Archive for the 'social computing technologies' category

CMC stuff I'm reading

The computer mediated communication field of research is of course very important to my dissertation, but it's so vast that it's been difficult to know where to look beyond the assigned references in my doctoral seminar (see - this is why they need the big high powered seminars).

Many of these are self-archived online so no barriers!  If I get a chance, I may come back and summarize these like I did for my comps readings. I still find those helpful.
Kiesler, S. B., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39(10), 1123-1134.
Describes some of the issues raised by electronic communication, including time and information-processing pressures, absence of regulating feedback, dramaturgical weakness, paucity of status and position cues, social anonymity, and computing norms and immature etiquette. An empirical approach for investigating the social psychological effects of electronic communication is illustrated, and how social psychological research might contribute to a deeper understanding of computers and technological change in society and computer-mediated communication (CMC) is discussed. A series of studies that explored how people participate in CMC and how computerization affects group efforts to reach consensus is described; results indicate differences in participation, decisions, and interaction among groups meeting face to face and in simultaneous computer-linked discourse and communication by electronic mail. Findings are attributed to difficulties of coordination from lack of informational feedback, absence of social influence cues for controlling discussion, and depersonalization from lack of nonverbal involvement and absence of norms.

Litt, E. (2012). Knock, Knock. Who's There? The Imagined Audience. Journal of Broadcasting & Electronic Media, 56(3), 330-345. doi:10.1080/08838151.2012.705195
For more than a century, scholars have alluded to the notion of an ?imagined audience??a person's mental conceptualization of the people with whom he or she is communicating. The imagined audience has long guided our thoughts and actions during everyday writing and speaking. However, in today's world of social media where users must navigate through highly public spaces with potentially large and invisible audiences, scholars have begun to ask: Who do people envision as their public or audience as they perform in these spaces? This article contributes to the literature by providing a theoretical framework that broadly defines the construct; identifies its significance in contemporary society and the existing tensions between the imagined and actual audiences; and drawing on Giddens's concept of structuration, theorizes what influences variations in people's imagined audience compositions. It concludes with a research agenda highlighting essential areas of inquiry.;

Sproull, L., & Kiesler, S. (1986). REDUCING SOCIAL CONTEXT CUES: ELECTRONIC MAIL IN ORGANIZATIONAL COMMUNICATION. Management Science, 32(11), 1492-1512.
This paper examines electronic mail in organizational communication. Based on ideas about how social context cues within a communication setting affect information exchange, it argues that electronic mail does not simply speed up the exchange of information but leads to the exchange of new information as well. Consistent with experimental studies, the authors found that decreasing social context cues has substantial deregulating effects on communication. And they also found that much of the information conveyed through electronic mail was information that would not have been conveyed through another medium.

[these two older ones are really to show the transformation from - people who are new to computers at all trying to use all the techniques they had in face-to-face communication - to heavy users who have developed affordances that enable them to have rich communication in media that don't have the traditional social cues]

**Treem, J. W., & Leonardi, P. M. (2012). Social media use in organizations: Exploring the affordances of visibility, editability, persistence, and association. Communication Yearbook, 36, 143-189.

[very useful as a reference for what kinds of things collaboration software should have,too]

Walther, J. B. (1992). Interpersonal Effects in Computer-Mediated Interaction: A Relational Perspective. Communication Research, 19(1), 52-90. doi:10.1177/009365092019001003
Several theories and much experimental research on relational tone in computer-mediated communication (CMC) points to the lack of nonverbal cues in this channel as a cause of impersonal and task-oriented messages. Field research in CMC often reports more positive relational behavior. This article examines the assumptions, methods, and findings of such research and suggests that negative relational effects are confined to narrow situational boundary conditions. Alternatively, it is suggested that communicators develop individuating impressions of others through accumulated CMC messages. Based upon these impressions, users may develop relationships and express multidimensional relational messages through verbal or textual cues. Predictions regarding these processes are suggested, and future research incorporating these points is urged.

