The U.S. Senate on Twitter: Week One

Over the last five years, the social media platform Twitter has become a standard part of the communications package of U.S. senators.  An analysis of Twitter activity by senators in the first four days of the 115th Congress (Tuesday January 3 to Thursday January 6) reveals a large amount of communication with a great deal of variety between members of the Senate. During this period, the 100 members of the Senate posted out 1,792 Twitter posts (“Tweets”).  Many of these posts were accomplished impersonally (as with Senators’ speeches, statements and letters) through work delegated to hired communications staff.

The distribution of these Tweets is uneven. The office of New York Senator Charles Schumer posted the largest number of Tweets during the four days at 79, with Texas Senator John Cornyn not far behind at 69 Tweets. These two most voluminous Twitter users directed their posts in different ways: three out of five of Sen. Cornyn’s Tweets mentioned or replied to another Twitter user, while Sen. Schumer broadcasted his Tweets slightly more than half the time without any reference to any other Twitter user.  While both senators had much to say, Sen. Schumer acted as more of a broadcaster and Sen. Cornyn acted as more of a communicator.  The least communicative Senator on Twitter was Thad Cochran of Mississippi, who only posted one Tweet during the first four days of the 115th Congress:

Thad Cochran's one and only Twitter post during the first four days of the 115th Congress was directed toward Vice President-Elect Mike Pence

For a member of the U.S. Senate, Sen. Cochran’s single Tweet went relatively unnoticed, with only 5 retweets, 24 likes, and 14 replies. Vice President-Elect Mike Pence did not respond to Sen. Cochran’s outreach.

Those senators who do not communicate tend not to be the recipient of communication. Sen. Cochran, for instance, was not mentioned by any other senator during the new Senate’s first week.  Sens. Schumer and Cornyn, on the other hand, received multiple mentions from other senators during the period.  The most mentioned senator during the first four days of the 115th Congress was Catherine Cortez Masto, the new Senator for Nevada.  Most of these mentions by other senators were messages of welcome, although some noted her work, as in this retweeting message from Sen. Schumer regarding the new Senate’s plans to dismantle the existing health care structure:

Senator Chuck Schumer Retweets Senator Catherine Cortez Masto on the repeal of Health Care for millions of Americans

Patterns of communication between members of the Senate via Twitter tended to be partisan, as the following social network graph of mentions and replies indicates. This network graph uses a “spring embedded” visualization technique so that ties (indicated via curved lines) draw connected nodes closer to one another:

Twitter network of United States Senators. Lines indicate mentions or replies. January 3-6, 2017. Red nodes are Republicans, Blue are Democrats, Green are Independents, and gray are non-senate accounts.

Most Democratic senators’ accounts tend to cluster close to one another (although Senator Michael Bennet of Colorado is the network’s only “isolate,” not mentioning or referring to any other Twitter account during the period), and most Republican senators’ accounts also tend to cluster close to one another as well. Interestingly, the two Independents of the Senate, Senators Angus King of Maine and Bernie Sanders of Vermont, are clustered closely to Democrats’ accounts, Sen. Sanders most markedly so. Sen. King clusters with Democrats in this period because he mentions the same non-Senate Twitter account that they do (as indicated in gray).

There are exceptions to strict partisanship. Many members of the Senate refer to the same non-Senate accounts across party lines, as in the case of Senator Heidi Heitkamp of North Dakota, represented as the blue dot in the upper left of the network graph. While Sen. Heitkamp does not directly converse on Twitter with Republican senators, neither does she converse with her Democratic colleagues.  Because Sen. Heitkamp mentions an account that is also mentioned by Senator Joni Ernst of Iowa, she is clustered with Republicans. Senator Tim Scott (represented as the red dot toward the bottom of the network graph) follows the same pattern, not directly mentioning Democratic senators’ accounts but mentioning a number of the same non-senate accounts that they do. Senator Joe Manchin takes this pattern of cross-partisanship through indirect contact to its fullest extent, referring to the Twitter accounts of Vice President Mike Pence as well as the news outlets Fox News and the news shows Fox & Friends and Morning Joe that are popular targets of communication for a number of Republican senators. In the network of Twitter communication, this places Sen. Manchin squarely in the midst of the Republican upper-half of the Senatorial network.

As the large number of gray-shaded accounts in the network indicate, members of the Senate spend considerable energy communicating to accounts outside the Senate. Some 471 accounts outside the Senate were targets of communication during the first 4 days of the 115th Congress. The most common target of communication during these days was President-Elect Donald Trump, who was mentioned in 17 senators’ Tweets. The next most common target was the account of Planned Parenthood, whose federal funding for poor women’s pap smears and contraception is under threat from Senate budget cutters.  Rounding out the top ten most-referred to Twitter accounts by senators are four Democratic senators, the collective account of Senate Democrats, one Trump cabinet pick (Governor Rick Perry of Texas), and two national news outlets.

