Graphing #MEPolitics, the Maine Politics Twitter Network

On the social media platform Twitter, users post messages of 140 characters or less. Those messages can include links to web pages or communications to other Twitter accounts using the @ (“at”) sign. When a # sign is placed in front of a word in a Twitter post, the word becomes a “hashtag” and that post is added to a stream of all other posts using the same hashtag. Direct mentions and replies build pair bonds in the Twitter environment; hashtags build community.

For years, people interested in discussing Maine politics have used the #MEPolitics hashtag to broadcast, to speak and to listen. As Election Day 2014 approaches, volume of chatter on the #MEPolitics hashtag has increased. Who’s speaking most? Who is speaking to whom (and who isn’t)? What’s being talked about? To find out, I’ve gathered all posts (popularly called “Tweets”) using the #MEPolitics hashtag over the last weekend: October 24-26, 2014. The following is a graph of the resulting social network, in which each unique contributor to #MEPolitics is represented by a dot, each tie indicates that one contributor has mentioned or replied to another contributor in a Tweet, and contributors are placed closest to those in the network with whom they tend to communicate most:

Network of Twitter Posts using the #MEPolitics Hashtag from 10-24 to 10-26 2014. Ties indicate mentions or replies.

A few features of the #MEPolitics network are immediately apparent. First, nearly every one of the 603 participants in the #MEPolitics hashtag over the weekend is a communicator and not just a broadcaster; only 23 individuals posted Tweets during the period without referring to or being referred to in some way by another Twitter user (these are the loners colored light green in the lower-left of the graph). Second, most participants (565 out of 603 participants) are connected to one another either directly or indirectly in one giant conversation; the few unconnected conversations graphed in the lower-right corner are happening in small groups of 2 or 3. Third, the large conversation in which most Tweeters are participating is itself divided up into smaller clusters, in-groups whose members more frequently communicate with one another than with outsiders. These smaller clusters of conversation are color-coded in the graph above.

What’s going on inside those clusters of communication? To help clarify, I’ve depicted each Maine candidate for governor or federal office not as a simple dot, but rather using their profile picture. Also rendered by their profile images are the Twitter accounts of the Democratic Party and Republican Party of Maine. We can see from the graph that independent gubernatorial candidate Eliot Cutler and independent congressional candidate Richard Murphy are, not surprisingly, located in their own unique sub-community separated from the communities of discussion surrounding the major-party candidates. Perhaps more surprisingly, conversation involving Republican candidates is not embedded in a single Twitter community, but rather split among four sets. Indeed, both Senator Susan Collins and Governor Paul LePage have two Twitter accounts each, and each of their accounts is placed in its own commnunity. The Democratic Party and Democratic Party candidates, in contrast, are all located in the same sub-group of accounts. It is fair to say, at least in the context of Twitter communication and at least for this time period, that Maine Democrats have a more cohesive social media community than Maine Republicans.

A careful observer may notice the absence of one candidate and one party from this graph. Where is Republican congressional candidate Isaac Misiuk, for instance? Where is the Maine Green Independent Party, which is fielding a slate of 13 candidates in this cycle? The answer is that neither Misiuk nor the MGIP are included in the graph because neither participated in the #MEPolitics discussion, at least over the weekend.

Finally, there are some notable clusters of communication with non-party, non-candidate accounts at the center; these are indicated with a text label identifying the most central account of a cluster. M.E. McRider (BikinInMaine) is a conservative citizen (“Fighting the spread of the disease which is liberalism!“) who posted 130 provocative Tweets over the period, attracting 48 responses:

M.E. McRider bikinInMaine Twitter user declares Harry Reid officially a domestic enemy of the United States

On the left, blogger Bruce Bourgoine posted 46 Tweets over the weekend, a smaller number than McRider, attracting 36 responses:

Bruce Bourgoine posts a criticism of Rand Paul as a user of misinformation

The Kennebec Journal (KJ_Online) and Bangor Daily News (bangordailynews) are two Maine newspapers sitting at the center of their own circles of conversation. The Portland Press Herald, another prominent Maine Newspaper, isn’t in its own independent Tweeting group; rather, its Tweets are referred to predominantly by Democratic candidates and their followers.

