| Gabriel |
And so we modern people take for granted that we both produce and consume through markets. The idea that we might acquire groceries because the butcher, the baker, and the brewer owe us favors rather than because we hand them cash or a Visa card seems primitive. Nonetheless, there are circumstances where we modern westerners consider prestations more appropriate than purchases. This preference extends well beyond obvious matters of intimacy like sex and Christmas presents and even reaches into business interactions.
Responses from Mike Munger, Alan Fiske, and Alex Tabarrok to follow.
| Gabriel |
Rod Dreher at The American Conservative has a post on people invoking the concept of “social construction” with his lead example being a speech and debate team that always changes the subject to a critical race theory rant about the conventions of debate itself, even if the pre-specified debate topic is about national service or green energy or whatever. The judge then awards the match to this non sequitur, invoking “social constructionism” to explain himself.
I can get angry about this on a whole other level than Dreher does, precisely because I think social construction is a valuable concept. And I really do take the concept seriously. My PhD training is as a neo-institutionalist (ie, how organizational practices are socially constructed), I have an ASR on market information regimes (ie, how socially constructed market data shapes market behavior), and my current project is on relational work (ie, how exchange is socially constructed as market or social). I also advise grad students on these sorts of topics. So it’s not like I’m some angry epistemological realist who goes around giving swirlies to phenomenologists.
Social construction is a really useful concept, but unfortunately, this really important concept has the misfortune of being popular with idiots who don’t really understand it. When this sort of person says “x is socially constructed” the implication is “therefore we can ignore x.” When I lecture on social constructionism I ridicule this sort of thing as “ruby slippers” social constructionism, as if your sociology professor tells you “why Dorothy, you’ve had the power to solve inequality all along, just click your heels three times and say ‘race is a social construct,’ ‘race is a social construct,’ ‘race is a social construct.'” If you really grok social constructionism, the appropriate reaction to somebody invoking the concept in almost any practical context is to shrug and say “your point being?” If you actually read Berger and Luckmann rather than just get the gist of it from some guy with whom you are smoking weed, you’ll see that the key aspects of social constructionism are intersubjectivity and institutions. That is social construction is important because social interaction is premised on shared conventions and becomes deeply codified to the extent that for most purposes it might as well be objective.
Suppose you had two contractors bidding on remodeling your kitchen. One of them says that it will be done in X days, involving Y materials, and cost you $Z. The other gives you a fascinating (but at times dubious) lecture about whether time exists in the abstract or only relative to perception, the ugly history of exploitation in the formica industry, and the chartalist theory of money. You then go back to the first contractor, who is bewildered and has no rebuttal to the second contractor’s very, um, creative arguments. You would have to be an idiot to award the bid to the second contractor, even if you think they are right about everything they said. As it happens, I actually believe that time, kitchen materials, and money are all socially constructed. It is also true that kitchen remodeling is also a social construct and one of the conventions of that particular social construct is that you talk about things like time, material, and price rather than offer a critical perspective on the same.
| Gabriel |
This morning Governor Chris Christie endorsed Donald Trump for president. There was widespread speculation that this reflected Christie hoping for an appointment as Attorney General in the event of a Trump victory. This was met with widespread disgust from mainstream conservative intellectuals, all of whom despise Trump (and immediately prior to the endorsement were delighting in Rubio having learned to fight Trump at his own insult comic game). Over on Twitter, Josh Barro observed that it is precisely Trump’s outsider nature that makes endorsing him attractive for an ambitious Republican politician.
