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.

Social Media Accounts of Candidates for the Maine State Senate

Deciding who to vote for in state legislative campaigns can sometimes be tricky because thorough coverage of local candidates can be hard to find. In the state of Maine,  state legislators in Maine are known for their accessibility. This may be because Maine’s legislative districts tend to be small; it may also be due to the friendly nature of Maine folk in general. Whatever the reason, getting in touch with candidates for Maine political office is both important and possible.

In this day and age, the quickest way to learn about state legislative candidates and to find their contact information is through social media platforms like individual web pages, Facebook and Twitter.  To help you in that process, the I’ve put together a spreadsheet with information about the social media presence of the 70 candidates for the Maine Senate in 2016, along with some additional contextual information. To download this information for personal use, click here for a Microsoft Excel file.

This sort of information changes all the time — if you have updated information about new accounts, please share a comment below to let me know, or write to james.m.cook@maine.edu.

Presentation Materials for Twitter Adoption in U.S. Legislatures at #SMSociety 2016 Conference

The following are links to supporting materials for the presentation “Twitter Adoption in U.S. Legislatures: A Fifty-State Study” made to the 2016 International Conference on Social Media & Society on Wednesday, July 13 at Goldsmiths, University of London.

1. Free full-text access:

ACM DL Author-ize serviceTwitter Adoption in U.S. Legislatures: A Fifty-State Study

James M. Cook
SMSociety ’16 Proceedings of the 7th 2016 International Conference on Social Media & Society, 2016

2. Download Powerpoint Presentation Slides from presentation

3. Abstract: This study draws theoretical inspiration from the literature on Twitter adoption and Twitter activity in United States legislatures, applying predictions from those limited studies to all 7,378 politicians serving across 50 American state legislatures in the fall of 2015. Tests of bivariate association carried out for individual states lead to widely varying results, indicating an underlying diversity of legislative environments. However, a pooled multivariate analysis for all 50 states indicates that the number of constituents per legislator, district youth, district level of educational attainment, legislative professionalism, being a woman, sitting in the upper chamber, holding a leadership position, and legislative inexperience are all significantly and positively associated with Twitter adoption and Twitter activity. Controlling for these factors, legislator party, majority status, partisan instability, district income, and the percent of households in a state with an Internet connection are not significantly related to either Twitter adoption or recent Twitter use. A significant share of variation in social media adoption by legislators remains unexplained, leaving considerable room for further theoretical development and the development of contingent historical accounts.

Please feel free to review these materials before or after my presentation. I look forward to your comments.

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.

This State Legislator Brought to You By…

Are you a social media user, or are you a platform for someone else’s app?

California state legislator Das Williams has signed on with the communication management service Constant Contact; it’s written all over his social media presence.  The company offers its paid users the option of automatically posting copies of e-mails online and linking to those e-mails through automated posts to Twitter.  But with every automatic Twitter post, Constant Contact has added the hashtag #constantcontact in bold blue.  People clicking on the hashtag won’t be taken to Das Williams’ messages; instead, they’ll be taken to a separate Twitter page with an advertisement for Constant Contact on top.

Das Williams on Twitter, constantly promoting constant contact

Rep. Michael Schraa of Wisconsin hasn’t posted to his Twitter account since July 9 of this year.  Nevertheless, he has posts on Twitter — automatic posts that say little good about Rep. Schraa (he has mild turnover among followers), highlight his substantive absence, and advertise for a private company — the automated Twitter metric generator fllwrs.

Twitter posts by Rep. Michael Schraa

Rep. Schraa’s experience with fllwrs may not be unique.  Google autocomplete indicates that the most common searches associated with fllwrs are “fllwrs unsubscribe” and “fllwrs stop.”  Those who are unable to stop their accounts’ association with fllwrs will continue to be billboards for the company, which in turn posts advertisements on its website to monetize its work.

Twitter is a medium through which people can communicate.  If they’re not careful, however, people can be transformed into a medium through which companies advertise.  Users can be used.

Stages of Teaching and Learning Social Media Analytics (Presentation Notes)

This afternoon, I’ll be making a short presentation of thoughts on teaching social media analytics at the 2015 conference of the International Communication Association as part of its BlueSky Workshop on Tools for Teaching and Learning of Social Media Analytics. While the workshop is focused on the experience of teaching using a series of particular tools, I am interested in rejecting the question, “Which tools are best for teaching?,” and supplanting it with the idea of building capability in students in a progressive strategy. At different stages in students’ development as social media researchers, different analytic platforms may be more or less appropriate as teaching tools.

Below is a copy of notes for my presentation; notes can also be downloaded as a PDF here.