Patterns of reference to particular media outlets are highlighted in the network graph below, which is identical to the graph above but which features the accounts of national news outlets with graphic icons. The three most commonly referred-to media outlets during the period were MSNBC (10 references), Fox News (8 references), and C-SPAN (7 references). The location of some of these news outlets is unsurprising. Right-leaning Fox News, Politico, and The Hill are referred to most commonly by by Republicans, and left-leaning NPR is referred to exclusively by Democratic senators. However, the centrist CNN is surprisingly only referred to by Republican senators, and the right-leaning Washington Times is referred by by both Republicans and Democrats in the Senate.

Twitter Network of the U.S. Senate from Jan 3-6 2017 with national media outlets highlighted as graphic icons


Data collection and visualization for this post was carried out with NodeXL software.

Installing R and the package igraph on a Mac: As Always, Not Quite the Same

The incredibly useful research program called R is available on many platforms — Linux, Windows and Apple computers — and can run the same scripts across all three of its different versions.  That said, the experience of getting R to run those scripts is not quite the same on an Apple Mac.  This seems to be some kind of unwritten rule for Macs — whatever your program, on a Mac the menus, procedures and names of commands will somehow end up being different.

So what?  Well, if you’re just getting started with R, you’ll need to occasionally get some tips and tricks for making the program work.  Most of the how-to blog posts and videos you can find out there use examples using a Linux or Windows system — and they just won’t work for a Mac.  I found this out the hard way when teaching students to use the igraph package for R to perform social network analysis.  A few of my students have Macs at home, and it didn’t take long for them to cry for help, because the R program they were dealing with looked very little like the R program I’d been showing them.

If you find yourself in the same boat, and are running into trouble using R and igraph, I hope the following video will be of some help. Using a screen capture of a Mac running OS X, I briefly demonstrate the experience of installing R and running a script with the igraph package on from an Apple vantage point.  One difference is that there are a few menu options you’ll need to select when installing igraph to actually make it run.  In another simple but crucial difference for Macs, you’ll need to select all the text in your script before running it.  THEN, and only then, use the “Execute” command.  That’s not necessary on a Windows computer, but it’s a make-or-break move on a Mac.

Why? Don’t ask me why. It’s the same old story that we’ve had for thirty years: it’s just different on a Mac.

The walkthrough video:

Please leave a comment if you have a question or need clarification, and I’d be glad to be of help if I can.

Social Limits are Real: they can End (or Save) your Life

(cross-posted in revised form from an ongoing column for the Kennebec Journal)

Part I: No Limits?

As part of my work in social media research, I follow the Twitter accounts of Maine’s elected officials, studying patterns in their public communications.

I wasn’t surprised to see graduation messages from Rep. Justin Chenette, D-Saco, in my feed this month; ’tis the season, after all, and local officials are regularly invited to speak.Maine State Representative Justin Chenette writes, "The only limitations, are the ones we set for ourselves

“The only limitations, are the ones we set for ourselves.”

“You can achieve anything you set your mind to.”

Ideas like these are commonly asserted: during the first half of the month of June 2016, the phrase “no limits” was posted to Twitter at a rate of more than 100 times per hour. That’s understandable.  The idea of no limits in life, of a plain of possibilities open wide before us, is a hopeful idea. It’s an inspiring idea.  It’s a popular idea.

Unfortunately, it’s also an incorrect idea.

Don’t believe me? Allow me to issue two challenges. If there really are no limits in life, try:

• Heading to Baxter State Park here Maine, then walking straight through the base of Mount Katahdin.

• Jumping so high into the sky that you launch yourself into orbit, without assistive technology.

No matter how much you wish, no matter how hard you try, you can’t accomplish these acts. We are all limited by laws of physics. Due to electromagnetic repulsion, you can’t walk through rock. Your legs just can’t generate enough kinetic energy to overcome the force of Earth’s gravity. Ignoring these limits can kill you.

Just as there are laws of physics, there are social laws that set limits in society. Consider the following image of a social network, in which circles represent people and lines represent relationships between them:

A spcial network of minority blues and majority reds

As you can see, there are two groups of people in this network: reds and blues. Sociologist Peter Blau considered the rate of “outgroup association” (the percent of all relationships by group members to people outside the group) and discovered a law: that smaller groups have a higher rate of outgroup association than larger groups.