This struck me as very astute and reminded me of Gould’s 2002 AJS on The Origins of Status Hierarchies. This model starts with a cumulative advantage model for status. The trick with cumulative advantage models though is to avoid their natural tendency towards absolute inequality and so the models always have some kind of braking mechanism so the histogram ends up as a power-law, not a step function. For instance, Rosen 1981 uses heterogeneity of taste and diminishing marginal returns to avoid what would otherwise be the implication of his model of exactly one celebrity achieving universal acclaim. Anyway, the point is that cumulative advantage models need a brake, and Gould’s brake is reciprocity. Gould observes that attention and resources are finite and so when someone has many followers, they lose the ability to reciprocate with them. To the extent that followers are attentive not only to the status of a patron, but the attention and resources the patron reciprocates, then their high numbers of followers will swamp the ability of high status patrons to reciprocate and so inhibit their ability to attract new followers. For instance, a grad student might rationally prefer to work with an associate professor who has only a few advisees and so can spend several hours a week with each of them than with a Nobel Laureate who has so many advisees he doesn’t recognize some of them in the hallway.
In this sense, Rubio as the clear favorite of the party establishment has already recruited great masses of political talent. Should Rubio win in November, he will have an embarrassment of riches in terms of followers with whom to fill cabinet positions and other high-ranking political roles. That is to say, Rubio’s ability to reciprocate the support of his followers is swamped by the great number of followers he has acquired. (I’m talking about followers among the sorts of people likely to be appointed to administration positions, I’ll get to voters later). This then makes some potential followers decide to affiliate with a patron who is not too busy for them, and hence Chris Christie is hoping to spend the next eight years building RICO cases against people who use the term “short-fingered vulgarian.”
But, there’s a problem with this, which is that status itself provides resources, especially in a system where power is not continuous but winner-take-all. (The discontinuity is really important, as Schilke and I argued recently). In this sense, it shouldn’t matter that a candidate with few endorsements has the fewest supporters competing for patronage because that candidate would lose and so not have patronage to allocate. That would be true if the political science model nicknamed “the party decides”(which we can generalize as the endogeneity of status competition) were true. But if that model were true, we would be seeing Rubio (who recruited the most intellectuals) or Jeb! (who raised the most money) as the clear front-runner and that is anything but the case since the GOP primary this cycle has been consistently dominated by outsiders (Trump, briefly Carson, and even Cruz, who is a senator but a notably un-collegial one).
This then suggests that we have to recognize that power, including the ability to allocate resources to followers, is not necessarily a function of how many followers one has. In ordinary times it might be, especially in the Republican party which normally follows the party decides model. However in this year it is clear that popularity in opinion polls and primaries/caucuses has no (positive) correlation with establishment support. This may be because Trump, like Lenin, is a figure of such immense charisma that he can defy the models. Or it may be that the base is revolting over a substantive issue like immigration. Or maybe the support of neo-Nazis with a bizarre interest in anime and the Frankfurt school is the secret sauce. Whatever the exact nature of why the party decides model is breaking, the fact is that it is. The Republican primary reminds me of Bourdieu’s model of a field of mass cultural production and a restricted field of production. Rubio is clearly dominating in the restricted field of elite conservative opinion, but that does him very little good considering how effective Trump is at the mass field. If we view the competition for endorsements not as an isolated system, but one that is loosely coupled to an adjacent system of competition for voters, then the status competition for endorsements is no longer entirely endogenous but there is a source of exogenous power shaping it. (In the Gould model this would be subsumed as part of Q_j). Hence Trump’s great popularity with voters despite his great unpopularity with party elites makes him more attractive than he would otherwise be to party elites who will break ranks and affiliate with the demagogue.
In Trump’s case, his fame, wit, and shamelessness have gained him the support of voters and this has disrupted the otherwise endogenous system of endorsements, however the model could generalize to any source of power outside of the endogenous process of consensus building within party elites. A very similar model would apply to those political actors who welcome a foreign invader as supporters in domestic disputes they would otherwise lose. Americans take for granted that the opposition party will be a loyal opposition and so we abide by the maxim that “politics ends at the water’s edge,” which is why periods like the Second Red Scare (or from the other perspective, the Popular Front that preceded it) seem so anomalous. However for centuries, machinations to set yourself up as a client-state after relying on imperial powers to depose the current batch of elites is most of what politics was. In such a scenario, a political actor who lacks much power within the internal dynamics of oligarchy could still acquire followers if they seemed to be favored by the forces massing across the border. So we might expect a lot of ambitious mitteleuropean politicians to affiliate with heretofore minor fascist parties c 1938, or with heretofore minor communist parties c 1943.
| Gabriel |
For each quote, guess the source: a classic of gift exchange or a Los Angeles Times article about deposed Sheriff and soon to be plea bargainee, Lee Baca. Highlight the text to see the answers and score your quiz!