Of course, it’s not just the structure of the #MEPolitics network that matters; the content of discussion this weekend matters too. With Election Day just a week and a half away, what subjects in Maine politics are being talked about the most? The ten most-used hashtags in last weekend’s #MEPolitics discussion were:

Top Ten Hashtags
1. #mepolitics: 2542 uses
2. #michaud2014: 386 uses
3. #michaud: 354 uses
4. #lepage: 320 uses
5. #hillaryclinton: 302 uses
6. #mike: 288 uses
7. #eliotcutler: 278 uses
8. #cutler: 246 uses
9. #maine: 224 uses
10. #poll: 206 uses

The weekend visit by Hillary Clinton on behalf of Democratic candidates and the race for Governor appear to have garnered the highest volume of attention. This pattern is borne out in a listing of the ten most linked-to web pages in #MEPolitics Tweets:

Top Ten Page Links
1. Story: Paul LePage leads polls: 45 links
2. Story: Michaud does best one-on-one: 24 links
3. Story: Hillary Clinton endorses Mike Michaud: 22 links
4. Editorial: the Governor’s race will determine health outcomes of sick Mainers: 21 links
5. Story: A retrospective on Mike Michaud’s record in the U.S. Congress: 14 links
6. Story: poll on bear baiting: 13 links
7. Video: Eliot Cutler asks Mainers to vote for someone else if he can’t win: 11 links
8. Story: Eliot Cutler benefits from out-of-state money: 11 links
9. Another Story: Eliot Cutler benefits from out-of-state money: 10 links
10. Michaud Campaign TV Ad: Cutler supporters who will vote for Mike Michaud: 10 links

Remember bear baiting? Although there are many letters to the editor being published about this controversial referendum, relatively few Twitter users are discussing the possible ban over social media. The subject of a bear baiting ban garnered only one link in the top ten links of the weekend. All other stories have to do with the race for the Blaine House.

You may notice a trend toward citing newspaper articles in the top ten link list. Let’s look at the ten most linked-to domains for a deeper look:

Top Ten Domains
1. 181 links
2. 109 links
3. 93 links
4. 37 links
5. (Kennebec Journal): 31 links
6. 22 links
7. 16 links
8. 16 links
9. 14 links
10. 14 links

Newspaper links are indeed the most popular, with the Portland Press Herald, the Bangor Daily News, the Kennebec Journal and the Lewiston Sun-Journal gaining spots in the top 10. Social media sites are also quite popular, with YouTube, Huffington Post and the blogging platform Blogspot representing the form. Campaign websites for Paul LePage and Mike Michaud make the list (notably, Eliot Cutler’s page does not). The final entrant in the top ten list of linked sources is the website, which proposes a new Constitutional Convention to amend the U.S. Constitution. Tweets mentioning this website consist almost entirely of posts made by M.E. McRider (handle @BikinInMaine) and responses to these posts.

McRider has made an impact this weekend in an otherwise election-centric week, and that impact is felt in discussion as well. Some Twitter users might elevate the salience of their favorite websites by simply posting a link again and again, a kind of anti-social behavior that some say borders on spamming. Yet McRider elicited responses as well, as evidenced by this last list of the ten most mentioned or replied-to accounts:

1. Mike Michaud (Democratic candidate for Governor)
2. Hillary Clinton
3. Eliot Cutler (Independent candidate for Governor)
4. Maine Democratic Party
5. Amy S. Fried, University of Maine political science professor and political columnist
6. Shenna Bellows (Democratic candidate for Senate)
7. M.E. McRider
8. Paul LePage (Republican candidate for Governor)
9. Bangor Daily News
10. Randy Billings, reporter for the Portland Press Herald

Last weekend, these were the speakers closest to the center of Maine political discussion on Twitter.

Methodological note: analysis and visualization was performed using NodeXL, a free and open-source plugin for Microsoft Excel that makes social media analysis accessible to almost anyone with a computer.

Building Offline Community to study Online Community: the Social Media & Society Conference

Attending academic conferences can feel a bit like living in a retelling of Goldilocks and the Three Bears. A conference that’s too small can leave you feeling underfed. On the other hand, a conference that’s too large can be overwhelming, intimidating and even alienating. A conference on a highly particular subject may be quite useful if you select just the right one, but may be completely useless if you’re even slightly off the mark. The presentations at an overly general conference may lack those crucial connections that stimulate career-changing “aha!” insights. If you’ve been to enough conferences, you probably know what I mean.

