A Map of Popular Connotations for 12 Social Media Sites, Winter 2014

If I say “Facebook is…,” how would you complete the sentence?

The response of any individual person to that question may be idiosyncratic, but when we look at the aggregate patterns that build up across the responses of many people, trends emerge that reflect our cultural beliefs and values regarding social media.  One convenient way to track trends is through Google Autocomplete.  When you enter a term in the Google search bar, have you ever noticed that certain suggestions appear to complete your thought automatically?

Google Autocomplete suggestions in November of 2014 for Facebook Is...

These are not random suggestions.  Rather, they reflect a weighted combination of how often different phrases appear in other Google “users’ searches and content on the web.”  Speaking in sociological terms, they are an indication of the most salient cultural associations with the phrase you’ve started typing.

In the autocompletion of “Facebook is…” that you see above, results are presented as a simple list of items, but it’s possible to obtain richer information than this. First, I’ve nabbed Google’s autocompletion lists for 12 of the most popular English-language social media platforms: Facebook, Twitter, Tumblr, LinkedIn, Vine, Flickr, MySpace, Ello, Instagram, Pinterest, Google+, and YouTube. To each platform’s name I’ve added the prompting word “is” and found up to 10 most-popular search suggestions (Some new platforms like Ello have low enough search volume to generate few results. Some other platforms have repetitive results I’ve combined — “Flickr is slow” and “Flickr is too slow” are just counted as “Flickr is slow.”). An interesting feature of these lists is commonality. Despite the rich variety and nearly endless possibility of the English language, many words to complete the phrase “_______ is…” appear on Google’s top 10 list for more than one social media platform. For instance, the phrase “______ is slow” is among the top 10 results for Facebook, Tumblr, Flickr, Pinterest and YouTube. The phrase “_______ is dead” is among the top 10 results for a full 9 out of the 12 social media platforms studied here.

To graph commonalities, I’ve created the 2-mode semantic network graph you see below. A 2-mode (or “bimodal”) graph is one in which there are two kinds of nodes indicating two different kinds of objects. In this graph, social media platforms are the first kind of node, and they are indicated in yellow. The second kind of node is a top-10 ending of the phrase “________ is” by Google autocomplete. These are color-coded pink if the phrase completions indicate negative sentiment, green if the phrase completions indicate positive sentiment, and white if there is no clear sentiment expressed with the phrase completion. For some ambiguous phrases such as “YouTube is on fire” and “Pinterest is ruining my life,” a quick browse through Google search results helps to make sentiment more clear (both of these phrases turn out to be complimentary). Finally, a line is drawn from a social media platform to a phrase if that phrase is listed in the top 10 Google autocomplete results for that social media platform.

Social Media Is... Most Common Associations of Popular Social Media Sites as Identified through Google Autocomplete

For the 12 social media platforms, there are 68 distinct phrase completions listed in the Google autocomplete top 10. A large majority of these phrase completions communicate clear sentiment, and a large majority of those sentiments are criticisms. Mentions of slow speed, crashes and unavailability appear common. With the exception of YouTube and Pinterest, all of the 12 social media platforms are popularly depicted as “dead” or “dying.” Predictions of doom for social media platforms appear to be a cultural universal, at least among the socially-distinct set of participants in social media and web searches. Facebook, LinkedIn, Vine, Flickr, Ello and Instagram have no positive phrases listed in their autocompletions. A strikingly positive deviation from the negative trend appears for MySpace. This finding is unintuitive, considering how far interest in MySpace has fallen since 2008. Consider the trend in Google search volume for “MySpace” from 2004-2014:

Relative Search Volume for MySpace in Google, via Google Trends, 2004 to 2014

The letters on that graph indicate influential mainstream news articles mentioning MySpace; does the lack of any articles whatsoever since 2010 hint at an explanation? Without newspaper or magazine articles promoting the MySpace network, and with hardly anyone searching for Myspace anymore, who is left but a small group of true believers in the once-great social network? The strongly positive sentiment toward MySpace in its top-10 rankings may be due to positivity in the small set of people who are still paying attention.

What other patterns do you notice in this graph of popular search completions for social media platforms? Do the autocompletions distinguish between different social media platforms, or do they unify?

Gas Prices in and out of Context: Hi and Lois need a Fact Check

On October 18 2014, the comic strip Hi and Lois comic strip looked back with fondness on a time when gas prices were just 35.9 cents a gallon.  At the present day, the middle-class character Hi grimaces as he pumps gas costing $3.99 cents a gallon.  In a meta-analysis of existing research, social scientist Michael R. Hagerty found that people tend to view their own lives as getting better but at the same time tend to look backward in time and conclude that the lot of the average person is getting worse.  In other words, we use rose-colored glasses to view our own lives, but gray-tinted glasses to view trends in the world in general.

Hi’s view of the world is certainly tinted gray in the strip you see below, but is this pessimist funk merited?  I don’t think so; the way out of the trap of our psychological biases is to check for sociological context.  Doing that, I’d alter the Hi and Lois strip from the original into a more realistic new version:

Put Hi and Lois in Context -- are gas prices in 2014 really that bad?

Correction 1: Gas hasn’t had a price of $3.99 per gallon in the United States since July of 2008. The average price per gallon of gas in the United States was down to about $3.10 in the middle of October 2014, and they’re getting even better a month later. Source: St. Louis Federal Reserve Bank Economic Research Database.

Correction 2: The last time gas cost 35.9 cents a gallon in the United States was the year 1969, but that literal price doesn’t tell the whole story; those 35.9 cents were worth a whole lot more in 1969 than they are worth today. If we adjust for inflation, paying 35.9 cents in 1969 had the same punch to our wallets as paying $2.32 today. Sources: Bureau of Labor Statistics and InflationData.com.

Correction 3: Why do we put gasoline in cars? To go somewhere. Chance Brown forgets that the fuel efficiency of cars was far different in 1969 from the fuel efficiency we experience nowadays. In 1969, passenger cars traveled 13.6 miles on a gallon of gas, on average. In 2013, the last full year for which data is available, passenger cars traveled 36.0 miles on a gallon of gas, on average. Sources: U.S. Department of Transportation and Federal Highway Administration.

If we put all these pieces of information together, it turns out that on average and adjusting for inflation, it took 17 cents to travel a mile in a car in 1969. In contrast, it only takes 8.6 cents to travel a mile in a car today.  The depiction of gas prices as a rising social problem doesn’t match the cheaper cost of transportation today.  There may be other social problems associated with fossil fuel transportation, but economy is not one of them.  Unless Hi is driving an extra-large SUV and driving his fuel efficiency far below average, he should be smiling, not frowning.  Even and especially when trends seem obvious, it’s important to put them in context.

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.