Is social media fair?

In Power, Weblogs and Inequality (2005)⁠, Shirky argues that the blogosphere can be understood as conforming to power law distributions, which basically means that, if one was to order each blog according to the level of public interest, the amount of interest in each blog declines exponentially for each step further down the list. To some, this observation might seem to be in conflict with the popular belief that social media, and the Internet in general, is democratizing media by empowering elites and non-elites alike with egalitarian access to the same platform, which is well expressed by Shirky’s TED Talk on the matter, available here. However, as I will assert, these seemingly disparate views are not only compatible with each other, but complementary, according to the broader themes of social network analysis.

The revolutionary component of the Internet, according to Shirky’s TED Talk, is that, while previous innovations in communications technologies allowed for either the broadcast of a specific message to a group of people or for one-on-one conversational discussion, the Internet allows for both. An institution can post a press release online for anyone to read, which is similar to publishing it in a book, but then, because the medium is online, individuals are empowered to not only respond to the publisher directly, but to discuss the press release with other members of the audience in discussions that the original publisher might not even be privy to. Prior to this, the basic structure of the media could be understood hierarchically – elites produced information that was reiterated down the pyramid to distributors capable of directly communicating the message to the media consumer. However, when the same media consumers can use the medium to discuss the message, or to broadcast their own message, elites can lose control of the narrative, and the hierarchy starts to flatten.

In a flattening hierarchy the relative power of previously disenfranchised members begins to increase, but, more importantly, their collective power increases as well. Any given individual person with a blog is probably not the most powerful, influential, or insightful news analyst in the world – they might not even be particularly good writers – but, as the small world experiments prove, they are almost certainly connected to a wildly interconnected social network, in which they are located six hops or less to any given media elite, and, indeed, the most influential individuals in the world. Furthermore, if one accepts the argument that increased weak ties are proliferated by the Internet, these distances are presumably diminished even further. In an environment where any individual’s tweet could plausibly appear in the President’s feed, the aggregate opinions of the masses isn’t just a question for academics and pollsters to answer, it is the other half of a feedback loop that increasingly characterizes the relationship between media elites and media consumers – in other words, Joe Schmo’s online take on renegotiating NAFTA is more important than it used to be because, even if Joe’s mother is the only person who will ever read it, it is one of many components of a broad, bottom-up reflection of the media’s narrative, which can only be characterized collectively as critical and oppositional by nature, or else it wouldn’t have been written in the first place.

I think that I disagree with Shirky’s assertions that the imbalances found in power law distributions can be thought of as fair, especially in the domain of interpersonal communications specifically, but I do agree with the assertion that seemingly unimportant people in this hierarchy can have an enormous impact on elites. There is an unanswered question about normativity – just because public forums tend to be imbalanced due to seemingly arbitrary reasons, doesn’t mean that they necessarily ought to be. Likening massive group discourses to typical in-person conversations is, perhaps, an oversimplification, but, oftentimes, it seems that the individuals who consistently dominate these discussions again and again have little substance to offer the collective, while the listeners, the noncredentialed thinkers, the marginalized, and those similarly overlooked individuals are the ones with the most insightful perspectives. Really listening and communicating with one another is hard work, and while this isn’t meant to be a Luddite’s critique of the printing press, I think that, to a certain extent, all of these technologies that are meant to make communication easier give us a false sense of interpersonal mastery – electronic communications seems very capable of increasing the quantity of discussions we have, but ill equipped for increasing the quality of our discussions.


White collar crime

According to the FBI’s website, white collar crimes, which are described as being “synonymous with the full range of frauds committed by business and government professionals”, are one of the bureau’s principal sections of investigation, as “a single scam can destroy a company, devastate families by wiping out their life savings, or cost investors billions of dollars (or even all three)”. The FBI classifies these types of crimes into three types: corporate fraud, money laundering, and securities and commissions fraud. Two of these classifications, corporate fraud, and securities and commissions fraud, are examined more deeply by the articles that are summarized within this blog entry, demonstrating the potential of social network analysis techniques to illuminate illicit networks typically thought to be difficult to understand by outsiders.