The reds in the network have five relationships, two of which are to non-reds. 2/5 = 60 percent. The blues in the network have 10 relationships, two of which are to non-blues. 2/10 = 20 percent.

Go ahead, add and subtract as many ties from this network as you wish. You can’t make the pattern go away. It’s an inescapable law.

So what? Why does this social law matter? Stop thinking about reds and blues and start thinking about numerical minorities: women in male-dominated corporate boardrooms, immigrants in native-born communities, or black people in majority-white America. Due to math alone, no matter how much you wish it to be otherwise, such minorities will have contact with majority members more often than majorities have contact with minorities.

Because of this pattern, minority groups must devote more energy to knowing the habits of two cultures just to get by. The stakes for failure can be high. A woman who ignores male culture in the boardroom can lose a job. An immigrant who can’t speak the language loses business. When northerner Emmett Till whistled at a white woman in the South, he lost his life.

This is one social law, one inescapable limit among many in society. A sense of limitless potential may feel good to this year’s crop of graduates, but if that feeling is untempered by a reality check, the outcomes can be disastrous.

Part 2: Deadly Friendships

Sociology’s laws are subtler than the laws of physics, but they’re no less deadly.

For another example of sociological laws and their consequences, let’s consider Feld’s Law. Twenty-five years ago, sociologist Scott Feld demonstrated that on average, your friends have more friends than you do. This sounds impossible, but it’s true.

To show you what I mean, we’ll look at another social network.  In this network, circles indicate people and lines indicate friendships between them:

A simple social network used to demonstrate Scott Feld's maxim regarding friendship among friends.

Some people in this network, like Carol, have more friends than others, like Hal.  Popularity and unpopularity is a normal part of human social life.

Also, some members of this network are friends with very friendly people; Don’s friends have 3.5 friends of their own on average (Carol with 4 friends, Ed with 3). On the other hand, the average friend of Carol has just 2.25 friends (Al, Betty and Don with 2 friends each, and Gina with 4).  That sort of variation shouldn’t be too surprising either.  But as Feld found, things get strange when we consider the overall trend.

Take a look at the table below, which shows the results for each person as well as the overall average for all people. The average person in our network has 2.5 friends. The average person’s average friend has 2.9 friends. That’s exactly the sort of result that Feld’s law predicts.

A table adding up friends, friends of friends, and averages for each of these in a social network.

Go ahead, draw your own social networks and do the math for yourself. You’ll find that Feld’s law holds true for almost any network you can think of. That’s odd, but why should you care?

One reason to care is that Feld’s law explains that feeling many of us have that we’re less popular than our friends. Odds are, you’re right. Don’t take it personally; it’s just the way societies work.

Another reason to care is that Feld’s law is also true for any relationship in which people share something with one another. People share needles in the opiate epidemic facing not just the state of Maine where I work but significant swaths of the North American continent more broadly. People have shared sexual relations since the dawn of humanity. These kinds of sharing can also share deadly viruses like AIDS, hepatitis B and syphilis.

If you’re thinking of sharing a needle, Feld’s law tells us that on average, the people you share needles with share needles with more people than you do. Feld’s law tells us that on average, the people with which you have unprotected sex have unprotected sex with more people than you do. This means that even if you share a needle just once, even if you have unprotected sex just once, your chances of catching a killer virus are disproportionately high.

Understanding invisible social laws are crucial for getting by in life because they provide guides for safe and unsafe behavior, just as physical laws guide us while we climb a mountain or skirt a rooftop. When you support research to uncover sociological laws, the benefits aren’t abstract. Understanding how society works can save a life — and that life could someday be your own.

A Hashtag Crash: CCS2016, Meet CCS2016, CCS2016, CCS2016 and CCS2016

Visit the Twitter hashtag channel #CCS2016 for information on the 2016 Conference on Complex Systems taking place in Amsterdam this upcoming September. Well actually, isn’t #CCS2016 the hashtag covering the 2016 Canadian Crowdfunding Summit? Or, wait, does #CCS2016 refer to the 2016 Content and Commerce Summit meeting in Orlando, Florida? Or is #CCS2016 the hashtag for announcements regarding 2016 Comic Con Spain? Could #CCS2016 be a hashtag for a cinematography conference in Caracas, Venezuela?

The answer is yes, yes, yes, yes, and yes. The Twitter hashtag channel #CCS2016 has been used to promote all of these, a simultaneous indication of the popularity, bottom-up flexibility, and strategic difficulty involved in using the social media platform. #CCS2016 has been used beyond this, within the past year referring to events as diverse as a Brazilian country music festival, a high school spirit effort, a “Corporate Community Summit” and an academic conference on “Cities as Community Spaces.”

