Quick Thoughts on the EO Suspending Immigration

[Edited 17:13 hrs, Jan 29]

In the midst of many other concerns, I wanted to write some thoughts on the recent suspension of the U.S. Refugee Admissions Program before I forgot them. Go to my Twitter page for a number of retweets on the Executive Order, most opposing it (but remember, retweet doesn’t mean approval). My main impressions: whether it’s unconstitutional or not, detaining an octogenarian Iranian couple in wheelchairs, or Iraqis who assisted U.S. troops, just sends a bloody bad message, as one of my Twitter acquaintance put it.

Above all else, I think the order is strange because it actually changes relatively little–so why the hoopla a) about signing it, and b) about opposing it? It’s not like the U.S. government has been welcoming refugees with open arms, mainly because, unlike Europe, we have a 3,000 miles of water separating us from the world’s refugee populations. So, it seems more about sending some kind of signal about…something (update, 1/31: Jake Fuentes speculated intelligently about what that something could be). In the mean time, administrative confusion is ensuring that the only people it’s affecting are decent people trying to go about their business.

David French at The National Review and William A. Jacobson at Legal Insurrection have done a great job actually breaking down the true from the false . French’s article is especially good; LI casts doubt on airport detentions that actually seem to have happened, and gets some other things wrong as well. One further caveat on these articles: you can flippantly talk about the moratorium “only” being X-number of days, but when you’re waiting for relief from starvation, homelessness, or persecution, 3 or 4 months is an awfully long time.

  1. The full text of the executive order can be found at CNN or The New York Times.
  2. For comprehensive statistics, with links, on U.S. immigration, the following articles from the Pew Research Center are absolutely indispensable:
    1. October 5, 2016, “US Admits Record Number of Muslim Refugees in 2016.”
    2. January 27, 2017, “Key Facts About Refugees to the U.S.”
  3. When I said yesterday that “it didn’t come from nowhere,” I was referring to the fact that these seven countries were already on the government’s radar.
    1. There was already a pretty stiff vetting process in place for refugees–you can see the State Department’s page on that,  or the infographic on obamawhitehouse.gov.
    2. In addition, the EO refers to U.S. Code Title 8, Chapter 12, which can be found at Cornell’s LII. The EO essentially works off the framework established by the Obama Administration.
    3. In fact, as Seth J. Frantzman points out with considerable force, nowhere in the EO are most of the seven countries mentioned–that’s because immigration from there was already restricted by the Obama Administration in 2015, and updated in February of 2016 to include Libya, Somalia, and Yemen–using similar language to the EO, I might add.
  4. Also, I just have to say, an infographic from last night claiming that countries where Trump does business will be exempt is click-bait bull, as soon as you realize the pre-existing background of these restrictions.
  5. Despite the rulings in New York and Virginia last night, it is actually not at all clear that this EO was unconstitutional, and I’d be surprised if these rulings are upheld in February. Primarily because the administration likely can demonstrate that it is not a “MuslimBan” (one reason I didn’t use that hashtag yesterday–besides, Syrian Christians are being denied entry anyway). Also that the language for persecuted religious minorities long pre-dates Trump. Some have complained that Middle Eastern Christians have gone to the back of the line under the Obama administration, and that could be true for all I know. However, while ISIS has a special hatred for Christian communities (as do many Christians’ neighbors, when the going gets tough), the majority of ISIS victims are Muslims (the BBC has the best column on this fraught topic).

So, what exactly is supposed to be gained by either the much-publicized signing of this EO, or the furious protests against it?  For instance, these measures almost certainly wouldn’t have prevented the San Bernardino shooting in 2015–Pakistan and Saudi Arabia aren’t on the list. On the other hand, it might have prevented the OSU attack perpetrated by a Somali immigrant. David French points out that, after review, the U.S. refugee cap will be set at 50,000–roughly what it was for the Bush and most of the Obama years. Much as it is a crime these days to “normalize” Trump, the fact is that this EO mostly continues what Obama started. Many things about it are bad, I think: the timing, the tweaks, the delays, and the harassment caused by shoddy implementation, are mostly negatives, or unproved positives, in my book. It was just clarified this morning that it doesn’t apply to green card holders.

So…why?? It could be, as a couple right-wing sites have suggested, that the administration was trolling the media. Or that the media and most academics and public intellectuals, myself included, let themselves/ourselves be trolled. It could also be that this is what the administration feels is the next logical step in national security. But really,  it needs to get its act together if so, because images speak louder than words in the information age.

Continue reading “Quick Thoughts on the EO Suspending Immigration”

How We Use Twitter, Part 2: #notmypresident, January 4, 2017 (plus: #BLMKidnapping)

Donald Trump has controlled the Twitter narrative about himself for the past 48 hours. Proof? Metrics. Here’s “How We Use Twitter, Part 2.” This one is centered on the hashtag #notmypresident, taken over the last 24 and 48 hours.