Objective: To introduce unexperienced undergraduate students to the process of analyzing social media with sufficient breadth that they may continue to learn independently.

Teaching Challenges Provoking Implementation:

  • As the mandate for higher education continues to widen, undergraduate students tend more and more to be non-traditional, to lack preparation, to lack confidence, and to be fascinated by but intimidated by math, research and technology.
  • Social media platforms are in a state of constant change.
  • Social media analytics packages and methods are rapidly evolving now and are likely to experience significant change in the next decade.

Learning Outcomes: Students who complete a course in social media analytics will be able to:

  1. Find and navigate social media platforms
  2. Recognize the common elements of social media:
    1. Individuals
    2. Actions
    3. Memberships
    4. Relationships
  3. Extract observations of these elements into datasets:
    1. Individual-level
    2. 1-mode network
    3. 2-mode network
  4. To analyze data and report data visualizations, qualitative categorizations and quantitative statistics

Strategy: A gentle, stepwise series of stages taking students from where they are to where they need to be, introducing students to a variety of analytic platforms, and focusing on the social research skills that will remain constant despite changes in social media and social media analytic platforms.

Stages of learning social media analytics, from Consumer to Manager to Secondhand Gatherer to Primary Gatherer to Analyst

Teaching Challenges in Implementation:

  • Universal access for students who no longer share a common campus, common hardware and common software
  • Reasonable yet challenging entry for students who come to class with a variety of previous experience and capabilities
  • A variety of reasonable endpoints for students who vary in their level of progression and accomplishment

Fire a Nebraska Catholic School Teacher, Hear About it in Maine… the Twitterverse Reverberates

#LetMatthewTeach is a hashtag protesting the firing of a Nebraska Catholic school teacher for being gay.  Last week, #LetMatthewTeach was one of the top three Twitter hashtags used by state legislators in Maine, two thousand miles away from the scene of the kerfuffle.  Clearly, social media can bridge distance in some interesting ways.  I describe some other trends in social media use by Maine state legislators last week in a post to the Open Maine Politics Blog.

Before #MillionsMarchNYC, a Protest Movement Takes to Facebook and Twitter

December 12 2014 is the day before the Millions March in New York City, an organized reaction to the death of unarmed black men at the hands of the police and more broadly to structural forms of racial discrimination. Tomorrow, a variety of professional journalists will hopefully describe the messages and activities of the protest and reactions to this protest. Today, we can study the run-up to the Millions March by watching people talk about it on Facebook and Twitter.

Facebook, the social media website that most people know best, lets users create personal accounts and pages that they control. Administrators of a page for a group or event can allow posts by others, but they can also purge them if they find the content disagreeable. For announcing activist events, Facebook is a top-down affair. If you want to know what movement organizers think of their protest event, look at their Facebook page. The following is a word cloud taken from administrative posts to the MillionsMarchNYC. Words are larger in this graphic if they occur more frequently:

A Wordle Word Cloud representing the frequency of various words used by organizers of the MillionsMarch in New York City on December 13 2014

We see a lot of practical information here, many references to locations and plans and logistical concerns.  This is what’s on the mind of movement leaders. What’s on the mind of the many thousands who are thinking about going?

Twitter is a social media website unlike Facebook, a website on which certain people own Pages and control those Pages’ content. On Twitter, subjects are organized by hashtags, which no one owns, no one can purge, and which therefore tends to be driven from the bottom up. A corporation with an image problem on Facebook can simply delete comments. Woe betide the corporation that offends on Twitter; it may entirely lose control of the public conversation about itself. If you want to know what people are thinking about a social movement inside and outside its leadership, look at Twitter.

To do just that, I’ve gathered up all Twitter posts (“Tweets”) using the hashtag #MillionsMarchNYC. Perhaps the simplest way of characterizing #MillionsMarchNYC tweets is over time; as of 12 Noon on December 12, here’s the trend in posting volume:

Graph: Volume of Twitter Posts using the hashtag #MillionsMarchNYC through December 12, 12 Noon Eastern Time

5,415 tweets using the newly-created hashtag were posted from November 26 to December 12, but the dates November 26-30 are not even included in this graph because the number of tweets during that initial period — just 6 — is miniscule in comparison to the conversation two weeks later. The trend clearly indicates a spike in use of the #MillionsMarchNYC hashtag, especially over the last few days before the march, but what ideas are associated with the spiking hashtag?