“Until he has given back, the receiver is ‘obliged,’ expected to show his gratitude towards his benefactor or at least to show regard for him, go easy on him, pull his punches…” (Bourdieu Logic of Practice)
“The etiquette of the feast, of the gift that one receives with dignity, but is not solicited, is extremely marked among these tribes.” (Mauss The Gift)
“I don’t solicit any gifts. I’ve never asked for a gift.… People just do it for me.” (Los Angeles Times)
“When you’re taking gifts from strangers, there’s only one reason. They only give gifts because they want something.” (Los Angeles Times)
“These, however, are but the outward signs of kindness, not the kindnesses themselves.” (Seneca Benefits)
“What they’re expressing is appreciation for the respectful way we do business.” (Los Angeles Times)
“No one is really unaware of the logic of exchange … but no one fails to comply with the rules of the game, which is to act as if one did not know the rule.” (Bourdieu Pascalian Meditations)
“Nobody is free to refuse the present that is offered.” (Mauss The Gift)
“My life would be much easier if people did not give me gifts.” (Los Angeles Times)
| Gabriel |
As long-time readers will remember, I have been collecting Twitter with the R library(twitteR). Unfortunately that workflow has proven to be buggy, mostly for reasons having to do with authentication. As such I decided to learn Python and migrate my project to the Twython module. Overall, I’ve been very impressed by the language and the module. I haven’t had any dependency problems and authentication works pretty smoothly. On the other hand, it requires a lot more manual coding to get around rate limits than does twitteR and this is a big part of what my scripts are doing.
I’ll let you follow the standard instructions for installing Python 3 and the Twython module before showing you my workflow. Note that all of my code was run on Python 3.5.1 and OSX 10.9. You want to use Python 3, not Python 2 as tweets are UTF-8. If you’re a Mac person, OSX comes with 2.7 but you will need to install Python3. For the same reason, use Stata 14 for tweets.
One tip on installation, pip tends to default to 2.7 so use this syntax in bash.
python3 -m pip install twython
I use three py scripts, one to write Twython queries to disk, one to query information about a set of Twitter users, and one to query tweets from a particular user. Note that the query scripts can be slow to execute, which is deliberate as otherwise you end up hitting rate limits. (Twitter’s API allows fifteen queries per fifteen minutes). I call the two query scripts from bash with argument passing. The disk writing script is called by the query scripts and doesn’t require user intervention, though you do need to be sure Python knows where to find it (usually by keeping it in the current working directory). Note that you will need to adjust things like file paths and authentication keys. (When accessing Twitter through scripts instead of your phone, you don’t use usernames and passwords but keys and secrets, you can generate the keys by registering an application).
I am discussing this script first even though it is not directly called by the user because it is the most natural place to discuss Twython’s somewhat complicated data structure. A Twython data object is a list of dictionaries. (I adapted this script for exporting lists of dictionaries). You can get a pretty good feel for what these objects look like by using type() and the pprint module. In this sample code, I explore a data object created by infoquery.py.