In Peoples and Sutton’s (2015)⁠ article, “Congressional bribery as state-corporate crime: a social network analysis”, Peoples and Sutton examine what they assert to be a form of corporate fraud, the contributions made from PACs to Congressional legislators and their impact on their voting behaviors, which they argue to be synonymous with traditional understandings of bribery – precisely stated, their research question is “Is there a statistically significant general effect of shared PAC contributors on vote similarity among pairs of lawmakers in the 109th U.S. House, controlling for other factors?” (Peoples & Sutton, 2015, p. 110)⁠. The authors rely on secondary data analysis, drawing upon financial contribution data provided by the Federal Elections Commission, as well as voting pattern data from the website . Their methodology is a synthesis of social network analysis techniques and traditional statistical methods. They construct edges between each specific member of the 109th House of Representatives characterized by the similarities in their PAC contributions, and apply a quadratic assignment procedure regression analysis to each of the dyadic pairs present within their network data. Considering the public’s overwhelmingly negative perception of Congress, their results are not surprising – the authors conclude that representatives are significantly influenced by their financial contributors, and that the best theoretical explanation for this phenomenon is an institutional culture of corruption that characterizes Congress.

In Hansen’s dissertation “The ‘bad boys’ of Wall Street: A network analysis of insider trading, 1979–1986” (2004)⁠, Hansen examines a similar white collar crime: securities and commissions fraud. Hansen applies a variety of sociological methods to answer a wide range of hypotheses, but her social network analysis’s scope of study can be summarized with the following research question: how do social network structures, and, specifically, their density, size, and degree of connectedness, impact insider trading? Similarly, Hansen also relies on secondary data analysis – the quantitative measures being borrowed from Stearns & Allan’s “ Economic Behavior in Institutional Environments: The Corporate Merger Wave of the 1980s” (1996)⁠, Stewart’s Den of Thieves (1992)⁠, Auletta’s Greed and Glory on Wall Street: The Fall of the House of Lehman (1986)⁠, and Bruck’s The Predator’s Ball: The Inside Story of Drexel Burnham and the Rise of the Junk Bond Raiders (1989)⁠. Hansen utilizes a more traditional social network analysis design, in which network metrics such as centrality and density are directly relevant to the research question, and, consequently, constructs network data that interconnects Wall Street traders as the network’s nodes. Hansen constructs two networks: a legitimate network, in which traders are connected through legitimate business relationships, and an illegitimate network, in which traders are connected through illicit transfers of money or information. Perhaps primed by the title of one of her secondary sources, Den of Thieves, Hansen anticipated that the network’s size and density would increase over time as the illegitimate network grew, but found that the size remained constant, and that the density actually decreased, reflecting, perhaps, that her illegitimate actors were self-motivated and tight-lipped. However, Hansen does demonstrate that a link exists between her legitimate and illegitimate networks, and that connectedness and centrality were key measures which patterned insider trading for her sample.

White collars crime are frequently thought of as being difficult to identify, and even more difficult to prosecute, but the application of these techniques appears to demonstrate their persisting prevalence. In any given instance, it seems, the influence of money on an individual’s decision-making process can be explained away as circumstantial, but, when such an individual is implicated in a larger network characterized by individuals in similarly compromising positions, their culpability seems much more transparent. Too often the rest of the world is characterized as wildly corrupt and antithetical to American values, when, increasingly, researchers seem to indicate that the US is similarly corrupt, if not exceptionally more corrupt, than the countries which politicians single out to illustrate American exceptionalism.


Auletta, K. (1986). Greed and Glory on Wall Street: The Fall of the House of Lehman. New York: Warner Books.

Bruck, C. (1989). The Predator’s Ball: The Inside Story of Drexel Burnham and the Rise of the Junk Bond Raiders. New York: Penguin Books.

Hansen, L. (2004). The “bad boys” of Wall Street: A network analysis of insider trading, 1979–1986. University of California Riverside.

Peoples, C. D., & Sutton, J. E. (2015). Congressional bribery as state-corporate crime: a social network analysis. Crime Law Soc Change, 64(103).

Stearns, L. B., & Allan, K. D. (1996). Economic Behavior in Institutional Environments: The Corporate Merger Wave of the 1980s. ASR, 61, 699–718.

Stewart, J. B. (1992). Den of Thieves. New York: Simon & Schuster.


I think, for my small scale social network analysis paper, I will be using my dataset from the StudentLoans subreddit (link this). In their content analysis of a peer support forum for consumer debtors, Stanley, Deville & Montgomerie (2016)⁠ argue that sharing information about their debts, within the context of a culture that increasingly associates financial wellbeing with morality, constitutes a source of resistance to creditors. Within their content analysis, Stanley, Deville & Montgomerie (2016)⁠ identified two primary types of posts in these peer support forums: troubleshooting posts, where individuals were looking for technical assistance with their debts, and journeying posts, where debtors seek emotional support. I’ve selected the StudentLoans subreddit because, based off my observations of the various subreddits related to student debt, I anticipate that the StudentLoans subreddit will be more inclined to troubleshooting-type posts, which will help my project stay in conversation with Stanley, Deville & Montgomerie (2016)⁠.

