2 3
## [2395] 8 2 2 2 6 2 3 2 2 3 2 2 4 2 2 2 2 12
## [2413] 2 14 7 2 2 3 2 2 7 3 2 2 2 2 2 5 2 2
## [2431] 2 2 2 3 2 2 2 2 2 6 2 2 3 3 2 4 2 7
## [2449] 5 2 2 2 5 6 2 2 2 2 2 2 2 7 4 2 2 2
## [2467] 2 2 11 2 2 2 2 2 2 2 2 3 2 2 2 2 2 4
## [2485] 2 3 3 3 2 3 3 3 2 2 2 2 2 2 3 2 2 4
## [2503] 7 3 2 2 3 2 2 9 2 2 2 2 3 2 2 2 3 2
## [2521] 3 2 2 2 2 7 2 3 2 2 4 2 2 2 2 2 2 2
## [2539] 2 2 2 2 2 2 2 2 2 5 2 2 2 2 2 2 2 2
## [2557] 2 2 10 2 2 2 2 2 6 4 2 2 3 3 2 2 2 2
## [2575] 2 2 2 2 2 3 2 2 2 4 2 2 2 2 3 2 2 3
## [2593] 2 2 4 2 2 2 2 2 2 5 2 2 3 2 2 3 2 2
## [2611] 3 2 3 2 2 3 2 2 2 2 3 3 2 2 2 5 13 2
## [2629] 4 2 4 2 2 2 3 3 2 3 2 2 2 2 2 2 2 2
## [2647] 2 2 2 8 2 2 2 4 3 2 2 4 2 2 2 2 2 3
## [2665] 6 2 2 2 3 2 2 2 2 3 4 2 3 2 10 2 10 2
## [2683] 2 3 2 2 2 2 10 2 2 7 2 2 4 2 2 3 2 2
## [2701] 2 4 2 2 2 2 2 2 2 2 2 4 2 2 4 2 2 2
## [2719] 3 2 3 3 2 2 2 3 2 4 2 4 2 2 2 2 2 2
## [2737] 2 2 2 3 2 4 6 2 3 2 2 2 2 2 10 4 2 2
## [2755] 2 3 2 3 2 3 4 2 2 8 2 4 2 3 2 3 3 2
## [2773] 2 6 2 4 2 2 2 2 2 3 2 2 2 2 2 3 4 2
## [2791] 2 2 2 2 2 3 2 2 2 2 2 8 3 2 2 2 2 3
## [2809] 2 2 2 2 2 11 2 3 2 2 3 3 2 2 2 2 2 8
## [2827] 3 2 3 4 2 2 2 2 2 2 2 2 2 2 5 2 6 3
## [2845] 2 2 3 2 3 2 2 2 2 2 2 2 2 5 2 3 4 3
## [2863] 2 10 3 2 2 2 4 2 2 6 2 2 2 2 2 2 2 2
## [2881] 2 12 2 3 4 2 2 2 2 2 2 3 2 2 2 4 2 3
## [2899] 3 2 2 3 2 2 2 2 2 8 2 3 4 2 2 2 2 2
## [2917] 3 2 3 2 4 4 2 2 7 3 3 3 2 2 3 2 6 2
## [2935] 2 8 2 2 2 2 3 2 2 2 2 2 5 3 2 18 2 12
## [2953] 2 3 2 2 4 2 2 2 2 7 4 2 8 2 2 4 2 3
## [2971] 2 2 3 2 2 2 2 2 2 3 2 4 5 2 2 2 2 2
## [2989] 2 5 2 2 3 4 2 2 2 2 11 6 2 4 2 3 2 3
## [3007] 3 2 5 2 2 2 2 4 2 2 2 6 6 2 2 4 4 2
## [3025] 2 3 2 3 2 2 2 2 4 2 2 2 2 2 2 2 2 2
## [3043] 2 4 2 2 3 4 2 2 2 2 2 7 2 3 4 2 2 3
## [3061] 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3079] 2 2 2 2 4 6 2 2 2 3 2 2 2 2 4 12 21 2
## [3097] 2 2 5 2 3 2 10 2 5 2 4 2 3 2 2 2 2 2
## [3115] 3 2 2 2 2 4 3 4 2 2 3 2 2 3 2 2 2 2
## [3133] 2 4 5 2 3 2 8 2 4 2 3 2 2 2 2 3 3 2
## [3151] 2 2 2 5 2 8 3 4 2 2 2 2 2 2 4 2 2 2
## [3169] 2 2 2 2 4 2 5 2 2 3 2 2 2 2 4 2 2 2
## [3187] 2 3 2 2 3 2 2 4 3 2 2 2 2 2 6 2 4 2
## [3205] 2 3 3 2 2 2 2 3 7 2 2 4 4 2 2 4 4 2
## [3223] 2 2 2 2 3 3 2 2 2 3 2 4 3 2 3 2 2 2
## [3241] 2 2 2 2 2 2 9 4 3 2 2 2 2 3 2 2 2 2
## [3259] 3 2 2 5 2 2 2 5 2 3 3 2 2 2 3 2 2 3
## [3277] 2 2 2 2 4 3 2 2 2 2 2 2 6 2 2 2 5 2
## [3295] 2 2 2 3 3 3 8 2 2 2 3 2 5 2 3 5 2 2
## [3313] 2 2 8 2 2 5 2 2 2 4 2 2 2 2 7 2 2 2
## [3331] 2 2 2 7 2 2 2 2 2 2 3 2 2 3 2 2 2 2
## [3349] 2 2 2 2 3 5 4 2 2 2 2 2 4 3 2 2 2 3
## [3367] 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3385] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3403] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3421] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3439] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3475] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3493] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3511] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3529] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3547] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3565] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3583] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3619] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3637] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3655] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3673] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3691] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3709] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3727] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3745] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3763] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3781] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3799] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3817] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3835] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3853] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3871] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3907] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3925] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3943] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3961] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3979] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3997] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4015] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4033] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4051] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4069] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4087] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4105] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4123] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4141] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4159] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4177] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4195] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4213] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4231] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4249] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4267] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4285] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4303] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4321] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4339] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4357] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4375] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4393] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4411] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4429] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4447] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4465] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4483] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4501] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4537] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4555] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4573] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4591] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4609] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4627] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4645] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4663] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4681] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4699] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4717] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4735] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4753] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4771] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4789] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4807] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4825] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4843] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4861] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4879] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4897] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4915] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4933] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4951] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4969] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4987] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