My initial plan to run a new test on the hashtags #medieval OR #medievaltwitter got deep-sixed yesterday morning when I had to restart my computer and in the process lost the data I had captured with Trendsmap, which I’m trying for a week. The quantity of Tweets fell off sharply, however, when I tried to recapture data in NodeXL (so that both captures would have roughly the same time stamp). The discussion would, therefore, be rather bland. So, with no immediate followup to the #medievaltwitter post in sight, I decided to go for current politics instead. Part 1 of this analysis can be considered a demo and recommendation of Trendsmap. Part 2 dives into data collected from Trendsmap and NodeXL.

Part 1 #notmypresident for the past 24 hours, c. 1730 hours January 4, 2017

Trendsmap is a growing and quite exciting platform for analyzing and visualizing Twitter data in real time. I’ve been testing it out for a few days with their gracious 1-week trial, running various searches on it. So far, I’m very impressed with Trendsmap, and though I can’t afford a subscription now, I am definitely interested in pursuing it later, or recommending it to my school.

In trial mode Trendsmap lets you examine tweets from the past 24 hours. Under analytics, we can see that there were 14, 500 tweets featuring the #notmypresident hashtag, with a 45%-55% female-male split, with peak activity around 8 p.m. on January 3.

When we look at the map, it seems almost all the activity comes from inside the U.S., with a couple exceptions–the UK, Australia, and New Zealand being the main exceptions, with other bursts in Israel, Nigeria, and the western Mediterranean.


Trendsmap lets you zoom in to examine the specific tweets that were the most influential, with interactive map features:

The details of the tweets reveal some interesting features. The second most popular hashtag was #theresistance, which could be further parsed to identify the community that opposes Trump the most (NodeXL’s “word pairs” feature indicates the same thing:).

Back to Trendsmap: New York City, Portland OR, Los Angeles, D.C., and Seattle account for a quarter of the tweets, though we can tell from the heat map that opposition to the incoming administration is either most widespread or most vocal east of the Mississippi. Roughly 44% of the tweets come from iPhones or Android devices, indicating (perhaps) a particular socio-economic background of the tweeters (someone dig up the research and correct me on this…).

Part 2: Specifics, NodeXL, and Analysis

In Trendsmap: When we come to look at the Top Retweets vs the Most Quoted Tweets, an interesting pattern emerges (again, one that NodeXL helps us go into in more detail. The top retweet had nothing to do with any specific Trump action or tweet, but rather was a meme reaction to “when you find out your friend voted Trump.” None of the top retweets came from the PEOTUS himself. However, he snagged four of the top 12 “Most Quoted Tweets,” three of which were retweeted far more than @KingBach’s tweet. The leader was his tweet about the intel briefing being moved to Friday, the second defended Assange, the third announced his press conference, and the fourth talked about Jackie Evanchos’ album sales after announcing she would perform at the inauguration.

When it comes to “Most Replied Tweets,” PEOTUS Trump took all twelve spots for the last 24 hours. The “Most Influencing Tweets” are a mix of tweets seen  in previous categories, but none are Trump’s. Trendsmap calculates “influence” here as accounts with the most followers–in other words, closeness centrality.

In NodeXL: Comparing these results with NodeXL, which for January 3 and 4 pulled 11,272 individuals who tweeted, and 26, 563 connections (again, the graph and workbook can be accessed at the NodeXL Graph Gallery). Note that NodeXL and Trendsmap display data differently, so the individuals often count for more than one tweet.

Here’s the data clustered into groups:

And here’s the data, rearranged according to the Harel-Karem Fast Multiscale layout and with groups in their own boxes:

Without going into the different groups (perhaps a topic for a later date, though politics will have moved on by then), let’s confine ourselves to some visuals and statistics on Trump’s absolute dominance of Twitter.

Not only does NodeXL back up Trendsmap’s overall picture, it becomes clear from the different tests NodeXL allows you to run that Trump’s power on Twitter is far greater than is apparent from Trendsmap’s visuals. To illustrate this visually, here is a full-page image of the network:

And here is what it looks like when you click on the node @realdonaldtrump, which sits at the center of group 1 (dark blue):

Note further that the rest of group 1 forms essentially two rings, one which is retweets, replies, and quotes, and the other which is responding to the first ring.