Walther, J. B. (1996). Computer-mediated communication impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3-43. doi:10.1177/009365096023001001
While computer-mediated communication use and research are proliferating rapidly, findings offer contrasting images regarding the interpersonal character of this technology. Research trends over the history of these media are reviewed with observations across trends suggested so as to provide integrative principles with which to apply media to different circumstances. First, the notion that the media reduce personal influences—their impersonal effects—is reviewed. Newer theories and research are noted explaining normative “interpersonal” uses of the media. From this vantage point, recognizing that impersonal communication is sometimes advantageous, strategies for the intentional depersonalization of media use are inferred, with implications for Group Decision Support Systems effects. Additionally, recognizing that media sometimes facilitate communication that surpasses normal interpersonal levels, a new perspective on “hyperpersonal” communication is introduced. Subprocesses are discussed pertaining to receivers, senders, channels, and feedback elements in computer-mediated communication that may enhance impressions and interpersonal relations.

**Walther, J. B. (2011). Theories of Computer-Mediated Communication and Interpersonal Relations. In M. L. Knapp, & J. A. Daly (Eds.), The Sage handbook of interpersonal communication (4th ed., pp. 443-479). Thousand Oaks, CA: Sage. Retrieved from

This is a good overview of the missing social cues and other CMC research over the past 30 years. He does, however, like his own theories and research best and others may not agree :)

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Why special librarians should be active on their organization's intranet social media

Back to the dissertation now and trying to do a big push this month to get ready to defend in the Fall. A member of my committee had some great suggestions of parts of the literature that I should cover in my literature review and had missed. Particularly some CMC things I overlooked (more on these another time).

Ran across this when looking for other things by Leonardi:

Leonardi,P.M. and Meyer,S.R. (2015) Social Media as Social Lubricant: How Ambient Awareness Eases Knowledge Transfer. American Behavioral Scientist 59, pp.10-34. DOI: 10.1177/0002764214540509

My place of work has an internal Facebook like thingy but it wasn't originally built and supported by IT. With all of the competing priorities, it wasn't clear at all that some social software should get their limited funding and attention. So a bunch of researchers set up Elgg on their own on a surplus computer running under someone's desk. Use took off. Eventually it was taken over by IT who now manages and supports it.

I immediately saw it as a place to advertise library services and resources, troll for questions that needed answering, and blog about things that can't go here. Later, 3 of us won a mini grant to create an add-on that allowed users to list what books they had on their bookshelves that they would be willing to lend out and to track to whom the books were lent.

But selling social media (beyond SharePoint) in the workplace might still be difficult.

This article finds the somewhat obvious, but has a nice lit review and it might be persuasive to some.

From the lit review:

Internal knowledge sharing in organizations is good because

  • increases efficiency
  • increases innovation
  • decreases mistakes
  • makes the organization as a whole more competitive

Internal knowledge sharing is difficult because knowledge is "sticky"

  • takes work to share (individual)
  • people believe they might lose power or status by sharing (individual)
  • knowledge is too complex to transfer
  • it might be hard to find people with whom to share knowledge (technology)
  • knowledge from outside the immediate group in the organization might be devalued (culture)

So the idea of the article is that people need to look around a bit - sort of like jumping in in jump rope - before knowing how to ask a question and to whom to direct a question.  Using social media not necessarily to ask the question but to find sources and figure out how to approach them should help mitigate the stickiness issues.

They did a survey in a large telecommunications company and only worked with people who used their internal social networking site.

Unexpectedly, initial tie strength and complexity of the question impact if the seeker will ask the question immediately.  Asking right away when it's complex leads to less satisfaction. But, they found that even when the question isn't ambiguous, waiting to ask it made the knowledge transfer more satisfactory. This bit from page 27 is interesting:

for the sample of knowledge seekers who did not ask for knowledge right away, of the five media we tested (phone, email, instant message, face-to-face, and enterprise social network site), only enterprise social network site was significant and positive. This suggests that, in support of H3, the enterprise social networking site was the only medium—when used in the short time between when the knowledge seeker identified the knowledge source and when he or she asked for the knowledge—that increased the likelihood that the knowledge seeker was satisfied with transfer. Furthermore, neither identify tie strength nor knowledge complexity had a significant impact on the likelihood of satisfactory knowledge transfer.

(H3 is exactly that - using social networking to gain more information about the source will increase satisfaction with the answer)

The authors emphasize again that they found that it's the "awareness of ambient communication" aspect of social networking that helps, not just using it as a direct channel through which to direct the communication.