Part of this project is a small-scale content analysis, ideally into a binary category that can be depicted as edge attributes on a social network analysis graph. I’m anticipating that I will probably categorize posts into the categories of “troubleshooting” and “political discussion,” but if I feel that the sampled threads are better represented with different categories, then I do will so. Primarily, I’m interested in the capacity of online peer support as a form of resistance against financial debt, so my research questions will be as follows: 1) what is the structure of the social network formed by interactions in the StudentLoans subreddit?, 2) do prominent members of the community favor discussion in one kind of post over the other?, and, 3) do newcomers to the community favor discussion in one kind of post over the other? Essentially, if these peer support forums are instruments of resistance, I’m interested in testing their resilience – do posters get burnt out telling individuals when they should consolidate their loans and when they should challenge something, or maybe it’s just all held together by a core group of highly motivated individuals?

Example Subreddit SNA

Methodologically, I will be utilizing Reddit data which I gathered via the Reddit API – I’ve written up the process here (link this). I might actually pull this data again now that I’m more comfortable working with json files, because I think there are some data fields available that my script didn’t pull, but I doubt that will change my methodology for this paper significantly. I’m not aware of too many explicitly sociological papers using Reddit data, although I’ve definitely seem some that use it as a component of a broader social media analysis, although one study that does utilize a similar methodology for data collection is Kilgo, Yoo, Sinta, Geise, and Suran’s (2016)⁠ “Led it on Reddit,” which investigates opinion leadership on Reddit. Similarly, they also pull data from the Reddit API, and while they don’t employ social network analysis, I think that they might have really benefited from it as much of their analysis centers around popularity. Regardless, I think that my data collection and analysis will be effective without relying on a model, since I already tested some of it out on a previous practicum for this class – more pressing is if my methodology will be effective for answering my research questions. Similarly, I’m sitting on the fence on how I should define edges in my social network. On the aforementioned practicum, I considered “thread interaction,” or being present within the same thread (which seems reasonable, as this is the level at which information is transferred), which I think I’ll probably do again. I could definitely use “direct replies” as a form of edges too, as Reddit employs nested comments where users can directly reply to each other, and while this design feels like it offers some more precision, I’m not sure how that precision would help with my specific research questions (see how these nested comments look in a typical Reddit thread below).

Kilgo, D. K., Yoo, J., Sinta, V., Geise, S., Suran, M., & Johnson, T. J. (2016). Led it on Reddit: An exploratory study examining opinion leadership on Reddit. First Monday, 21(9).

Stanley, L., Deville, J., & Montgomerie, J. (2016). Digital Debt Management: The Everyday Life of Austerity. New Formations, 87(87), 64–82.


Centrality measures in SNA

The four types of centrality used in social network analysis, degree, betweenness, closeness, and Eigenvector centrality, have a variety of different applications. Briefly, degree centrality is the total number of direct connects one node has with another, closeness considers the average distance from one node to the other nodes in the network, betweenness considers a node’s position within the totality of all shortest paths between any two given nodes within a network, and Eigenvector centrality rates the importance of the other nodes connected to the specific node in question. The application of these modes of centrality depends on the research question. For example, if one is interested in specifying individuals within a network that act as gatekeepers, betweenness might be the most appropriate metric, as it considers a node’s position in important paths within the network. Alternatively, if one is interested in the spread of a pathogen, Eigenvector centrality might help identify node that could compromise the entire network. If one is interested in determining who’s on the “inside” and who is relatively isolated within a network, closeness centrality would be a good choice. Finally, there’s degree centrality, which is somewhat descriptively weak as it doesn’t consider the broader network, but if a node exhibits a high level of degree centrality, it’s probably fairly important within its network.