When we look at the metrics, Trump crushes nearly every category:

This can be parsed further when we look at In-Degree (Out-Degree doesn’t matter, as Trump doesn’t reply to people, but people reply to him):

Betweenness Centrality, a much more potent measure of influence than Closeness Centrality:

Eigenvector Centrality and Page Rank:

So, depressing as this may be, Twitter is dominated by Donald Trump, and the opposition voices that engage with him do next to nothing to counter his influence–in fact they actually boost it. Most of the opposition, from what I can see in these tweets, has no immediate, specific message other than “he’s awful/unqualified/terrible/embarrassment/ridiculous” or some combination thereof. Additionally, if you look at the “Top Retweets” above, most have links of some kind, which require further reading from interested parties. Trump doesn’t have to provide links, and that is an adavantage–if you wanted to be snarky, you could say that to be in opposition to Trump requires more thought…At the very least, I’ve seen many academics acknowlege the limitations of 140 characters while tweeting away happily. Trump is just fine with these 140 characters, because they do deliver a message immediately, with no need for further explanation or links that would require prolonged reading or engagement. And by doing so, he maintains the image of talking directly to the people of the U.S. and the world (though much of the world isn’t on Twitter, or not Tweeting about the U.S.). Basically, as Amanda Hess opined back in February 2016, Trump is actually really good at Twitter, from a rhetorical and political standpoint. Given his reach, the quality of the positions advocated is largely moot–“quantity has a quality all its own” (almost certainly not said by Stalin).

All that being said, however, the counter-narrative is out there, and seems robust. As yet, however, it has not coalesced.


Plus: This evening, the hashtag #BLMKidnapping began trending. I have not had a chance to run a NodeXL analysis of it, but it would probably strain my computer to calculate. As of 12:05 a.m. on January 5, Trendsmap recorded the following:

With this heat signature map:

With these Top Retweets and Most Quoted tweets:

With these Most Replied and Most Influencing Tweets:

I didn’t watch the entire video,  so maybe I missed where BLM is invoked. In any case, it seems the far-right have taken over the Twitterverse on this one. I’ve found Nina Turner   to be invaluable for good counter-narrative on this horrible incident, and Shaun King later this morning should be as well.

How We Use Twitter Part 1: #MedievalTwitter Roundup from December 23, 2016

On December 23, at a time when the President Elect can turn the world on its head with a tweet, or a simple sentence about arms races, I thought I’d see what we medieval folk were talking about on Twitter…

I’ve been playing with the NodeXL network analysis tool for Excel, both to get back into social network analysis and to explore some of the capabilities of the platform. I’d only begun to test it in the aftermath of the Great Crusades Debate of February 2015 (for which I still have all the data, and will be blogging that soon-ish, or finding a place to publish it). Anyway, I wanted to see what kind of discussions we had been having before Christmas, and to discover which conversation topics generated the most interactions. In a sense, then, this can be seen as followup to my earlier post, Don’t Oversell Twitter, this time with some hard data and perhaps a more nuanced interpretation of such data. [Also, please note, before you get all fired up, that all the information captured here is public data.]

Parameters and Terms

The program I like best  for this kind of analysis is NodeXL. NodeXL has several specialties, but among its best features is its ability to analyze Twitter networks.  I decided to take a look at two hashtags, at 1200hrs and 1500hrs on December 23 (I didn’t finish writing this post till today), and see how they traveled about the platform, in terms of interactions.

There are a number of things to remember before diving in. NodeXL doesn’t measure impressions, that is the number of times someone saw the tweet, or engagements, that is the number of times someone interacted with the tweet (these are Twitter’s own definitions). This needs to be remembered when looking at the data captured here on interactions. Relevant terminology, for those not familiar with network analysis lingo:

Vertex: Also known as “nodes,” these are the individual items that make up the network. In this case, the nodes are individuals, or individual Twitter accounts.

Edge: This is the connection between two nodes, or vertices. Most network analysis programs can analyze edges in several ways. They can display edges simply to show that there is a connection, without specifying what kind it is: this is called an “undirected network.” If you change the settings to display who is doing the action to or toward whom, that is a “directed network.”

Directed/Undirected: As above. The graphs here are directed, because I want to see who is performing the action to whom. An arrow to a node indicates that the node has been mentioned, quoted, replied to, or retweeted.

Centrality: For a good overview of “centrality,” see UC Riverside’s page on it, and Peter Hoff’s easy-to-grasp PowerPoint from the University of Washington.

Betweenness Centrality:  This is the first of several common ways of testing how important various nodes are in the network. “Betweenness” indicates how often a node lies on the path to other nodes–in other words, it’s a way of figuring out who controls access, or serves as an access point, for information, and hence who has the most influence in a network.

Closeness Centrality: Closeness indicates how far a vertex (node) is from other nodes. In other words, it measures who knows the most people, or who connects with the most people. It doesn’t tell us how influential that person is–they may just be sociable.

Eigenvector Centrality: Eigenvector is a more discerning way of calculating In/Out Degree (how many links does a node receive or give). Not all nodes are equal–so, if you’re a node, the Eigenvector measurement calculates the relative importance of the nodes that you connect to.

With these terms in mind, let’s look at the data.


