Income and Internet Usage

When I initially started to do research on Americans’ social media habits, I operated under the assumption that almost all Americans had access to the internet. As such, I believed that most individuals could easily access Facebook or Twitter from their computer; if someone didn’t use social media it was due to personal preference, not lack of access.

And it is true that a large majority of Americans live in homes with a computer that’s connected to the internet. As of 2012, 74.8% of households have an Internet connection at home (Census Bureau). Of the 25.2% of American households that don’t have internet access, 7.3% report not having an internet connection because it is too expensive (Census Bureau). That’s a small minority of the population, but the United States is a large country with over 117 million households; that 7.3% represents over 8.5 million families that can’t afford the internet.

I had to take into account a new variable when examining America’s social media use: class. And indeed, regular internet usage is positively correlated with a person’s income. 73% of people making less than $30,000 a year report using the internet compared to 99% of people making over $75,000 a year (Madden).

I expected social media use to likewise be positively correlated with income. But surprisingly, people in lower income brackets are actually more likely to use social media than wealthier individuals. 72% of people making under $30,000 a year use social media. Only 65% of people in higher income brackets report using social networking sites. In addition, lower income groups are more likely to use both Facebook and Twitter (Madden).

The increasing use of cell phones as an internet browsing device might explain poorer people’s propensity to use social media. 43% of people making less than $30,000 a year do most of their internet browsing on their cellphone as compared to 21% of people making over $75,000 a year. Cellphones lower barriers to entry by allowing people to cheaply access the internet even when they don’t have access to a computer connected to the internet at home.

I was surprised that having a lower income—despite preventing people from having a computer with internet access—doesn’t prohibit people from using social media. Perhaps social media truly is a democratizing force that allows a greater portion of the population to participate in our country’s political discourse.

Given the increasing importance of social media in geopolitics, it would also be interesting to explore the relationship between income and internet usage in other countries. Are richer households more likely to have access to the internet in other countries? Are poor people in Hong Kong or Tehran able to access social networking sites? The answers to these questions may help us understand the demographic makeup of protest movements around the world.

“Computer & Internet Trends in America.” Measuring America. United States Census Bureau, 3 Feb. 2014. Web. 19 Oct. 2014. <>.

Madden, Mary. “Technology Use by Different Income Groups.” Pew Research Internet Project. Pew Research, 29 May 2013. Web. 19 Oct. 2014. <>.

Facebook in History

Earlier this week I spent time in Swem’s Special Collections looking at the notebook of a 19th century businessman. The Prince William County resident’s dated cursive was nearly illegible, the pages were faded and torn, and the text was overfilled with marginalia and curious attempts at multiplication. But the notebook is worth the trouble. For historians, such writings provide critical insights into the daily anxieties and hopes of middle class Virginians; the excitement over the world fair, the religious commodities purchased, the debts owed. From such documents we can historicize the experience of everyday life.

All of which led to a question: a hundred years after my death, what documents will historians utilize to historicize the ordinary, daily affects of my generation? Postings on social network sites are one immediate answer. Within my age cohort, 89% of people use social networking sites and 46% of internet users report posting original photos and videos (Pew 2014). On social media, we work out the inane realities of day to day living—information of little use to us, but of value to historians trying to figure out exactly what we were up to.

Historians no longer have to sift through mildewed, incomplete documents. Instead, we have a perfectly preserved ledger of the social lives of an entire generation. Paucity has been replaced with overabundance and researchers will struggle to construct coherent historical narratives out of a nearly infinite reserve of readily available data. In a matter of decades, the field of history stands to be revolutionized and historians may find themselves pining for the days when one had to crank through microfilm for a glimpse into the past.

But despite the radical changes social media threatens to impose upon Herodotus’ old discipline, there’s a relative lack of understanding as to what structures our online behavior. What motivates a post? Which topics are discussed? Who does the posting? This is to say, we have all the more reason to begin an inquiry into the ways people discuss politics online. No discipline stands alone, and history may find itself in need of political theory and computer science sooner rather than later.

“Social Networking Fact Sheet.” Pew Research Centers Internet American Life Project RSS. Jan. 2014. Web. 7 Oct. 2014. <>.

Health Care Policy in the United States: A Historical Perspective

This semester, I am taking my Government senior seminar in 20th Century American Social Policy.  We have just begun covering developments in U.S. health policy from 1900-1950.  While doing reading for the class, I was shocked by how similar much of the discourse surrounding national health insurance in this time period was to accusations and criticisms used during the debate over the Affordable Care Act (ACA).  Events from as early as the 1900s have had a profound impact on the way opponents of national health insurance characterize federal programs.  The historical context of the first debates over government regulation of health care created a set of frames that are still used when discussion of health care arise.

For example, when California and New York began debating comprehensive health care bills in the early 20th century, opponents of the state involvement in health care decried these programs “socialized medicine” (“The Lie Factory,” Lepore).  The “Red Scare,” or fears of latent communist or socialist elements among the American population, provided a ready-made set of criticism for proponents of national or state health insurance programs.  The most fervent opponents of national health insurance, physicians and private insurance companies, capitalized on these widespread fears and labeled any form of government involvement “creeping socialism” (Hamovitch 281). Public opinion on government regulation of health services was generally positive, however the public was unsure as to what form they wanted this involvement to take. Organizations such as the American Medical Association (AMA) and congressional Republicans manipulated public opinion and frightened significant portions of the populace.