type(users) #shows that users is a list type(users) #shows that each element of users is a dictionary #the objects are a bunch of brackets and commas, use pprint to make a dictionary (sub)object human-readable with whitespace import pprint pp=pprint.PrettyPrinter(indent=4) pp.pprint(users) pp.pprint(users['status']) #you can also zoom in on daughter objects, in this case the user's most recent tweet object. Note that this tweet is a sub-object within the user object, but may itself have sub-objects
As you can see if you use the pprint command, some of the dictionary values are themselves dictionaries. It’s a real fleas upon fleas kind of deal. In the datacollection.py script I pull some of these objects out and delete others for the “clean” version of the data. Also note that tw2csv defaults to writing these second-level fields as one first-level field with escaped internal delimiters. So if you open a file in Excel, some of the cells will be really long and have a lot of commas in them. While Excel automatically parses the escaped commas correctly, Stata assumes you don’t want them escaped unless you use this command:
import delimited "foo.csv", delimiter(comma) bindquote(strict) varnames(1) asdouble encoding(UTF-8) clear
Another tricky thing about Twython data is there can be variable number of dictionary entries (ie, some fields are missing from some cases). For instance, if a tweet is not a retweet it will be missing the “retweeted_status” dictionary within a dictionary. This was the biggest problem with reusing the Stack Overflow code and required adapting another piece of code for getting the union set of dictionary keys. Note this will give you all the keys used in any entry from the current query, but not those found uniquely in past or future queries. Likewise, Python sorts field order randomly. For these two reasons, I hard-coded tw2csv as overwrite, not append, and build in a timestamp to the query scripts. If you tweak the code to append, you will run into problems with the fields not lining up.
Anyway, here’s the actual tw2csv code.
#tw2csv.py def tw2csv(twdata,csvfile_out): import csv import functools allkey = functools.reduce(lambda x, y: x.union(y.keys()), twdata, set()) with open(csvfile_out,'wt') as output_file: dict_writer=csv.DictWriter(output_file,allkey) dict_writer.writeheader() dict_writer.writerows(twdata)
One of the queries I like to run is getting basic information like date created, description, and follower counts. Basically, all the stuff that shows up on a user’s profile page. The Twitter API allows you to do this for 100 users simultaneously and I do this with the infoquery.py script. It assumes that your list of target users is stored in a text file, but there’s a commented out line that lets you hard code the users, which may be easier if you’re doing it interactively. Likewise, it’s designed to only query 100 users at a time, but there’s a commented out line that’s much simpler in interactive use if you’re only querying a few users.
You can call it from the command line and it takes as an argument the location of the input file. I hard-coded the location of the output. Note the “3” in the command-line call is important as operating systems like OSX default to calling Python 2.7.
python3 infoquery.py list.txt
#infoquery.py from twython import Twython import sys import time from math import ceil import tw2csv #custom module parentpath='/Users/rossman/Documents/twittertrucks/infoquery_py' targetlist=sys.argv #text file listing feeds to query, one per line. full path ok. today = time.strftime("%Y%m%d") csvfilepath_info=parentpath+'/info_'+today+'.csv' #authenticate APP_KEY='' #25 alphanumeric characters APP_SECRET='' #50 alphanumeric characters twitter=Twython(APP_KEY,APP_SECRET,oauth_version=2) #simple authentication object ACCESS_TOKEN=twitter.obtain_access_token() twitter=Twython(APP_KEY,access_token=ACCESS_TOKEN) handles = [line.rstrip() for line in open(targetlist)] #read from text file given as cmd-line argument #handles=("gabrielrossman,sociologicalsci,twitter") #alternately, hard-code the list of handles #API allows 100 users per query. Cycle through, 100 at a time #users = twitter.lookup_user(screen_name=handles) #this one line is all you need if len(handles) < 100 users= #initialize data object hl=len(handles) cycles=ceil(hl/100) #unlike a get_user_timeline query, there is no need to cap total cycles for i in range(0, cycles): ## iterate through all tweets up to max of 3200 h=handles[0:100] del handles[0:100] incremental = twitter.lookup_user(screen_name=h) users.extend(incremental) time.sleep(90) ## 90 second rest between api calls. The API allows 15 calls per 15 minutes so this is conservative tw2csv.tw2csv(users,csvfilepath_info)
This last script collects tweets for a specified user. The tricky thing about this code is that the Twitter API allows you to query the last 3200 tweets per user, but only 200 at a time, so you have to cycle over them. moreover, you have to build in a delay so you don’t get rate-limited. I adapted the script from this code but made some tweaks.
