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.

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.

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.