The graph below features all participants in the #CCS2016 hashtag channel from April 1 to 11, 2016. Every dot (called a node) represents a Twitter account that has made a post including the #CCS2016 hashtag. Every line (called a tie) represents an instance in which one Twitter account has mentioned or replied to another Twitter account. Together, these nodes and ties make up a springtime social network for CCS2016.

Social Network for Twitter accounts using the #CCS2016 hashtag from April 1-11, 2016

As you can see, this is a disconnected network.  The large dark blue network at the top of the graph consists of Twitter users discussing Comic Con Spain.  The smaller light blue network below it is beginning to grow after the announcement of an annual Conference on Complex Systems.  To its left are participants in the upcoming Canadian Crowdfunding Summit.  To the lower right are a handful of remaining nodes discussing less popular or timely representations of the title “CCS2016.”

Separate conversations are put in separate areas of this two-dimensional graph.  On Twitter itself, no such separation is afforded. The purpose of a hashtag is to provide a space that community members can visit when they have something to say, or have a desire to listen.  In this busy, muddied virtual room called “#CCS2016,” multiple conversations are taking place on top of one another.

Why don’t all these different groups use a different hashtag?  A social media marketer would advise always reviewing past use of a phrase before adopting it as a hashtag of one’s own, lest one be accused of acting as a hashtag crasher.  But regardless, lines of distinction in Twitter can help keep the conversation coherent.  Different users are speaking different languages: English, Portugese, Spanish.  This is a sorting mechanism.  A second sorting mechanism comes from the ties charted in the graph above: people with a particular interest in a hashtag are most likely to find out about that hashtag because they follow other people with the same particular interest.  This means relevant hashtag posts are most likely to appear in a user’s Twitter timeline.  Finally, popularity of particular uses for a hashtag may shift over time, as one event comes to a head and another recedes into the past.

At times, it’s a mess, but this is what civic democracy looks like.

Talking Around The University of Maine at Augusta: A Twitter Mention Graph

Like many institutions of higher education these days, the University of Maine at Augusta communicates about its accomplishments and keeps track of the work of others using the social media service Twitter. In its communications, UMA traces the paths of the community that surrounds it.

Unlike the social media platform Facebook (oriented toward friend and family relationships) or Pinterest (devoted to the sharing of images), Twitter acts like a news clipping service of sorts. Limited to 140 characters of text, Twitter posts are like headlines in a newspaper, with links to web pages containing more information. Making headlines social, Twitter posts can mention other Twitter accounts that are relevant to the story. By tracking those mentions, we can find communities of posters who find one another’s work relevant.

To generate the social network graph you see below, I’ve searched through all Twitter posts made this year by the university’s official account, @UMAugusta, and identified all of the other Twitter accounts that @UMAugusta has mentioned. In a second step, I looked at the records of each of the Twitter accounts @UMAugusta mentioned and found out whether and how often they referred to one another. The result, formally speaking, is a level 1.5 ego network. In the graph below, Twitter accounts are indicated with labeled dots; in the parlance of social network analysis, these are called “nodes” or “vertices.” The larger a dot is in the graph, the more often it is mentioned by other Twitter accounts. Mentions between Twitter accounts are indicated with curved lines, which network analysts refer to variously as “lines,” “arcs,” “edges” or “ties.” The darker a line is, the more often mentioning occurred between two Twitter accounts.

Who Mentions Whom? A social network of mentions over Twitter surrounding @UMAugusta from January to October 2014

To highlight structure in the network of mentions surrounding @UMAugusta, I identified five clusters of Twitter accounts who mentioned one another especially often. These clusters are color-coded in the network graph above. Because the identification of clusters of conversants was driven by data, not by pre-conceived notions about which accounts might “naturally” be grouped together, it is curious to see how particular clusters focus on particular domains. Some patterns:

  • The dark green cluster in the lower-right of the graph consists strongly of offices and officers connected to student life and services at the University of Maine at Augusta.
  • The dark blue cluster in the upper-left of the graph is anchored around newspapers and newspaper reporters of central and southern Maine — the Portland Press Herald, the Kennebec Journal of Augusta and the Morning Sentinel of Waterville. These three newspapers are not simply tied by geography, but are also published under the aegis of the MaineToday Media company; @centralmesports is a joint outlet of the Kennebec Journal and Morning Sentinel. Other central Maine institutions — Colby College and the Holocaust & Human Rights Center — are also featured in this cluster.
  • The light green cluster in the lower-left of the graph features strong representation in the arts, with the 5 Rivers Arts Alliance, Harlow Gallery, photographer Jill Guthrie, and The Band Apollo included.
  • Immediate substantive commonalities in the red upper-right cluster, including my own account, the Maine State Library, the Maine Humanities Council and a edu-metrics website NerdScholar are elusive. We are tied to one another because of our mutual communications across disciplinary boundaries.
  • The light-blue cluster at the bottom of the graph is a remainder category, consisting mostly of Twitter accounts that UMA has mentioned but that do not mention other accounts often.
  • Finally, although these clusters identify groups of accounts that communicate more often internally, connections between clusters are frequent, indicating that most of the accounts mentioned by the University of Maine at Augusta are part of a broader community.