One change I made was to only scrape as deep as necessary for any given user. For instance, as of this writing, @SociologicalSci has 1192 tweets, so it cycles six times, but if you run it in a few weeks @SociologicalSci would have over 1200 and so it would run at least seven cycles. This change makes the script run faster, but ultimately gets you to the same place.
The other change I made is that I save two versions of the file, one as is and the other that pulls out some objects from the subdictionaries and deletes the rest. If for some reason you don’t care about retweet count but are very interested in retweeting user’s profile background color, go ahead and modify the code. See above for tips on exploring the data structure interactively so you can see what there is to choose from.
As above, you’ll need to register as an application and supply a key and secret.
You call it from bash with the target screenname as an argument.
python3 datacollection.py sociologicalsci
#datacollection.py from twython import Twython import sys import time import simplejson from math import ceil import tw2csv #custom module parentpath='/Users/rossman/Documents/twittertrucks/feeds_py' handle=sys.argv #takes target twitter screenname as command-line argument today = time.strftime("%Y%m%d") csvfilepath=parentpath+'/'+handle+'_'+today+'.csv' csvfilepath_clean=parentpath+'/'+handle+'_'+today+'_clean.csv' #authenticate APP_KEY='' #25 alphanumeric characters APP_SECRET='' #50 alphanumeric characters twitter=Twython(APP_KEY,APP_SECRET,oauth_version=2) #simple authentication object ACCESS_TOKEN=twitter.obtain_access_token() twitter=Twython(APP_KEY,access_token=ACCESS_TOKEN) #adapted from http://www.craigaddyman.com/mining-all-tweets-with-python/ #user_timeline=twitter.get_user_timeline(screen_name=handle,count=200) #if doing 200 or less, just do this one line user_timeline=twitter.get_user_timeline(screen_name=handle,count=1) #get most recent tweet lis=user_timeline['id']-1 #tweet id # for most recent tweet #only query as deep as necessary tweetsum= user_timeline['user']['statuses_count'] cycles=ceil(tweetsum / 200) if cycles>16: cycles=16 #API only allows depth of 3200 so no point trying deeper than 200*16 time.sleep(60) for i in range(0, cycles): ## iterate through all tweets up to max of 3200 incremental = twitter.get_user_timeline(screen_name=handle, count=200, include_retweets=True, max_id=lis) user_timeline.extend(incremental) lis=user_timeline[-1]['id']-1 time.sleep(90) ## 90 second rest between api calls. The API allows 15 calls per 15 minutes so this is conservative tw2csv.tw2csv(user_timeline,csvfilepath) #clean the file and save it for i, val in enumerate(user_timeline): user_timeline[i]['user_screen_name']=user_timeline[i]['user']['screen_name'] user_timeline[i]['user_followers_count']=user_timeline[i]['user']['followers_count'] user_timeline[i]['user_id']=user_timeline[i]['user']['id'] user_timeline[i]['user_created_at']=user_timeline[i]['user']['created_at'] if 'retweeted_status' in user_timeline[i].keys(): user_timeline[i]['rt_count'] = user_timeline[i]['retweeted_status']['retweet_count'] user_timeline[i]['qt_id'] = user_timeline[i]['retweeted_status']['id'] user_timeline[i]['rt_created'] = user_timeline[i]['retweeted_status']['created_at'] user_timeline[i]['rt_user_screenname'] = user_timeline[i]['retweeted_status']['user']['name'] user_timeline[i]['rt_user_id'] = user_timeline[i]['retweeted_status']['user']['id'] user_timeline[i]['rt_user_followers'] = user_timeline[i]['retweeted_status']['user']['followers_count'] del user_timeline[i]['retweeted_status'] if 'quoted_status' in user_timeline[i].keys(): user_timeline[i]['qt_created'] = user_timeline[i]['quoted_status']['created_at'] user_timeline[i]['qt_id'] = user_timeline[i]['quoted_status']['id'] user_timeline[i]['qt_text'] = user_timeline[i]['quoted_status']['text'] user_timeline[i]['qt_user_screenname'] = user_timeline[i]['quoted_status']['user']['name'] user_timeline[i]['qt_user_id'] = user_timeline[i]['quoted_status']['user']['id'] user_timeline[i]['qt_user_followers'] = user_timeline[i]['quoted_status']['user']['followers_count'] del