5005] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5023] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5041] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5059] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5077] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5095] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5113] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5131] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5167] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5185] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5203] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5221] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5239] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5257] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5275] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5293] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5311] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5329] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5347] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5365] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5383] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5401] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5419] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5437] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5455] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5473] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5491] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5509] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5527] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5545] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5563] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5581] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5599] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5617] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5635] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5653] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5671] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5689] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5707] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5725] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5743] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5761] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5779] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5797] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5833] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5851] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5869] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5887] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5905] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5923] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5941] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5959] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5977] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5995] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6013] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6031] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6049] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6067] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6085] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6103] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6121] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6139] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6157] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6175] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6193] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6211] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6229] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6247] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6265] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6283] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6301] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6319] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6337] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6355] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6373] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6391] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6409] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6427] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6463] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6481] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6499] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6517] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6535] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6553] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6571] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6589] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6607] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6625] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6643] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6661] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6679] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6697] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6715] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6733] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6751] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6769] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6787] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6805] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6823] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6841] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6859] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6877] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6895] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6913] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6931] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6949] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6967] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6985] 1 1 1 1 1
##
## $centralization
## [1] 0.04544153
##
## $theoretical_max
## [1] 97664288The in-degree is 0, as the permalinks are never sending out responses, so authors never get anything back. Our overall centralization is 0.05, so it is a sparse network.
Let’s look at our other basic measures of the network, starting with density, reciprocity, and transitivity:
Calculate densities
## [1] 0.0001431025## [1] 0.0001431025## [1] 0## [1] 0The edge density and graph density are both 0.001, and the reciprocity and transitivity are both 0. These numbers reflect the sparcity of the network and the lack of reciprocal and transitive ties due to the data structure’s reliance on permalinks rather than authors to connect comments into threads.
Now let’s look at the diameter and mean distance measures of the network:
## [1] 36## [1] 10.25006The diameter of 36 reflects the breadth of conversations across these subreddits, and the mean distance around 10 means that there are some authors who post in multiple subreddits and therefore allow some contact between authors in different threads. However, there is still a decent amount of separation between authors if they need over 10 ties to reach one another on average, which is larger than than the general rule of thumb of roughly 6 degrees of separation between individuals on most social media sites.