How rare, and therefore how precious, is the conference that hits the Goldilocks sweet spot in between these distasteful extremes. The 2013 Social Media & Society International Conference was that conference for me. Gathering and connecting presentations on the causes, kinds and consequences of online social connection, #SMSociety13 managed to be more than simply the sum of its individual presentations. Researchers across diverse fields of social science, humanities, business and computer science shared distinctive approaches and concerns regarding the same substantive subject, which meant that we all had some basis for understanding but also had something to learn:

Topics of discussion at #SMSociety13, the 2013 Social Media and Society Conference

Attendance numbered in the sweetly moderate middle between a hundred and two hundred, providing a critical but collegial mass of thinkers who began conversations during one set of presentations and continued them across others. How do we bridge (or barricade) the quantitative-qualitative divide? How do we know who is “really” speaking in an online environment, and how do participants manage the online presentation of self? What are the ways in which online interaction leads to offline action? As we ran into one another again and again in various combinations, these questions carried over into the late night at a pub and over danishes in the morning, with an aggregate from far-flung places becoming a quirky community.

Photos from the 2013 Social Media and Society Conference at Dalhousie University in Halifax, Nova Scotia

The Social Media & Society International Conference meets again at Ryerson University in Toronto on September 27-28, 2014. Got a paper or panel in mind? Submit through this link: I’d love to see you there. Abstracts are due April 18. Poster proposals are due May 23.

Combining Results of Multiple Twitter Searches into one File on the Cheap

Twitter is a great subject for social media research because 1) it is used by a lot of active and influential people and 2) its data is presumed public, obviating privacy concerns. Yet the sheer volume of Twitter data poses problems for researchers, especially those without thousands of extra dollars needed to harness insane amounts of computer power. Part of the solution for modest researchers at small institutions like myself is to study relatively small-scale subjects. Another part of the solution is to tie together multiple low-cost solutions and not look for one magic software package to address all needs.

I’m working on a project right now in which I’ve been following all tweets by and tweets mentioning members of the Maine State Legislature over time. I could write a program in PHP using the Twitter API to accomplish this… if I had a bit more time and know-how. I’ll try to get these later, but for now, I’m running multiple copies of the program Tweet Archivist Desktop, each of which captures and saves tweets by or regarding one Twitter account as they’re posted. Tweet Archivist Desktop costs just $9.99 for a perpetual license, which I consider well work the price.

Tweet Archivist Desktop creates a separate .csv dataset for each of the searches I’m saving. To gather them all together, I’m following advice shared helpfully by solveyourtech. On my Windows laptop, I’m entering the command prompt and combining all csv files in a folder into a single csv file with a variant of the “copy” command.

copy command in Windows command prompt combines multiple csv files into one

Remembering Pete Seeger 1-28-14: Collective Memory, Shared on Twitter

Activist folksinger Pete Seeger died at the age of 94 on January 27, 2014. As word of Seeger’s death spread on January 28, Twitter was flooded with tributes, including 28,226 posts made to the social media outlet’s #PeteSeeger hashtag channel by 9 PM. Of those posts, 21,617 (some 76.8%) were “re-tweets” of others’ posts. Pete Seeger wouldn’t have minded: he was a staunch believer in people forming publics to sing together, hearing a call and issuing a response, finding a tune and amplifying it not by microphones but in sheer numbers.

What did the world sing today about Pete Seeger? To answer that question, I tuned the Tweet Archivist Desktop (a handy $10 tool) to the #PeteSeeger hashtag, where it archived users’ public posts silently and efficiently in a background window on my computer. I used NodeXL (free and open-source) to find the most common word pairs in posts and to visualize them in the graphic you see below. When pairs are connected into chains and webs, the result is a semantic network that captures the spirit of the day.

Remembering Pete Seeger: a data visualization of a semantic network of the most common words and their connections in the 28,226 #PeteSeeger Twitter contributions from midnight to 9 PM on January 28 2014

In case you’re wondering, the word “communist” only appears 29 times in all those posts, far too rarely to reach the threshold required to appear in the image. “Thank” or “thanks” appears over 2,000 times.