Data mining and visualization for this graph of the @UMAugusta network were carried out using free and open source NodeXL software.

Graphing #MEPolitics, the Maine Politics Twitter Network

On the social media platform Twitter, users post messages of 140 characters or less. Those messages can include links to web pages or communications to other Twitter accounts using the @ (“at”) sign. When a # sign is placed in front of a word in a Twitter post, the word becomes a “hashtag” and that post is added to a stream of all other posts using the same hashtag. Direct mentions and replies build pair bonds in the Twitter environment; hashtags build community.

For years, people interested in discussing Maine politics have used the #MEPolitics hashtag to broadcast, to speak and to listen. As Election Day 2014 approaches, volume of chatter on the #MEPolitics hashtag has increased. Who’s speaking most? Who is speaking to whom (and who isn’t)? What’s being talked about? To find out, I’ve gathered all posts (popularly called “Tweets”) using the #MEPolitics hashtag over the last weekend: October 24-26, 2014. The following is a graph of the resulting social network, in which each unique contributor to #MEPolitics is represented by a dot, each tie indicates that one contributor has mentioned or replied to another contributor in a Tweet, and contributors are placed closest to those in the network with whom they tend to communicate most:

Network of Twitter Posts using the #MEPolitics Hashtag from 10-24 to 10-26 2014. Ties indicate mentions or replies.

A few features of the #MEPolitics network are immediately apparent. First, nearly every one of the 603 participants in the #MEPolitics hashtag over the weekend is a communicator and not just a broadcaster; only 23 individuals posted Tweets during the period without referring to or being referred to in some way by another Twitter user (these are the loners colored light green in the lower-left of the graph). Second, most participants (565 out of 603 participants) are connected to one another either directly or indirectly in one giant conversation; the few unconnected conversations graphed in the lower-right corner are happening in small groups of 2 or 3. Third, the large conversation in which most Tweeters are participating is itself divided up into smaller clusters, in-groups whose members more frequently communicate with one another than with outsiders. These smaller clusters of conversation are color-coded in the graph above.

What’s going on inside those clusters of communication? To help clarify, I’ve depicted each Maine candidate for governor or federal office not as a simple dot, but rather using their profile picture. Also rendered by their profile images are the Twitter accounts of the Democratic Party and Republican Party of Maine. We can see from the graph that independent gubernatorial candidate Eliot Cutler and independent congressional candidate Richard Murphy are, not surprisingly, located in their own unique sub-community separated from the communities of discussion surrounding the major-party candidates. Perhaps more surprisingly, conversation involving Republican candidates is not embedded in a single Twitter community, but rather split among four sets. Indeed, both Senator Susan Collins and Governor Paul LePage have two Twitter accounts each, and each of their accounts is placed in its own commnunity. The Democratic Party and Democratic Party candidates, in contrast, are all located in the same sub-group of accounts. It is fair to say, at least in the context of Twitter communication and at least for this time period, that Maine Democrats have a more cohesive social media community than Maine Republicans.

A careful observer may notice the absence of one candidate and one party from this graph. Where is Republican congressional candidate Isaac Misiuk, for instance? Where is the Maine Green Independent Party, which is fielding a slate of 13 candidates in this cycle? The answer is that neither Misiuk nor the MGIP are included in the graph because neither participated in the #MEPolitics discussion, at least over the weekend.

Finally, there are some notable clusters of communication with non-party, non-candidate accounts at the center; these are indicated with a text label identifying the most central account of a cluster. M.E. McRider (BikinInMaine) is a conservative citizen (“Fighting the spread of the disease which is liberalism!“) who posted 130 provocative Tweets over the period, attracting 48 responses:

M.E. McRider bikinInMaine Twitter user declares Harry Reid officially a domestic enemy of the United States

On the left, blogger Bruce Bourgoine posted 46 Tweets over the weekend, a smaller number than McRider, attracting 36 responses:

Bruce Bourgoine posts a criticism of Rand Paul as a user of misinformation

The Kennebec Journal (KJ_Online) and Bangor Daily News (bangordailynews) are two Maine newspapers sitting at the center of their own circles of conversation. The Portland Press Herald, another prominent Maine Newspaper, isn’t in its own independent Tweeting group; rather, its Tweets are referred to predominantly by Democratic candidates and their followers.