user_timeline[i]['quoted_status'] if user_timeline[i]['entities']['urls']: #list for j, val in enumerate(user_timeline[i]['entities']['urls']): urlj='url_'+str(j) user_timeline[i][urlj]=user_timeline[i]['entities']['urls'][j]['expanded_url'] if user_timeline[i]['entities']['user_mentions']: #list for j, val in enumerate(user_timeline[i]['entities']['user_mentions']): mentionj='mention_'+str(j) user_timeline[i][mentionj] = user_timeline[i]['entities']['user_mentions'][j]['screen_name'] if user_timeline[i]['entities']['hashtags']: #list for j, val in enumerate(user_timeline[i]['entities']['hashtags']): hashtagj='hashtag_'+str(j) user_timeline[i][hashtagj] = user_timeline[i]['entities']['hashtags'][j]['text'] if user_timeline[i]['coordinates'] is not None: #NoneType or Dict user_timeline[i]['coord_long'] = user_timeline[i]['coordinates']['coordinates'] user_timeline[i]['coord_lat'] = user_timeline[i]['coordinates']['coordinates'] del user_timeline[i]['coordinates'] del user_timeline[i]['user'] del user_timeline[i]['entities'] if 'place' in user_timeline[i].keys(): #NoneType or Dict del user_timeline[i]['place'] if 'extended_entities' in user_timeline[i].keys(): del user_timeline[i]['extended_entities'] if 'geo' in user_timeline[i].keys(): del user_timeline[i]['geo'] tw2csv.tw2csv(user_timeline,csvfilepath_clean)
| Gabriel |
There has been a tremendous amount of hype over the last few years about universal pre-K as a magic bullet to solve all social problems. We see a lot of talk of return on investment at rates usually only promised by prosperity gospel preachers and Ponzi schemes. Unfortunately, two recent large-scale studies, one in Quebec and one in Tennessee, showed small negative effects for pre-K. An article writing up the Tennessee study in New York advises fear not, for:
These are all good studies, and they raise important questions. But none of them is an indictment of preschool, exactly, so much as an indictment of particular approaches to it. How do we know that? Two landmark studies, first published in 1993 and 2008, demonstrate definitively that, if done right, state-sponsored pre-K can have profound, lasting, and positive effects — on individuals and on a community.
It then goes on to explain that the Perry and Abecedarian projects were studies involving 123 and 100 people respectively, had marvelous outcomes, and were play rather than drill oriented.
The phrase “demonstrate definitively” is the kind of phrase you have to very careful with and it just looks silly to say that this definitive knowledge comes from two studies with sample size of about a hundred. Tiny studies with absurdly large effects sizes are exactly where you would expect to find publication bias. Indeed, this is almost inevitable when the sample sizes are so underpowered that the only way to get β/se>1.96 is for β to be implausibly large. (As Jeremy Freese observed, this is among the dozen or so major problems with the PNAS himmicane study).
The standard way to detect publication bias is through a meta-analysis showing that small studies have big effects and big studies have small effects. For instance, this is what Card and Krueger showed in a meta-analysis of the minimum wage literature which demonstrated that their previous paper on PA/NJ was only an outlier when you didn’t account for publication bias. Similarly, in a 2013 JEP, Duncan and Magnuson do a meta-analysis of the pre-K literature. Their visualization in figure 2 emphasizes the declining effects sizes over time, but you can also see that the large studies (shown as large circles) generally have much smaller β than the small studies (shown as small circles). If we added the Tennessee and Quebec studies to this plot they would be large circles on the right slightly below the x-axis. That is to say, they would fall right on the regression line and might even pull it down further.