Back to my post title. What does this mean for special librarians in corporate, government, or research settings (not academic or public)? It reinforces the idea that maintaining an active presence on your intranet social networking site is a good idea so that your potential users can check you out, get to know you, and better ask you questions. Of course, try not to sound like an idiot on there because then they'll know that, too :)

Also, if your organization is interested in KM, has an intranet, AND you have enough people to get to some sort of critical mass (what might that be?), setting up one of these social networking services is probably a good idea.

Leonardi, P., & Meyer, S. (2014). Social Media as Social Lubricant: How Ambient Awareness Eases Knowledge Transfer American Behavioral Scientist, 59 (1), 10-34 DOI: 10.1177/0002764214540509

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Why FriendFeed Rocked

If you're a librarian or into open access or scholarly communication, at some point you've probably heard of FriendFeed. The service closed today after seven years and it was kind of like the final episode of Cheers or MASH. It had been acquired by Facebook a while ago and development had stopped. Reliability was down. The number of active users was down and had never been anywhere near Facebook even in its prime. There was no way for it to make money - no ads, no premium features, no subscriptions.

With that said, there are a lot of people who are really torn up about them shutting it down. We built a community there - a stay at home mum from Australia, an engineer from Detroit, a software developer from Alberta, several ministers, lots of other neat people, and the LSW. The Library Society of the World is sort of an anti-association. Read Walt's discussion of that in his May 2015 Cites and Insights (pdf)

So why did it work? When I started with it, there were lots of social software things all over - blogs, Twitter, Flickr, and there were more and more as time went on. Many of these act like they will be your one and only place. But that's obviously not true. They have different functions, different communities, different affordances... Used to be you could share things from your Google Reader account but that wasn't the same.

What FriendFeed did is to bring all of these feeds in to one place, with a little snippet or picture, and let you comment and reshare and like. You could share something right there, but you didn't have to. It would try to group things if you had your blog posting directly and your Twitter stream duplicated that. You could see what your friends liked and find new and interesting people that way. For the first few years I was on there I was only going to follow library people, well, and of course Heather, and Cameron, and Neil, and Egon, and ... but I was glad I did get to enjoy and eventually follow some really neat people.

If someone posted something you didn't want to see, you could hide just that post, or you could hide things they shared via a particular feed. You could block someone completely so you wouldn't have to see their comments.

I've played with a lot of other tools, but FriendFeed just worked for me.  It was a great source of recipes, if nothing else!

There was a team of savvy folks archiving as much as they could. So far, the best way to see what it was like is to see Micah Wittman's . That's really pretty cool.

So where is LSW now? We're trying Discourse at (doesn't allow you to bring feeds in but you can get a cod badge). We're also trying which is really, really cool... but we don't know how sustainable. And we followed each other on Twitter... but it's not the same.

I miss it already!

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Another dissertation on science blogs

Any readers interested in my work (and you'd probably have to be following me for a while to even know what that is), will probably be interested in that of Paige Brown Jarreau. She's a PhD Candidate at LSU and is defending any day now. She did a massive set of interviews and a survey and has shared some of her results on FigShare, on her blog, and in her Twitter stream. So far we've mostly had a glimpse of her findings - can't wait to see the rest of her dissertation (good grief the rate I'm going I guess I'll get a chance to cite it in mine :) )

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Using R TwitteR to Get User Information

I'm gonna keep stating the obvious, because this took me a few hours to figure out. Maybe not working continuously, but still.

So, I have like more than 6000 tweets from one year of AGU alone, so I'm gonna have to sample somehow. Talking this over with my advisor, he suggested that we have to find some reasonable way to stratify and then do random within the stratification. I haven't worked all the details out yet - or really any of them - but I started gathering user features I could base the decision on. Number of tweets with the hashtag was super quick in Excel. But I was wondering if they were new to Twitter, if they tweeted a lot, and if they had a lot of followers. That's all available through the api and using the TwitteR package by Jeff Gentry.  Cool.

So getUser() is the function to use. I made up a list of the unique usernames in Excel and imported that in. Then I went to loop through.

library("twitteR", lib.loc="C:/Users/Christina/Documents/R/win-library/3.0")
#get the data
 data USERdata<-vector()
 temp<-getUser(USER, cainfo="cacert.pem")
 #test for users 4-6<-sapply(data$user[4:6],userInfo)

But that was sorta sideways... I had a column for each user... sorta weird. Bob O'H helped me figure out how to transpose that and I did, but it was sorta weird.