Of course, it’s not just the structure of the #MEPolitics network that matters; the content of discussion this weekend matters too. With Election Day just a week and a half away, what subjects in Maine politics are being talked about the most? The ten most-used hashtags in last weekend’s #MEPolitics discussion were:

Top Ten Hashtags
1. #mepolitics: 2542 uses
2. #michaud2014: 386 uses
3. #michaud: 354 uses
4. #lepage: 320 uses
5. #hillaryclinton: 302 uses
6. #mike: 288 uses
7. #eliotcutler: 278 uses
8. #cutler: 246 uses
9. #maine: 224 uses
10. #poll: 206 uses

The weekend visit by Hillary Clinton on behalf of Democratic candidates and the race for Governor appear to have garnered the highest volume of attention. This pattern is borne out in a listing of the ten most linked-to web pages in #MEPolitics Tweets:

Top Ten Page Links
1. Story: Paul LePage leads polls: 45 links
2. Story: Michaud does best one-on-one: 24 links
3. Story: Hillary Clinton endorses Mike Michaud: 22 links
4. Editorial: the Governor’s race will determine health outcomes of sick Mainers: 21 links
5. Story: A retrospective on Mike Michaud’s record in the U.S. Congress: 14 links
6. Story: poll on bear baiting: 13 links
7. Video: Eliot Cutler asks Mainers to vote for someone else if he can’t win: 11 links
8. Story: Eliot Cutler benefits from out-of-state money: 11 links
9. Another Story: Eliot Cutler benefits from out-of-state money: 10 links
10. Michaud Campaign TV Ad: Cutler supporters who will vote for Mike Michaud: 10 links

Remember bear baiting? Although there are many letters to the editor being published about this controversial referendum, relatively few Twitter users are discussing the possible ban over social media. The subject of a bear baiting ban garnered only one link in the top ten links of the weekend. All other stories have to do with the race for the Blaine House.

You may notice a trend toward citing newspaper articles in the top ten link list. Let’s look at the ten most linked-to domains for a deeper look:

Top Ten Domains
1. 181 links
2. 109 links
3. 93 links
4. 37 links
5. (Kennebec Journal): 31 links
6. 22 links
7. 16 links
8. 16 links
9. 14 links
10. 14 links

Newspaper links are indeed the most popular, with the Portland Press Herald, the Bangor Daily News, the Kennebec Journal and the Lewiston Sun-Journal gaining spots in the top 10. Social media sites are also quite popular, with YouTube, Huffington Post and the blogging platform Blogspot representing the form. Campaign websites for Paul LePage and Mike Michaud make the list (notably, Eliot Cutler’s page does not). The final entrant in the top ten list of linked sources is the website, which proposes a new Constitutional Convention to amend the U.S. Constitution. Tweets mentioning this website consist almost entirely of posts made by M.E. McRider (handle @BikinInMaine) and responses to these posts.

McRider has made an impact this weekend in an otherwise election-centric week, and that impact is felt in discussion as well. Some Twitter users might elevate the salience of their favorite websites by simply posting a link again and again, a kind of anti-social behavior that some say borders on spamming. Yet McRider elicited responses as well, as evidenced by this last list of the ten most mentioned or replied-to accounts:

1. Mike Michaud (Democratic candidate for Governor)
2. Hillary Clinton
3. Eliot Cutler (Independent candidate for Governor)
4. Maine Democratic Party
5. Amy S. Fried, University of Maine political science professor and political columnist
6. Shenna Bellows (Democratic candidate for Senate)
7. M.E. McRider
8. Paul LePage (Republican candidate for Governor)
9. Bangor Daily News
10. Randy Billings, reporter for the Portland Press Herald

Last weekend, these were the speakers closest to the center of Maine political discussion on Twitter.

Methodological note: analysis and visualization was performed using NodeXL, a free and open-source plugin for Microsoft Excel that makes social media analysis accessible to almost anyone with a computer.

Track Social Networks… to Find the People Tracking You

As the course designer and instructor for an undergraduate social networks course at the University of Maine at Augusta, I am often asked why students should take the course. I think there are many answers to this question. One answer comes from a humanities standpoint: learning how to represent patterns in relationships with attention to meaningful visual cues can deepen understanding of design and lead to innovation in art. Culturally speaking, networks have geek appeal as sparkling and colorful objects lending panache to infographics. If critical thinking is important to you, you might be interested in network analysis for the challenge of mastering multidimensionality and matrix mathematics; as you work at network puzzles you’ll develop your logical and quantitative reasoning ability. But these appeal aren’t all: the study of social networks can be practically useful, too.