Examining the networks of each subreddit
Because the combined data of all 7 subreddits generated a network that had an unclear structure, let’s generate network plots for each subreddit on their own to see if there are any visible differences between them.
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These subreddit networks appear generally similar to each other, with the exception of the Bollywood subreddit, which has more centralized threads. To see how different it is, let’s compare it to one of the more generally representative networks from the Movies subreddit.
Let’s see just those two graph side by side:
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Do the size, centrality, density, and distance measures reflect the visual differences between these two networks? Let’s find out.
First, let’s look at the size of our subreddit networks. First for the Bollywood subreddit:
## [1] 1000And for the Movies Subreddit:
## [1] 998Both networks have about 1000 posts, so they are of equivalent size. Next, let’s look at the centrality degree for all of the Bollywood subreddit:
## $res
## [1] 159 2 2 5 3 2 3 15 2 2 2 2 2 2 2 2 2 2
## [19] 2 3 2 2 4 2 2 9 2 2 4 2 2 6 6 2 3 3
## [37] 2 2 2 2 3 3 2 5 2 2 51 2 2 4 2 6 3 2
## [55] 2 2 2 2 4 2 3 2 2 3 2 5 3 2 2 3 2 2
## [73] 2 2 2 2 3 2 2 2 2 7 2 3 2 4 2 2 2 2
## [91] 2 3 2 2 3 3 3 3 2 2 2 3 2 19 2 3 5 2
## [109] 6 2 2 2 2 2 8 34 2 2 2 3 3 2 3 2 4 5
## [127] 2 3 2 2 2 2 3 2 5 2 4 2 2 29 2 2 2 2
## [145] 2 6 2 2 3 4 3 3 3 2 2 4 2 3 2 13 3 3
## [163] 2 2 2 6 3 2 2 2 2 2 3 2 4 2 2 11 2 11
## [181] 4 2 2 7 4 2 3 2 2 2 7 2 3 2 2 4 4 2
## [199] 4 2 2 2 2 4 2 2 3 2 4 2 2 2 2 2 2 4
## [217] 3 3 2 2 2 2 2 19 2 2 3 3 2 2 6 2 8 7
## [235] 4 2 2 2 4 15 2 4 2 3 3 4 3 8 2 3 2 3
## [253] 2 2 2 2 2 2 2 2 2 2 2 2 13 2 12 7 11 2
## [271] 2 2 2 2 2 3 2 2 2 2 5 2 10 2 2 2 2 2
## [289] 2 2 2 2 2 3 2 2 2 2 10 2 4 2 2 3 2 2
## [307] 4 4 4 6 3 2 2 2 8 3 2 2 2 2 3 2 2 2
## [325] 2 2 2 2 2 3 2 2 4 2 2 2 3 3 2 2 2 2
## [343] 2 2 2 4 2 2 4 2 3 4 2 3 2 3 4 2 2 2
## [361] 5 2 2 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1
## [379] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [397] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [415] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [433] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [451] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [469] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [487] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [505] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [523] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [541] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [559] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [577] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [595] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [613] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [631] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [649] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [685] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [703] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [721] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [739] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [757] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [775] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [793] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [811] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [829] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [847] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [865] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [883] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [901] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [919] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [937] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [955] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [973] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [991] 1 1 1 1 1 1 1 1 1 1
##
## $centralization
## [1] 0.07865724
##
## $theoretical_max
## [1] 1996002The centrality degree of the Bollywood subreddit is 0.079, which is higher than our combined network’s centrality degree of 0.045. This higher centrality is reflected in the plot we saw above.