Measures of centrality don’t feature prominently within every analysis section that utilize social network analysis, but researchers frequently use these metrics to describe a network. For example, in Grandjean’s (2016)⁠ article, “A social network analysis of Twitter: Mapping the digital humanities community,” Grandjean constructs a directional network using Twitter following/er relationships, and ranks them according to degree centrality, betweenness centrality, and Eigenvector centrality – note, Grandjean uses in-degree and out-degree centrality measures, which indicate the direction of the relationships which the degree centrality ranks, because Grandjean’s data is directional in nature. Simiarly, Murthy & Lewis (2014)⁠ also construct social networks based off of social media data. They utilize a survey instrument which also informs much of their analysis, but one of the metrics which they utilize to find “hubs” within their network is degree centrality – they also analyze this population of individuals with high degree centrality and find that they are more likely to be women. Personally, I find this methodology surprising – given the authors’ interest in finding hubs, ranked centrality metrics might bolster the conclusions made from their survey analysis, and I think that it is a very practical form of analysis for social media projects.

Grandjean, M. (2016). A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1).

Murthy, D., & Lewis, J. P. (2014). Social Media, Collaboration, and Scientific Organizations. American Behavioral Scientist, 59(1), 149–171.


Bowling leagues and political participation

For some reason it’s weird to say out loud, or to type, but I was in a bowling league in high school. And, for me, I think I can comfortably say that it didn’t impact my likelihood to participate in the political system, integrate me with individuals that I could count on for support, pay off in the form of material benefits later in life, or increase my capacity to weather difficult times. In fact, I can say with complete confidence that I haven’t spoken a word to anyone remotely associated with said bowling league in ten years. Surprisingly, this didn’t discourage me from relentlessly citing Putnam as an undergraduate, because it’s kind of a wonderful ideology to make sense of both macro issues like our failed government, and micro issues, such as “why didn’t that jerk try to hold the door for me?”, and, since I don’t have a point of comparison to a time when even the cool kids were in bowling leagues, I can’t really dismiss the theory. Times have changed a lot since I was in high school though, and when I look around today I see people as politically engaged as they’ve ever been, and, through technology, people communicating more than, in all likelihood, any generation ever has. As Portes (2002) indicates, perhaps Putnam’s error is in treating an egocentric measure such as social capital in such a way that it could be measured like GDP, which certainly seems like some sort-of methodological fallacy, or maybe it’s just a good old case of rose tinted hindsight glasses, and while I’m certainly not going to disagree with the premise that high measurements of social capital are a positive indicator for any given individual, I don’t think Putnam’s writing has a lot of explanatory power relative to the political world. That said, I have always been sympathetic to his corollary point, which is that collective social capital is declining because people are watching absurd amounts of television – I doubt that collective social capital is declining significantly, but people are definitely watching an absurd amount of television, which always seemed like something that would be interesting to investigate. Also, to be fair, nobody goes bowling alone, they’re just not in silly competitive leagues anymore.

My critique of social capital is twofold: 1) it’s association with the term “capital,” which, for me, must refer to a mechanism for maintaining relative class privilege, and 2) the concept from which it’s derived, capital, is already a social innovation. That’s not to say that I’ve reversed course, and will now use my blogs as a space to trivialize the importance of social networks – there is power in social organization, boons and consequences are inherent in every web of relationships, and our degree of social integration demonstrably shields us from diminished life outcomes – but, as Kadushin (2012) argues, comparing these concepts to financial capital produces something of a tortured simile. Labor exploitation, extraction, and colonial projects yielded financial capital. Europe did not set the world on fire to make friends; instead, they proposed a radically exploitative mode of financially deterministic relations to govern the world. If Warren Buffet runs out of sugar tomorrow morning, he might try to borrow some from his neighbor and establish a lender’s relationship of reciprocity, but until the economy of that friendship becomes commensurable with the extraordinary social upheaval that he might cause by, perhaps, taking a massive short position on a major employer simply because he has a hunch, I don’t really think it’s fair to suggest that these concepts are significantly comparable.


Blog 2: The Social Network Analysis Perspective

The social network analysis perspective is a mode of analysis that unites one of sociology’s fundamental objects of study, the intersection between the individual and society, with modern analysis techniques. Specifically, social network analysis is rooted in Moreno’s sociometric analysis techniques, the “Harvard structuralist” school, and the Manchester anthropologist tradition (Keim, 2011)⁠. Whereas traditional quantitative projects are suited for comparing individuals and populations, social network analysis techniques allow the researcher to study specific relationships between actors, both directly and indirectly, in order to answer research questions that consider social relationships as a primary concern, such as research into socialization phenomena.