So then I tried this way:<-function(startno,stopno){
# set up the vectors first
for (i in startno:stopno) {
thing<-getUser(data$user[i], cainfo="cacert.pem")[i]<-data$user[i]

return(data.frame(,created=USER.created, posts=USER.posts,followers=USER.foll, stringsAsFactors=FALSE))

So that was cool, until it wasn't. I mean, turns out that 2% of the users have deleted their accounts or block me or are private or something. So it didn't recover from that error and I tried to test for is.null() and is.NA() but it failed....
So then I went back to the mailing list and there was a suggestion to user try() but eek.
So then I noticed that if you have a pile to look through you're actually supposed to use
lookupUsers(users, includeNA=FALSE, ...)
And I did, and I wanted to keep the NA so that I could align with my other data later... but once again, no way to get the NAs out. And it's an object that's a pile of lists... which I was having trouble wrapping my little mind around (others have no issues).
So I went back and used that command again, and this time said to skip the NA (the not found users). Then I think from the mailing list or maybe from Stack Overflow? I had gotten the idea to use unlist. So here's what I did then:
easy.tweeters.noNA<-lookupUsers(data$user, cainfo="cacert.pem")
#check how many fewer this was
#1247 so there were 29 accounts missing hrm
for (i in 1:1247){holddf<-twListToDF(easy.tweeters.noNA[i])

And that created a lovely dataframe with all kinds of goodies for it. I guess I'll have to see what I want to do about the 29 accounts.

I really would have been happier if it was more graceful with users that weren't found.

Also, not for every single command you have to user the cainfo="cacert.pem" thingy... Every time, every command.

ALSO, I had figured out oauth, but the twitter address went from http:// to https:// and so that was broken, but I fixed it. I hope I don't have to reboot my computer soon! (Yeah, I saved my credentials to a file, but I don't know... )

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Using R to expand Twitter URLS

So this should be so simple and obvious that it's not worth a post, but I keep forgetting how to do everything so I'm gonna put this here to remind myself.

Here's where I am. I have a list of 4011 tweets with #agu12 or #agu2012 hashtag. A lot of these are coding as "pointers" - their main function is to direct readers' attention somewhere else. So I got to wondering: where?  Are they directing people to online versions of the posters? Are they just linking to more NASA press releases?  % going to a .edu?

Of course all the URLs are shortened and there are services you can use to expand, but in R, it's already right there in the TwitteR package as


This uses the API . All you have to do is plug in the URL. Cool!

So here was my original plan: find the tweets with urls, extract the urls, expand them, profit! And I was going to do all this in R. But then it got a little ridiculous.
So instead I: used open refine to find all the urls, then assigned IDs to all the records, and then used filtering and copy and pasting to get them all in two columns ID, URL.

Issues: non-printing characters (Excel has a clean command), extra spaces (trim - didn't really work so I did a find and replace), random commas (some needed to be there), random other punctuation (find and replace), #sign

The idea in R was to do a for loop to iterate through each url, expand it, append it to a vector (or concatenate, whichever), then add that to the dataframe and do stats on it or maybe just export to Excel and monkey with it there.

For loop, fine, append - not for love nor money despite the fact that I have proof that I successfully did it in my Coursera class. I don't know. And the API was failing for certain rows. For a couple of rows, I found more punctuation. Then I found the rest of the issues were really all about length. They don't expect shortened urls to be long (duh)!  So then I had to pick a length, and only send ones shorter than that (50) to the api. I finally gave up with the stupid append, and I just printed them to the screen and copied them over to Excel. Also I cheated with how long the for loop had to be - I should have been able to just say the number of rows in the frame but meh.
Anyhow, this worked:

 setwd("~/ mine")
library("twitteR", lib.loc="C:/Users/Christina/Documents/R/win-library/3.0")
#get the data
data <- read.csv("agu12justurl.csv", colClasses = "character")
#check it out
#test a single one
#this was for me trying to append, sigh
full.vec <- vector(mode="character")
#create a vector to put the new stuff in, then I'll append to the data frame, I hope
#check the for loop 
 for (i in 1:200){print(data.sub$url[i])}
#that works
for (i in 1:3){print(decode_short_url(data.sub$url[i]))}
#that works - good to know, though, that if it can't be expanded it comes back null

#appending to the vector is not working, but printing is so will run with that 
for (i in 1:1502){ if(nchar(data$url[i])>50){
 } else {

If anyone wants to tell me what I'm doing wrong with the append, it would be appreciated. I'm sure it must be obvious.