Now let’s see the centrality degree for all of the Movies subreddit:
## $res
## [1] 72 2 2 3 2 3 2 3 2 2 2 2 2 2 3 3 2 2 2 5 2 2 2 2 2
## [26] 2 2 2 3 2 2 2 2 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2
## [51] 2 2 2 2 2 2 4 2 3 3 2 3 2 2 2 2 2 3 2 4 2 3 2 2 2
## [76] 5 7 3 2 2 2 2 2 3 2 2 2 2 2 2 7 2 3 2 2 2 2 2 2 3
## [101] 3 2 2 2 2 3 2 3 2 2 2 2 2 2 2 2 6 2 2 2 2 6 4 2 2
## [126] 2 2 4 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2 3 2 2 2
## [151] 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 3 2 2 3 2 4 3 2 2 2
## [176] 2 3 2 2 2 2 2 2 2 2 3 2 4 2 2 2 3 2 7 2 2 2 2 2 2
## [201] 2 4 3 2 5 3 2 2 2 3 2 3 2 2 2 3 3 2 2 2 2 2 2 2 4
## [226] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [251] 2 3 2 2 2 2 2 3 2 2 4 2 2 2 2 3 2 2 2 5 2 2 2 2 2
## [276] 2 2 2 2 12 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [301] 2 3 2 9 3 2 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2
## [326] 3 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2 2 2 3 2
## [351] 2 2 2 3 2 2 2 2 2 2 2 2 2 7 2 2 4 2 2 2 2 2 2 2 2
## [376] 3 2 4 2 2 2 2 2 2 2 2 2 3 3 5 2 5 2 2 2 3 2 2 2 2
## [401] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 2 2 2 2 3
## [426] 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 4 2 2 2 2 2 3
## [451] 2 2 4 3 2 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 6 2 2 2 4
## [476] 2 2 2 2 4 3 2 2 7 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2
## [501] 2 2 2 3 2 2 2 2 2 2 2 2 3 4 2 4 2 2 4 2 2 2 2 2 2
## [526] 2 2 2 2 2 2 2 2 3 2 3 2 2 3 2 5 2 3 2 2 2 2 2 2 2
## [551] 2 3 2 3 2 4 2 3 4 2 18 2 3 2 2 2 2 2 2 2 2 2 6 2 2
## [576] 2 2 2 2 2 4 3 2 3 2 2 2 2 2 2 2 2 4 12 21 2 2 2 2 10
## [601] 2 2 2 2 2 2 2 2 2 2 2 4 3 2 2 2 2 2 4 2 2 3 2 2 2
## [626] 5 3 2 3 2 2 2 3 8 3 2 8 3 2 2 2 2 2 2 1 1 1 1 1 1
## [651] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [676] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [701] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [726] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [751] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [776] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [801] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [826] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [851] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [876] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [901] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [951] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [976] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## $centralization
## [1] 0.03514053
##
## $theoretical_max
## [1] 1988018This centrality degree for the Movies subreddit of 0.035 is lower than the centrality degree of 0.045 for our combined network. Because the Movies subreddit network looks broadly representative of the majority of the subreddit networks, it is likely that the greater centrality degree of the Bollywood subreddit raises the centrality degree of the combined network above the typical value for the non-Bollywood subreddits, which show lower centrality.
What about our edge and graph densities? Again, let’s look at the Bollywood subreddit first:
## [1] 0.001001001## [1] 0.001001001And the edge and graph densities for the Movies subreddit:
## [1] 0.001003009## [1] 0.001003009These densities are nearly identical, showing that the networks do share common discussion dynamics, which is not surprising because they are both happening on the same site (reddit.com).
Finally, let’s compare the diameter and distance measures for the two networks. First, for the Bollywood subreddit:
## [1] 25## [1] 9.59684Now for the Movies subreddit:
## [1] 48## [1] 12.23142The diameter of the Bollywood network is only ~50% as big as the diameter the Movies network, once again reflecting the denser discussion ties of that subreddit, and likely the smaller number of participants. As would expect, there is a ~25% shorter mean distance between participants in the Bollywood network than in the Movies network.
Conclusion and Discussion of Limitations
The many discussion groups of the social media site Reddit, known as “subreddits”, provide a very large and diverse set of communities for social network analysis. For this project, seven active subreddits related to movies were studied, and most share a common general network structure. The most interesting result of this analysis was the unique structure of the subreddit on Bollywood movies, which was much more centralized than the other subreddits, even when compared against the other genre-specific subreddits on documentaries, classic films, and horror movies.
However, it must be acknowledged that this analysis is hampered by the missing data generated by the data scraping pool, which did not provide initial authors for all threads. This detail was not noticed until late in the process, and the workaround of using permalinks as a recipient in the edgelist meant that reciprocity and transitivity measures could not be calculated. Future studies could reduce the dataset down only to discussions where the original post submitter is known; however, this would reduce the amount of available data. Alternatively, there may be other tools that can capture more complete information on Reddit posts, but this data search capability is not made available by Reddit and may need to be made individually.
That is, unless I messed up on the edgelist creation, which is entirely possible since it was the first time I’ve ever tried to do it!