One practical use of social network analysis is highlighted by the Disconnect extension you can add to your Chrome, Firefox, Safari, or Opera internet browser…

worried faceI should break in here. Whenever you read "extension you can add to your internet browser," you should begin to get nervous. Many add-ins, add-ons, and add-arounds to your internet browsing or Facebook or Twitter experience are so colorful and fun to play with. But they have a second purpose lurking behind the colorful and fun one: to track your movement across websites so someone can sell data about where you go and what you do. But when consulting Disconnect's privacy policy, I was pleasantly surprised to discover that the Disconnect extension collects information about you only minimally and doesn't sell information to advertisers: "Disconnect never sells your personal info.... Our browser extensions don't collect any of your personal info. Unlike most websites, our site doesn’t collect your IP address."

… so as I was saying, the Disconnect extension available for most internet browsers makes use of social network analysis to share useful information about websites that let your data leak out to third parties:

If you install the Disconnect extension in your browser, then visit a website, it will create a network graph (or “sociogram”) with that website at the center, visually linked to other websites that are given data whenever you visit that site. By bringing those network graphs together for different websites, you can figure out how your personal information might be combined and how that combination might be harmful to you.

That might sound a little abstract, so let me make it concrete. Consider the mini-industry on the internet of “Print-On-Demand” apparel. On websites like CafePress, Zazzle and Skreened, you can browse through thousands of t-shirt designs made up by people like you. If you find a design you like, you can put it on a t-shirt that fits your style, order that shirt, and have it printed up and sent specially to you. The printer gets a cut of the profits, the designer gets a cut of the profits, and you get just the shirt you want.

While these print-on-demand services are offering you a service that makes them a little money, are they harvesting your data on the sly? To find out, I activated the Disconnect extension in my browser and visited the CafePress, Zazzle and Skreened websites. Disconnect produced three sociograms, which I combine to form the network graph you see below:

How the Skreened, CafePress and Zazzle websites track your visits: February 2014

The above image is current as of February 2014, and represents an change in tracking since the last time I looked at these websites in December of 2012:

Skreened, CafePress and Zazzle website tracking technology habits: December 2012

There are a number of patterns to notice. Consistently and by a wide margin, CafePress has been sending information about you to the largest number of third-party websites. Over time, on the other hand, Skreened and Zazzle (to a lesser extent) have started to catch up, sending more information about you to other companies. Those companies include Lucky Orange (“We don’t just tell you who is on your site, we show you what they are doing”), Monetate (“helping you understand your customers’ situations, behaviors and preferences”), Retention Science (“analyze & predict customer behaviors”), and Tell Apart (“If you’ve ever clicked on an ad for a pair of shoes that seem like they were made for you, Tell Apart may very well have been responsible“).

When the practices of individual websites such as CafePress, Skreened and Zazzle are combined into a network, we can find points of overlap. CafePress and Skreened send their information to three websites in common:,, and Each of these services tracks users by IP address, so that your behavior at CafePress and your behavior at Skreened can be combined: these data mining companies can bring together your behavior at CafePress and your behavior at Skreened to figure out aspects of your identity and preferences that might not be apparent if they had access to only one of the websites. All three websites send data to, leading to even more detailed insights about you. Would you be surprised to find out that also receives information about visitors from, and Would it surprise you to know that is owned by Google, bringing this overlap into even sharper focus?

Looking at simple lists of the third-party recipients of your information on a website can give you a rough sense of how leaky an individual website is. Looking at the network overlap in recipients tells you which of those recipients are likely to be learning the most about you, constructing an increasingly accurate virtual you for sale.

The Paradigm that isn’t in your Introduction to Sociology Text

The first chapter of the Introduction to Sociology textbook I teach with today is not very different from the first chapter of the Introduction to Sociology textbook I read as an undergraduate student in the 1980s. In text after text, there’s a nod to Marx, Weber and Durkheim (followed by a sniff at Comte). An identification of historically unrecognized founders such as Jane Addams and W.E.B. Du Bois follows. Then there’s a reference to C. Wright Mills and the “sociological imagination” before the big finish: an identification of the “big three” paradigms of sociology. These are without variation identified as functionalism, symbolic interactionism, and conflict theory.

