Asian Journal of Communication just published a manuscript written by Yon Soo Lim, Han Woo Park and me, titled Mediated relations: new methods to study online social capital. This article is part of the AJC special issue Online Social Capital and Participation in Asia-Pacific, guest edited by Marko Skoric (Nanyang Technological University, Singapore).

This is the abstract:
The Web has expanded the research agenda for communication scholars to study social capital. In this field of Internet studies, new indicators of social behavior and social relations have surfaced to describe and understand how social capital develops online and what the consequences are for social capital in general. Specifically, Web 2.0 as characterized by User Generated Content on weblogs and the enormously popular social network sites significantly increased the impor-tance of studying online social capital. To study online social capital, traditional
and new means of data collection and analysis can be used. This study focuses on the origins of the concept of social capital, how it is used in communication studies, and the means to measure social capital. Furthermore, two examples of studying online behavior and online social relations are provided to represent webometric tools for data collection and analysis: (1) the analysis of hyperlinks between political actors’ websites in South Korea, and (2) semantic network analysis of writings produced by professional journalists online and bloggers in South Korea. These examples use advanced analytical methods (hyperlink network analysis and semantic network analysis) to understand the online practices.

For the full article follow this link

Recently, Susan Greenfield gave a lecture at the Oxford Internet Institute. Apparently, she seems to be quite controversial, academically speaking. Unfortunately, the Q&A section, if there was one, wasn’t recorded.

The lecture is about 47 minutes long. Beneath the first video, there is another one from BBC’s Newsnight with Susan Greenfield and Ben Goldacre. He has a lot of critique on Greenfield’s media appearences. I most say that I am more on his side than on hers. In general, Greenfield’s argument in this lecture is for cognition and against sensory experiences. For instance, in her lecture she apparently favors Tolstoy’s War and Peace over the computer game Onimusha, in my opinion an normative and elitist position. Looking at Amazon’s ranking War and Peace ranks #32,658 while Onimusha ranks #8,345. This suggests the game is more popular than War and Peace. OK, the comparison in rank is not with out difficulties. Still, my argument is that Greenfield a) suggests War and Peace is more meaningful, and b) does not take into account their relative popularity. Regarding a), there are computer games that deal with moral choices (Bioshock, Black and White or Portal) and are more than just flashing lights and quick visual sequences. Navigating your way through a maze in a computer games requires quite some cognitive skills. So, the picture Greenfield’s presents is one-dimensional. Regarding b), even if computer games provide flat story lines, there are many more books that have little depth as well.

I’m working on the analysis of the tweets on the Dutch general elections of 2010. Because in the five week prior to Election Day, there is a considerable amount of tweets to analyse, 4,585,614 to be precise. Because the large amount of tweets I use SPSS to organize the data and create variables. While working on the data, what struck me was that Twitter’s identification of, for instance, retweets it quite sloppy to say the least.

As you may know, Twitter provides an API to allow access to their data re-using the tweets, location etc in mashups. A few of these variables are of interest for researchers, for instance user characteristics such as name, location, follower and following network size. I use these in my research as well, combined with data from other sources (see the upcoming issue of Party Politics on Twitter use by candidates in the EU elections of 2009).

As I was working on the actual tweets content I found some curious discrepancies between Twitter’s measurements and mine. For instance, retweets, identified as “RT” in the tweet text, are only flagged as a retweets by Twitter when they are positioned right at the beginning of the tweet. Even if there is a blank space before the “RT” it fails to flag it as a retweet. Furthermore, “RT” codes put somewhere in the middle of the tweet are not identified correctly either. You could consider these occurrences as false negatives.
There are also false positives: “RT” codes identified as tweets but actually are not. Consider this: in the Netherlands, there’s this broadcasting organisation called RTL. Indeed, the first to characters are identified as a retweet by Twitter. Similarly, tweets starting with the text RTV (often used as an abbreviation of for Radio and Television) is also identified as a retweet.

So, to what extent does this influence the findings? In the table below I cross-tabulated the original classification against my corrected version.

What we see here, is that there are 787 false positives and 127994 (=127987+7) false negatives. That’s 2.8% of tweets incorrectly classified. Well, this small fraction seems not too disturbing. Or does it? Well, in terms of descriptive analysis it might be negligable. As long as the miss-classifications are at random (which I didn’t check yet).

At the same time, if one wants to use these retweets to construct a social network of people retweeting each other (yes you can do that), things might be different. Even if the miss-classifications are at random, it might seriously affect network structure indicators.

Similar classification issues are at play for mentions and replies. Only the first mentioned names are identified, whereas many tweets mention multiple names. Furthermore, a reply is only a reply when the twittername begins at the first position (i.e. when one includes the @-sign).

In my opinion, it’s surprising that the programmers at Twitter Inc. haven’t used something like regular expressions to classify the data correctly. To be fair to Twitter, they acknowledge that their retweet count is an approximation. In the mean time it’s better to be safe than sorry: classify them yourself if you can.

Still, if there are tools available – I already mentioned regular expressions or think of the string functions available in SPSS – researchers studying the actual tweets might consider these to get more accurate results.