A Hashtag Contested: Positive and Negative Social Media Reaction to the RSA-NSA Scandal

For some time now, public relations professionals have been worrying about “the bashtag problem.” Corporations may spend years cultivating positive conversations about their products over social media by developing and promoting a hashtag, only to see “their” hashtag fall into bashtag status when negative social media posts about that organization swamp the positive posts the organization seeks. Upset that public criticism may “ruin their brand,” some corporations have developed intimidation strategies to shut up and shut down isolated critics. But when large numbers of people join in the bashtagging, there’s no easy way to stop the dissent.

Through the fall of 2013, cybersecurity corporation RSA enjoyed positive references on its #RSAC hashtag on Twitter that it had developed to advertise its annual professional conference. In late December, however, it emerged that RSA’s data encryption products had a “back door” built into them that allowed the National Security Agency (NSA) to break users’ encryption and (possibly without a warrant) snoop on private communications. On December 23, RSA issued a “non-denial” that seemed to implicitly acknowledge the arrangement. On that day, the positive flavor of the #RSAC hashtag changed.

After collecting the Twitter posts (or “tweets”) of the #RSAC hashtag using the Tweet Archivist Desktop, I’ve looked at the content of each one, determining whether its attitude toward RSA or the RSA Conference (RSAC) was positive, negative or neutral. The following graph tracks the volume of positivity, negativity and neutrality in the #RSAC hashtag from December 21 2013 through January 14 2014 (today):

Volume of Tweets Positive, Negative and Neutral Toward RSA in the #RSAC hashtag, 12/21/2013 to 1/14/2014

After an initial burst in which some prominent conference speakers canceled their participation in protest, it appeared that negative tweets regarding the RSA Conference might abate over the end-of-year holidays, and RSA began to use the channel to promote its conference again. Then, on January 7, RSA let out a teaser of a Tweet about the identity of its keynote speaker:

RSA Tweets on January 7 2014: Click here to find out who has been announced as #RSAC closing keynote speaker for 2014

That speaker is Stephen Colbert. With a celebrity drawn into the story, public attention returned, generating a new peak of critical #RSAC tweets that seems to be continuing. Some of those tweets are original, but the bulk of them constitute just a few messages, tweeted and retweeted over and over again over the #RSAC hashtag channel. Anti-surveillance social movement organization Fight For the Future has deployed a special web page

Fight for the Future asks its followers to send out automated tweets to overwhelm the #RSAC hashtag

… on which it asks its followers to share this message on Twitter: "Surveillance is no joke! Tell @StephenAtHome to cancel his keynote at this NSA tainted conference. #RSAC"

15.4% of all Tweets on the #RSAC hashtag from December 21 2013 to January 14 2014 are this one Tweet, posted over and over. Another Fight for the Future mass tweet, "Does Stephen Colbert secretly love the NSA? There's only one way to find out: #RSAC," accounts for another 2.1% of #RSAC Tweets during the period.

Fight for the Future is part of a coalition of anti-surveillance groups who have announced a national day of protest on February 11. It’s called “The Day We Fight Back.” Where will the fight be? On the streets? Will there be a march? A picket? A rally in some square?

Apparently not. According to press materials, all activities will be taking place on the internet, where followers will be encouraged to share graphics on their blogs, to change their profile photos on Facebook, and to chant pre-written slogans over Twitter.

In American social movements, web banners are replacing cloth banners. Marches are giving way to orchestrated internet bashtagging. Yesterday’s gone, yesterday’s gone.

YouTube, Socially HalfBaked

In undergraduate courses, I often exhort students to express their ideas in measurable terms and to make sure that what they think they’re measuring and what they’re actually measuring have a reasonable connection.  This could be seen as the worry of a fussy academic, but there are real consequences to fuzzy thinking and fuzzy measurement in what some people call “the real world.”  I recently came across a “real-world” example of fuzzy research in the field of social media analytics that I’d like to share with you.   As this example shows, the use of trendy and colorful infographics can’t always bridge an information gap.

Thinking about YouTube: All Views? Views Per Video? Average Video Length?

On November 27 2013, the social media analytic company SocialBakers released a report in which it confidently declared that “Videos Under Two Minutes Generate the Most YouTube Views.” This is an ambiguous claim with at least two possible meanings:

Possible Meaning #1: If we count all YouTube views, most of the views will be of videos under two minutes long.
Possible Meaning #2: A video of less than two minutes in duration will tend to obtain more views than a longer video.