So what's the answer? Not sure. I'll probably do a post on string splitting and counting... OR I'll be back in Open Refine. How do people only ever work in one tool?

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Keeping up with a busy conference - my tools aren't doing it

I wrote about trying to use TwitteR to download AGU13 tweets. I'm getting fewer and fewer with my calls. I was very excited to try Webometric Analyst from Wolverhampton and described by Kim Holmberg in his ASIST webinar (BIG pptx, BIG wmv).

One of the things Webometric Analyst will do is do repeated searches until you tell it to stop. This was very exciting. But I tried it and alas, I think Twitter thinks I'm abusive or something because it was way throttled. Like I could see the tweets flying up on the screen at but the search was retrieving like 6. I ran the R search mid-day today and got 99 tweets back which covered  5 minutes O_o. I asked for up to 2000, from the whole day, and had it set to retry if stopped.


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The #agu12 and #agu2012 Twitter archive

I showed a graph of the agu10 archive here, and more recently the agu11/2011 archive here, and now for the agu12/2012 archive. See the 2011 post for the exact methods used to get the data and to clean it.

#agu12 and #agu2012 largest component, nodes sized by degree

#agu12 and #agu2012 largest component, nodes sized by degree

agu12 and 2012 other components no iso sized by degree n1294

#agu12 and #agu2012 other components, no isolates, nodes sized by degree

I will have to review methods to show this, but from appearances, the networks are becoming more like hairballs. In the first year, half the people were connected to theAGU and the other half were connected to NASA, but very few were connected to both. The other prominent nodes were pretty much all institutional accounts. In 2011, that started to decrease and now in 2012 you can't really see that division at all. There are the top three nodes - two the same plus a NASA robotic mission - but then there's a large second group with degrees (connections to others) around 40-80 (combined indegree and outdegree) of individual scientists.

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An image of the #agu2011, #agu11 Twitter archive

A loooong time ago, I  showed the agu10 archive as a graph, here's the same for the combination of agu11 and agu2011. I mentioned already about the upper/lower case issues (excel is oblivious but my graphing program cares) - this is all lower case (I first tried to correct but kept missing things so I just used Excel's =LOWER()). I also discussed how I got the data. I'm going to have to probably go back and do this for 2010 if I really want equivalent images because 1) I only kept the first @ (this has all the @) 2) I don't believe I did both 2010 and 10 so I probably missed some. For this image I did a little bit of correcting. One twitter name spelled wrong and quite a few people using the_agu or agu instead of theagu. I also took out things that were like @10am or @ the convention center.

I made this graph by taking my excel spreadsheet that was nicely username first@ second@ .... and copying that into Ucinet's dl editor and saving as nodelist1. Then I visualized and did basic analysis in NetDraw.

agu2011 and agu11 largest component, sized by degree

agu2011 and agu11 largest component, sized by degree

The largest component is 559 nodes of 740 and this time you don't see that breakdown where the people who tweeted @NASA didn't tweet @ theAGU. There were 119 isolates and other components with 2,3, and 10 nodes:

Other components, sized by degree (no isolates)

Other components, sized by degree (no isolates)

eta: oh yeah, one other little fix. I took out random punctuation at the end of user names like hi @cpikas! or hey @cpikas: or  well you get the idea

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New, now scientists can use blogs to talk to other scientists about science!

I collect articles on scientists using blogs and twitter. Mostly because it’s relevant to my dissertation, but also because I find them interesting. You can see a listing here: (used to be displayed on my UM page, but that broke in the transition).

So one of these articles that I saw tweeted by about five people at the same time is Wolinsky, H. (2011). More than a blog. EMBO reports 12, 1102 - 1105. doi:10.1038/embor.2011.201 .

Of course it starts with the arsenic life discussion. It talks about the immediacy of the blog reaction and the tone of the discussion on the blogs.  Overall a nice article.

I think the subtitle of the piece is unfair. It acts like the title of this post when the article itself is more about where blogs have evolved to right now. There are a lot of differing experiences with blogs and differing uses, some of which have always been talking shop.

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