When I made the transition to graduate school and started reading and listening to professional sociologists, I noticed immediately that the phrases “functionalism,” “symbolic interactionism” and “conflict theory” were not being used in journal articles, conferences, colloquia or seminars. When I asked my graduate advisors whether they considered themselves to be functionalists, symbolic interactionists or conflict theorists, they’d raise their eyebrows and say, “well, really I’m not any of those things.” It’s not as though functionalists, symbolic interactionists or conflict theorists never existed. Rather, these divisions were identified in the middle of the 20th Century as a handy way of summarizing the then-current fault lines of the discipline. Despite the fact that sociologists have largely moved on from these conceptual categories in their work, there seems to be a reluctance upon the part of textbook publishers to let go of the “big three.”

Some change has been creeping in. Perhaps the largest innovation over the last quarter century has been to occasionally add reference to postmodernism as an alternative fourth paradigm. Unlike the other three terms, the term “postmodernism” does make a major appearance in modern scholarship, as the following graph showing the occurrence of the paradigmatic phrases in the Google Scholar database of publications shows:

Occurrence of the Phrases Postmodernism, Conflict Theory, Functionalism and Symbolic Interactionism in the Google Scholar database from 2000 to 2013

The presence of “postmodernism” in Google Scholar search results should perhaps not be taken as an indication of the presence of “postmodernism” in the sociological literature, since postmodernism is an intellectual movement reaching far into the humanities. Similarly, the relative presence of “functionalism” may be overstated in this graph since functionalism also describes an intellectual movement in architecture and linguistics. Still, the presence of postmodernism appears considerable, and possibly explains the movement’s new inclusion in sociology texts.

Bringing the Networks In

I’ve brought this up before, but I’d like to make a current case for bringing the study of social networks into the mix of paradigms in an Introduction to Sociology course. Social network analysis is a field centered in sociology that doesn’t fit neatly into any of the three classic 20th Century paradigms identified in introductory textbooks. It isn’t macrosocial like conflict theory or functionalism (although work related to it has macrosocial implications), and while it deals with the nature of interaction social network analysis largely eschews the study of symbols, expectations and meanings that is of central importance to symbolic interactionism. Instead, social network analysis draws from graph theory, matrix algebra and theories about groups to focus on the structure of communication and affiliation outside the individual, primarily at a micro- to meso-social level. Although some pounce on the word “analysis” to suggest that the study of social networks is only methodology, the contention that the structure of social relations represented by networks has consequences for individuals, groups and societies involves a strong and distinct image of society that creates a basis for the creation of social theory. That’s what a paradigm is. The distinctiveness and conceptual clarity of network analysis gives it the potential to stand along symbolic interactionism, conflict theory, functionalism and postmodernism in an introduction to sociology text.

The case for social network analysis as a paradigm worth inclusion is bolstered by pure volume. Let’s add Google Scholar counts for “social network analysis,” a movement in sociological study that is largely left out of introductory sociology textbooks. In contrast to “postmodernism” and “functionalism,” the phrase “social network analysis” leads to a restrictive search, leaving out “social networks” references that don’t contain analysis and “network analysis” references that don’t feature the modifier “social.” The phrase “social network analysis” pretty much guarantees that results will fall within the social science and probably underestimates the actual volume of scholarship on the subject. This creates what’s called a conservative test of the presence of social network sociology. Here are the results with “social network analysis” added in:

Occurrence of Paradigmatic Phrases, including "Social Network Analysis," in Google Scholar Database from 2000 to 2013

At the turn of the 21st Century the relative presence of “social network analysis” was nothing remarkable, but for the past six years “social network analysis” has outperformed the three classic sociological paradigmatic phrases by an increasingly large margin, even when restrictively phrased. In the year 2013, “social network analysis” outperformed “postmodernism” for the first time.

Google Scholar is a very handy (and widely replicable) way of assessing the volume of scholarship for a subject, but the tool cannot easily filter by discipline. On the other hand, the University of Maine at Augusta Library’s physical and online collection of books and journals is more limited in breadth than Google Scholar’s database in contents but allows results to be filtered by discipline.

Social Science Publications in the UMA Library Collection Published since 2000 Featuring these Phrases...
Social Science Publications in the UMA Library Collection Published since 2000 Featuring these Phrases...

As you can see, these results indicate the same pattern: since the year 2000, new publications in the social sciences mentioning social network analysis have strongly surpassed publications mentioning the three classic paradigms, approaching the number of publications in the social sciences for “postmodernism.” Last year, the number of new publications for “social network analysis” in the university collection surpassed those for “postmodernism” as well.

Introductory textbook authors, pick up those pens. There are a number of audacious social facts in the network paradigm worth sharing.