The rise of political consulting firms also helped opponents of national health care mount effective public relations campaigns.  Early health care battles represented one of the earliest instances of special interest campaigning.  The AMA assessed a $25 dollar fee on all of its members to pay “Campaign Inc.,” one of the first political consulting firms (Quadagno ).  Campaign Inc. had no qualms about quote misattribution, out-of-context “facts,” and outright falsified statistics.  Their efforts successfully derailed at least five attempts at either state or national health insurance plans.   Physicians who dared publicly support reform efforts were expelled from the AMA and lost their admitting privileges at local hospitals.

As seen through the ongoing debate over the Affordable Care Act (ACA), rhetoric surrounding government intervention in health care has remained remarkably similar.  The coinciding of the “Red Scare” and nascent efforts to reform health care allowed opponents of reform to permanently link communism and socialism to questions surrounding government healthcare provisions.


Works Cited

Hamovitch, Maurice B.  “History of the Movement for Compulsory Health Insurance in the United States,” Social Service Review 27, 3 (Sept. 1953): 281-99, available from JSTOR

Quadagno, Jill.  One Nation Uninsured: Why the U.S. Has No National Health Insurance (New York: Oxford University Press, 2005), ch. 1 (Blackboard).

Lepore, Jill. “The Lie Factory,” The New Yorker (Sept. 24, 2012), available at


Girl Talk: How to identify gender by online speech patterns

Do patterns of online political discussion differ based on the gender of the writer? One of the keys to answering this question may be LIWC, or Linguistic Inquiry and Word Count, a “a computerized text analysis program that categorizes and quantifies language use” (Kahn 263). LIWC analyzes text by recognizing words and grouping words into different categories. For example, “I” and “me” are grouped into the “self-referential words” category while verbs like “think” and “believe” are grouped into the “cognitive processes” category. These categories range in specificity from broad language descriptors like “affect” to specific emotions and topics like “sadness” and “occupation”.

LIWC will be especially useful for the Online Political Discussion Computer Science team as we begin working with our 2008 twitter data set. We will use hashtags that are co-occuring with #politics to create a social network diagram of political discourse. For example, each node will be a tweet, and it will be connected to every tweet with which it shares a hashtag. Overlaying LIWC data with the social network diagram will show how the language content of tweets is mapped out over the network. Specifically, I hope to use LIWC to focus on the relationship between gender and online political discussion. However, the twitter metadata does not disclose the gender of twitter authors. Instead, I will use LIWC to analyze the language patterns of tweets to figure out the gender of twitter users.
How do we differentiate the language patterns of males and females? This is a question that both linguists and feminists have confronted for years. Second wave Feminist writers tackled this question using the language of power and powerlessness. In “Discourse Competence: Or How to Theorize Strong Women Speakers,” Sara Mills argues that the linguistic elements that make women’s speech different from men’s speech, like expressions of uncertainty and reliance on verbal fillers are not unique to women, but are expressions of submissiveness (Mills 4). At the same time, Mills writes that women act as the facilitators of conversation. Instead of steering the course for conversation, women tend do the “repair-work” of the conversation by asking questions and avoiding awkward silences (Mills 5). It should be noted, however, that some of the feminist writings of the 1970s are more theoretical than quantitative. In Language and Woman’s Place—a text on the linguistics of gender that was ground-breaking in the 1970s—the author admits that “the data on which she bases her claims have been gathered mainly through introspection: she examined her own speech and that of her acquaintances, and used her own intuitions in analyzing it” (Lakoff 46). Nonetheless, these theories of the linguistics of gender create a useful framework for discussing online political discourse. For example, if women truly are the “facilitators” of conversation, will female-authored tweets have higher measures of centrality? Or does the nature of online communication destroy the need for conversation facilitators, in which case one might predict the marginalization of female-authored tweets. Or does Twitter, a female-dominated social media site, represent a completely different paradigm for female speech?
While these questions make a good framework for theorizing about gender in online political discussion, there is still the issue of analyzing tweets for gender. For that, I look to Koppel et al.’s work on automatically categorizing written work by author gender (Koppel 401-412). Koppel and his team used a comprehensive list of words and grammatical patterns to create an algorithm that was able to predict the gender of the author of a text with eighty-percent accuracy. Although Koppel did not use LIWC in his algorithm, his team’s methods will inform how I will manipulate LIWC, which allows users to add words or expressions to dictionaries.

Works Cited

Kahn, Jeffrey H., Renée M. Tobin, Audra E. Massey, and Jennifer A. Anderson. “Measuring Emotional Expression with the Linguistic Inquiry and Word Count.” The American Journal of Psychology 120.2 (2007): 263. Print.

Koppel, M.. “Automatically Categorizing Written Texts by Author Gender.” Literary and Linguistic Computing 17.4 (2002): 401-412. Print.

Lakoff, Robin Tolmach. Language and woman’s place. New York: Octagon Books, 19761975. Print.

Mills, Sara. “Discourse Competence: Or How to Theorize Strong Women Speakers.” Hypatia 7.2 (1992): 4-17. Print.