Because social network analysts consider networks as opposed to comparing individuals or groups, the structure of the data they analyze consequently changes. Traditional research often focuses on measurements about these individuals or groups, taken in a vacuum that ignores their interactions. Conversely, social network analysts primarily study data directly relevant to the interaction of two actors. Specifically, this is known as relational data, and is typically understood as the data that specifies any two nodes’ relation to each other. This could be as simple as stating that a relationship between two actors exists, but could also specify the direction of that relationship, or other qualities such as whether or not a relationship is antagonistic.

In a sense, relational data can be seen as similar to descriptive statistics in traditional studies, but quantitative social network analyses can go a little further. Descriptive analysis techniques can also be applied to a social network representation, allowing for measurements of centrality, distance between nodes, and clustering. Predictive analysis will, of course, demand an appropriate research question, but, given an adequate representation of the social network, researchers will find that social network analysis yields significant predictive power.

Take, for example, the Richmond Police Department. Police departments across the country have employed a variety of predictive techniques in order to police more effectively, but, according to reporting by The Economist (2010), the RPD has included social network analysis within their toolkit. According to the article, Richmond police officers have to come to maintain relational data on suspects and their acquaintances. Furthermore, it is reported that they mine social media for data, especially in relation to predicting potential locations of parties. Of course, critics suggest that the end result of these techniques’ application has done nothing but reinforce problematic patterns of stereotyping and over-policing, but, according to the article, RPD and other departments employing similar predictive techniques claim to be able to reduce police labor-hours and, consequently, costs.

Keim, S. (2011). The Social Network Perspective. In Social Networks and Family Formation Processes (pp. 19–30). Hackensack NJ: VS Research.

Mining social networks: Understanding the social web. (2010, September). The Economist. Retrieved from


Six degrees of separation

In Christkis and Fowler’s (2009) work, Connected, the authors’ fundamental argument is that people’s behavior and emotions are affected by the people that they know, the people that those people know, and so on – in other words, by the social network which an individual is integrated in. The authors present four theses about life within social networks: 1) individuals shape their networks, meaning that individual preferences has some effects on their social network’s size and shape 2) that networks shape individuals, in that patterns in networks, such as the amount of connections somebody has, significantly affects that individual’s life outcomes, 3) that an individual’s friends affect them, in that individuals often emulate their friends’ behavior or emotional state, and 4) that an individual’s friends’ friends affect them (and friends’ friends’ friends, etc.), which is an extension of the third rule – an individual’s friends’ friends can affect that individual’s friends, which, in turn, can affect the hypothetical individual in a chain reaction. Actions, behaviors, and feelings of people that you might not even know can create a cascading reaction, distributing the effect throughout the social network like a contagion.

In many ways, this is common sense. As an example, the authors present one of Stanley Milgram’s experiments, in which Milgram uses a small crowd of people in New York City, who look at a specific building’s window at a specific time, in order to prime those around them to do the same thing. Milgram is interested in determining the critical point – how large of a crowd is needed to successfully prime typical New York pedestrian traffic to emulate the behavior? This is similar to the frequently repeated joke, which suggests that lines are so ubiquitous in contemporary society that people will queue up in an existing line simply because it’s there, even without knowing what the line is for, Milgram is able to successfully manipulate the individuals who weren’t “in” on the experiment to repeat the behavior.

Another example of this phenomenon: William Whyte’s (1988)⁠ report and short film The Social Life of Small Urban Spaces. Whyte is tasked with trying to figure out why some plazas in New York City are able to attract crowds, and others are not, and comes up with some common sense observations. For example, Whyte observes that people are attracted to kiosks and food stands which, in turn, draws more people into the area, creating a clustering effect. Similarly, Whyte’s primary thesis is that available seating area is key – when enough people are sitting in a plaza, it invites other individuals to share the space.

The Milgram and Whyte experiments may seem like common sense, but many of the observations that Christakis and Fowler (2009)⁠ make are not. The authors identify surprising social epidemics that can be traced through social networks such as smoking, depression, voting, and even suicide. As the authors assert, given that social networks are increasingly identified as a causal factor in the spread of various social phenomena, understanding the form of these social networks is increasingly important. To this end, diagramming these networks with nodes that represent individuals, documenting their interpersonal relationships, as well as the shape and size of these networks in their totality can serve as a powerful analytical tool.

Works Cited

Christakis, N. A., & Fowler, J. H. (2009). Connected:How your Friends’ Friends’ Friends Affect Everything You Feel, Think, and Do. New York: Little, Brown and Company.

Whyte, W. H. (1988). The Social Life of Small Urban Spaces. USA.

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