These possible meanings may sound similar, but they are substantially quite different. Meaning #1 brings to mind the saying that “most car crashes happen within a mile of home.” This may be true, but that fact doesn’t imply that driving close to home is more dangerous because we also do most of our driving within a mile of home. In the same vein, it might be that most video views are for videos that are under two minutes long, but if most videos are under two minutes long, that’s not at all surprising.

What we really want to know if we’re driving is what locations are more risky. For every mile we drive closer to home, are we more or less likely to crash? If we’re posting YouTube videos with the hope of obtaining views, what we really want to know is whether a single short video tends to snag more views than a single medium-length video or a single extended-length video. That question is expressed in Meaning #2.

It appears from the following text that SocialBakers is interested in testing the question expressed in Meaning #2:

“Using YouTube to reach your Fan’s can be a tricky proposition. Done right, and you can create something that your audience will remember for a long time after, and will want to share with their friends. Videos have the potential to really go viral. But how long should a video be? Make it too long, and people will be yawning and looking for something more interesting to occupy their time. Make it too short, and you might risk your content being easily forgettable and your message undelivered. We did some data investigation to get to the bottom of what video length, on YouTube, will makes the biggest impact….”

Sounds straightforward, doesn’t it? But watch as SocialBakers nimbly shifts back to Meaning #1:

“To do this, we looked at the 300 most viewed channels among different industries. The first thing we noticed is that videos between 16 seconds to 120 seconds generate almost 50% of all views on YouTube. The most successful videos are almost unanimously below 2 minutes in length.”

Did you notice the shift? In the second sentence from that passage, they’re measuring the number of views for all videos and comparing it to the number of views for all videos between 16 and 120 seconds. The problem is that there may just be a whole lot of videos between 16 and 120 seconds long — if so, it’s no wonder that they account for all those views. What we need to know to figure out whether this information is useful is another piece of information: what percent of YouTube videos are between 16 seconds and 120 seconds long. If such videos make up 70% of YouTube videos, then it’s not at all impressive that they generate 50% of all views. In fact, that result would be underwhelming. If, on the other hand, such videos make up just 20% of YouTube videos, then it would be quite impressive for them to garner 50% of all views.

Well, what does SocialBakers actually measure? To figure this out, let’s look at the company’s slickly-produced infographics from its brief report:

SocialBakers: Videos under two minutes generate the most YouTube views

This infographic doesn’t clarify matters at all. The numbers reported are percentages, but what are they percentages of? If you look closely, you’ll notice the large-text title implies that the percentages in the graphic are percentages of views (“generate the most YouTube views”). On the other hand, the tiny text underneath the graphic tells us that what SocialBakers has calculated is the “average length of YouTube videos,” not the share of views generated by YouTube videos.

SocialBakers’ second infographic makes it clear what’s going on. Take a close look at the numbers listed below, which are labeled “Lengths of YouTube Videos”:

SocialBakers: Common Lengths of YouTube Videos

All of the counts at the top of each bar add up to 579,112 videos. Those must be counts of videos, not counts of views, because a just one recent video from the top channel, PewDiePie, has gained nearly 2 million videos. The number of videos of 0-15 seconds (50,505) is 8.8% of 579,112. The number of videos of 16-30 seconds (90,619) is 15.6% of 579,112. The second infographic confirms for us that the first infographic is measuring the commonality of videos of different lengths — not the share of views obtained by videos of various lengths. Those two different-looking infographics are really just sharing the same information in different layouts.

SocialBakers’ infographics don’t have tell us whether a long video tends to obtain more views than a short video, because the infographics don’t measure the number of views per video. Those infographics don’t describe views at all (and there is no more data described in SocialBakers’ report to make up for this lack). Regardless, SocialBakers concludes that “Everyone Loves Short and Sweet Videos,” that “it is often far more effective to take up a small amount of viewing bandwidth in order to keep your audience entertained,” and that “you usually can’t go wrong by making sure your video is short and sweet.” Let’s not forget the title of SocialBakers’ report: “Videos Under Two Minutes Generate the Most YouTube Views.

Check That Data… If You Can

SocialBakers’ conclusions in the headline and text of its report don’t follow at all from the information SocialBakers has presented, but the uncomfortable truth is that most people will nod their heads and accept those conclusions anyway. If video producers follow SocialBakers’ recommendations on the basis of this report alone, they do so at their peril. If you are a consumer of social media advice, it is wise for you to be in the minority who check out claims.