A useful feature of Twitter for answering that question is that a single post may contain more than one hashtag. The co-occurrence of #MillionsMarchNYC with other hashtags in the set of Nov. 30 – Dec. 12 tweets is indicated in the following frequency table:

hashtag frequency
#blacklivesmatter 2020
#icantbreathe 1360
#dec1314 1232
#nyc2palestine 832
#shutitdown 317
#ericgarner 316
#nyc 170
#stolenlives 168
#ferguson 136
#dayofanger 128
#thisstopstoday 116
#mikebrown 103
#justiceleaguenyc 91
#fromtherivertothesea 90
#washingtonsquarepark 72
#indictthesystem 71
#nyc2ferguson 66
#anonymous 62
#wecantbreathe 62
#expectus 61
#nojusticenopeace 55
#intersectionality 49
#palestine 47
#d1314 44
#12/13/14 35
#41986 35
#12/13/2014 35
#freepalestine 27
#millionsmarchsf 25
#akaigurley 22
#weekofoutrage 22
#justiceforericgarner 21
#directaction 19
#justiceformikebrown 19
#alllivesmatter 18
#jailsupport 18
#millionsmarch 17
#handsupdontshoot 16
#equalrights4all 15
#humanrightsday 15
#michaelbrown 15
#opbelgium 14
#santacon 14
#ftp 12
#icantbreath 12
#nycprotest 12
#dayofresistance 11
#dec1213 11
#love 10
#millionsmarchoakland 10
#nojusticenopeacenoracistpolice 10

In the interest of brevity, I’ve only included hashtags used at least ten times in this list.  Just three hashtags co-occur with #MillionsMarchNYC more than 1,000 times: #blacklivesmatter, #icantbreathe and #dec1314.  The tail of the distribution is long, however, with many hashtags occurring a handful of times or just once:

Frequency Distribution of Hashtag CoOccurrence with #MillionsMarchNYC from November 26 to December 12 2014

These many hashtags do not simply co-occur with #MillionsMarchNYC in these Twitter posts, however.  They also sometimes co-occur with one another, forming a co-occurrence network that tells us something about the symbolic landscape of the leadup to this protest.

Sometimes the truth is messy; the following is a graph showing the complete co-occurrence network of hashtags used with #MillionsMarchNYC (the #MillionsMarchNYC hashtag itself is removed from the network to highlight connections between other tags).  Every hashtag is a node in this network and every co-occurrence between two hashtags appears as a tie between the two nodes.  A tie is drawn more darkly if the co-occurrence happens more often, and a node is drawn in greater size if the hashtag it represents co-occurs with a greater number of other hashtags.  Nodes are given different colors to highlight sets of nodes that are more strongly connected with one another:

Complete Co-Occurrent Network of Hashtags in #MillionsMarchNYC Twitter Posts, Nov. 26 to Dec 12

That’s pretty hard to read, isn’t it?  A few tags are evident, but there are so many that they overlap with one another, blending into a blurry mess.  The culture of a social movement can actually be a lot like that, with a large number of voices saying so many things.  But if we start to filter out the least common hashtag utterances, clearer patterns begin to emerge.

Here’s the same Twitter hashtag network, but this time just showing the hashtags for which co-occurrences happen at least 5 times:

Network of Hashtags Co-Occurring at least 5 times in the #millionsmarchnyc network on Twitter, Nov. 26-Dec. 12 2014

Here’s the same Twitter hashtag network, but this time just showing the hashtags that co-occur with some other hashtag at least 20 times:

Network of Hashtags Co-Occurring at Least 20 Times in the #millionsmarchnyc hashtag on Twitter, Nov. 26 - Dec. 12 2014

And here’s the same Twitter hashtag network, but this time just showing the hashtags that co-occur with some other hashtag at least 100 times:

Cultural Network of Hashtag Co-occurrence in the Tweets mentioning #MillionsMarchNYC from November 26 to December 12 2014

If we filter for frequency, we lose detail, but at the same time the core of this movement’s culture becomes apparent.

Although I stand by my claim that this hashtag network indicates something about social movement culture, I should note a few important limitations.  First, the use of a hashtag involves a person deciding how they would like others to categorize their declarations.  These are professions of manifest culture; latent culture remains hidden.  Second, Twitter is not a form of social media that is used by everyone; according to the Pew Internet Project young adults, urbanites and African-Americans are disproportionately likely to post to Twitter.  However, it’s important to note that this is exactly the population that forms the strongest constituency for the Millions March in New York City. In addition, even with the limitations I’ve just noted, the conversation on Twitter is much more expansive and inclusive than the conversation within the movement’s core organizing cadre.  If we’re interested in distinctions between leaders and potential participants in a social movement, Twitter is a pretty good place to look.

Tool Postscript. For data gathering, I used the Twitter API.  For data processing, I used UCINET.  For data visualization, I used NodeXL.

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?

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.

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