This is what publication bias looks like: old small studies have big effects and new large studies have small effects.
I suppose it’s possible that the reason Perry and Abecedarian showed big results is because the programs were better implemented than those in the newer studies, but this is not “demonstrated definitively” and given the strong evidence that it’s all publication bias, let’s tentatively assume that if something’s too good to be true (such as that a few hours a week can almost deterministically make kids stay in school, earn a solid living, and stay out of jail), then it ain’t.
| Gabriel |
Today the Economist posted a graph showing the patrons of factions in various civil wars in the Middle East. The point of the graph is that the alliances don’t neatly follow balance theory, since it is in fact sometimes the case that the friend of my enemy is my friend, which is a classic balance theory fail. As such, I thought it would be fun to run a Spinglass model on the graph. Note that I could only do edges, not arcs, so I only included positive ties, not hostility ties. One implication of this is ISIS drops out as it (currently) lacks state patronage.
Here’s the output. The second column is community and the third is betweenness.
> s Graph community structure calculated with the spinglass algorithm Number of communities: 4 Modularity: 0.4936224 Membership vector:  4 4 3 2 2 2 4 3 4 3 1 4 1 3 3 4 2 4 2 > output b [1,] "bahrain_etc" "4" "0" [2,] "egypt_gov" "4" "9.16666666666667" [3,] "egypt_mb" "3" "1.06666666666667" [4,] "iran" "2" "47.5" [5,] "iraq_gov" "2" "26" [6,] "iraq_kurd" "2" "26" [7,] "jordan" "4" "6.73333333333333" [8,] "libya_dawn" "3" "1.06666666666667" [9,] "libya_dignity" "4" "0.333333333333333" [10,] "qatar" "3" "27.5333333333333" [11,] "russia" "1" "0" [12,] "saudi" "4" "4" [13,] "syria_gov" "1" "17" [14,] "syria_misc" "3" "31.0333333333333" [15,] "turkey" "3" "6.83333333333333" [16,] "uae" "4" "4" [17,] "usa" "2" "74.4" [18,] "yemen_gov" "4" "74.3333333333333" [19,] "yemen_houthi" "2" "0"
So it looks like we’re in community 2, which is basically Iran and its clients, though in fairness we also have high betweenness as we connect community 2 (Greater Iran), community 3 (the pro Muslim Brotherhood Sunni states), and community 4 (the pro Egyptian government Sunni states). This is consistent with the “offshore balancing” model of Obama era MENA policy.
Here’s the code:
library("igraph") setwd('~/Documents/codeandculture') mena <- read.graph('mena.net',format="pajek") la = layout.fruchterman.reingold(mena) V(mena)$label <- V(mena)$id #attaches labels plot.igraph(mena, layout=la, vertex.size=1, vertex.label.cex=0.5, vertex.label.color="darkred", vertex.label.font=2, vertex.color="white", vertex.frame.color="NA", edge.color="gray70", edge.arrow.size=0.5, margin=0) s <- spinglass.community(mena) b <- betweenness(mena, directed=FALSE) output <- cbind(V(mena)$id,s$membership,b) s output
And here’s the data:
*Vertices 19 1 "bahrain_etc" 2 "egypt_gov" 3 "egypt_mb" 4 "iran" 5 "iraq_gov" 6 "iraq_kurd" 7 "jordan" 8 "libya_dawn" 9 "libya_dignity" 10 "qatar" 11 "russia" 12 "saudi" 13 "syria_gov" 14 "syria_misc" 15 "turkey" 16 "uae" 17 "usa" 18 "yemen_gov" 19 "yemen_houthi" *Arcs 1 18 2 9 2 18 4 5 4 6 4 13 4 19 7 2 7 14 7 18 10 3 10 8 10 14 10 18 11 13 12 2 12 9 12 18 15 3 15 8 15 14 16 2 16 9 16 18 17 5 17 6 17 14 17 18