A more thorough way to check out claims would be to replicate SocialBakers’ study. In order to carry out a replication, however, we would need to know what SocialBakers actually did in its study. SocialBakers shares some information in its infographics: we know from those graphics, for instance, that SocialBakers studied videos in the date range of July 1 to September 23, 2013. But did it study all new videos introduced during that period? All existing videos introduced during that period? Some other quantity entirely? We don’t know. We’re also unclear about how many videos SocialBakers measured; was it “videos from the top 300 most viewed brand channels across different industries” (infographic #2) or “videos from a sample of the top 300 most viewed brand channels” (infographic #1)? What kind of sample? What industries were selected and by what standard? Since we don’t know these details, we can’t replicate SocialBakers’ study to directly test its claims. This is probably not a mistake. If SocialBakers told you exactly how to replicate its work, after all, it would be releasing a proprietary business secret. Social media consulting as a business thrives on some secrecy, unlike social research as an academic pursuit, which thrives on the sharing of technique.

What we’ll have to settle for is a more indirect replication. This indirect replication starts with SocialBakers’ central claim for video producers: that a short video will gather more views than a long video.  SocialBakers has a 230-employee-strong stable of employees that can muster.  As a single busy individual, I’ll have to look at YouTube videos on a more modest scale.   I can take a fairly good look nonetheless: to follow the spirit of SocialBakers’ notion, I looked at the 10 YouTube channels with the most subscribers on November 30 2013:

1. Spotlight
2. PewDiePie
3. Smosh
4. HolaSoyGerman
5. JennaMarbles
6. RihannaVEVO
7. nigahiga
8. RayWilliamJohnson
9. OneDirectionVEVO
10. Machinima

I’ve gathered information on the length of, and number of views of, the ten most recent videos from each channel, resulting in 100 videos. This is an admittedly small set compared to that obtained by SocialBakers, but it has two advantages. First, these are the most recent successful videos by the most successful channels on YouTube, so if we are interested in emulating success, this is where we ought to look. Second, the procedure by which I obtained these measurements is “transparent,” meaning that I’ve told you exactly how it’s done. If you don’t believe my results, you can replicate my work to show me I’m wrong.

Let’s look at the results I obtained in three ways. First, we’ll look at the simple number of videos of various lengths. Because there are 100 total videos, these counts can also be read as percentages:

Number of Videos of Various Lengths (source: 10 most recent videos from each of the 10 most-subscribed YouTube video channels)

The results here are quite striking: the most common video length is not between 31 seconds and a minute, as reported in SocialBakers’ chart, but rather between 5 and 10 minutes. The ten most successful YouTube channels produce relatively lengthy videos, not short ones: only 5 out of their most recent 100 videos are of a minute or less in length, and only 9 out of the most recent 100 videos run for two minutes or less.

Second, let’s look at the raw number of views of these 100 videos:

Number of Video Views in Ranges of Different Video Lengths for the 10 most recent videos of the 10 most popular YouTube Channels

With over 1.1 billion video views, the videos between 3 minutes and 10 minutes in length clearly have the most views. However, from our first chart above we also know that videos between 3 minutes and 10 minutes in length account for the largest number of videos (72 out of 100 of them). Is the dominant presence of video views in this range due simply to the number of videos in the range? To find out, we can divide the total number of views in each length category by the total number of videos in a category. The result is the average number of views per video in a category, graphed below:

Average Number of Views per Video, by Length of Video, YouTube November 2013

Finally we can arrive at an answer to the question posed by SocialBakers: if we believe that the ten most popular video channels provide a model to emulate, and if we believe the length of a video is what drives people to view a video or not, then video producers seeking viewers would be well advised to upload videos of between 3 and 5 minutes in length. The next most advisable length for a video would be somewhere in the range of 5 to 10 minutes. Compared to the longer videos from these popular producers, videos of two minutes or less appear to be among the least popular on YouTube, not the most popular.

Keep Asking Questions

At this point, you may have more questions than answers. For instance, are the ten most popular video channels really the model to emulate? Could they have advantages that middle-range producers can’t touch? And is it possible that the length of a video isn’t what leads people to watch, but some other feature of a video that might itself be associated with length? To answer these questions, we’d need (yes) more research. But in order to get to this second tier of questions, we need to answer our first question — and that in turn means our measurements must be able to answer our question, and that we need to be specific in describing how our measurements are made.

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