The first one is #medievaltwitter itself, as of about 1500 hours, December 23, 2016. Full data, including the worksheets, can be found at the NodeXL Gallery. I used the Harel-Koren Fast Multiscale layout because it shows more clearly the relationship between nodes (vertices) within groups. Note that this search was only for the hashtag #medievaltwitter, and not #medieval (I’ll run a search on both terms in my next post or two, as that will throw up a much richer network). For December 17 to December 23, the coverage allowed by Twitter, 454 users participated in the hashtag. Let’s look at the top five groups:

A couple things stand out from this, at least to me. First, the nodes with the highest “betweenness” centrality–those whose posts got the most interaction on Twitter–were nodes that shared pictures.

Group 1 (dark blue, 99 nodes) and Group 2 (light blue, 53 nodes) are centered on two individuals: Francois Soyer, who tweeted 13 times in the period captured by NodeXL, and Emily Steiner, who tweeted 4 times. In both cases, the author tweets that received major interactions had to do with art (which is what Professor Soyer and Professor Steiner often tweet about, much to our collective edification). Professor Steiner had one tweet that went very active, on a 15th-century Brabantese ‘Crib of the infant Jesus,’ while Professor Soyer had several (not including his own retweets).

Art was again the main topic of Group 4 (light green,35 nodes, cluster highlighted in red, right), centered on Rory Naismith’s tweet of a 12th-century Scandinavian chess piece.

So a basic read of #medievaltwitter is that we’re most enthusiastic about art and manuscript images–in other words, we are interested in the warp and weft of our trade.

This is where Group 3, dark green, 51 nodes, is very interesting. First, it has no fixed center (an advantage of the Harel-Koren layout, you can see this), and it centers entirely on issues of the profession and activism. In other words, this is where you will find people who tend to tweet about political, professional, and activist issues. There are two major “poles”: the Society for Medieval Feminist Scholarship’s tweet about the Trans Travel Fund for conference travel and David Perry’s tweet about the Scottish mosque vandalized with “Deus vult” and “Saracen Go Home” (weird and disgusting, but I haven’t seen any further update on this).

These received 19 and 14 retweets, respectively–not nearly as much interaction as art and manuscript images.

Between these two clusters are four nodes that connect the two: Rick Godden and Kathleen Kennedy each possess high Betweenness Centrality ratings, followed in much lesser degree by Matt Gabriele and T. S. Wingard.

Group 5, the last group I’m going to interrogate here, was centered around tweets from the University of Lancaster about their new lectureship in medieval history and Levi Roach, at Exeter (I highly recommend you follow him) retweeting that and tweeting other things. Group 5 also includes tweets from a number of people in my research areas, including De Re Militari, Andrew Ayton, and Iain MacInnes.

Just to be clear, I’m that solitary box down in the corner, the lonely nodes who had no interaction with folks. Or barely any. I was inquiring as to medieval commentaries and interpretations on the Book of Joshua, but got only one response. Oh well…

It should be noted that a heck of a lot of “medieval twitter” isn’t actually included in the hashtag. Many institutions that tweet medieval things don’t include any “medieval” or “medievaltwitter” hashtag in them. Further, who is the most influential tweeter depends entirely on what metrics you use to calculate it (refer to the “Top Items” column in the spreadsheet). If you look at “betweenness,” fjsoyer and piersatpenn are the top two. Yet by “closeness” calculations history_geek has the most immediate reach, because she has the most followers-but not necessarily the most influence. Which is why, when you calculate influence according to “Eigenvector,” fjsoyer appears the most influential #medievaltwitter user in this week.

So, what can we learn from this incomplete picture? Generally what I said above–most of us like tweeting and retweeting pictures and sources that help us teach and research more effectively. Job announcements, politics, activism, and such are far less prominent topics of conversation, at least under a #medievaltwitter hashtag. Personally, I don’t think that’s a problem, but others might.


The second network is a hashtag that my colleague at Dominican University, David Perry, started using: #NewBloodLibel (full data at NodeXL Gallery). It refers to Fox News and Breitbart claiming that a Lancaster, PA school canceled a production of “A Christmas Carol” because of the moral objections of a Jewish family. Complete fabrication, but the family has since fled Lancaster for the time being. That this could happen in America in the twenty-first century, right as the Holocaust generation is dying out, is especially disturbing. (Of course, the vectors of this are extremely complicated, both inside and outside of Israel itself–among other factors, it depends on your equating of opposition to Israeli settlements with Antisemitism. Not touching that with a 39.5-foot pole). In any case, it doesn’t seem to be catching on, if the metrics and visualization are anything to go by. But with it seeming more likely  that the Nazis will be marching in Montana after all, I have a feeling we’ll be needing this hashtag more and more in the new year. Feels like the 1930s, all of a sudden…

To be continued…