What you see here is the network of tweets between candidates in the 2010 campaign of general elections 2010. The visualization is a directed graph of tweets from candidates, aggregated at the party level. The animated image shows the outgoing and incoming relations (i.e.the tweets to and from, out- and in-degree of sorts) by party, starting with CDA (at 3 o’clock), then proceeding clockwise. The separate graphs were produced by NodeXL, then merged into a single animated gif. I decided to layout the nodes in a circle and not use any algorithm. All attempts to create more insight using these algorithms failed, so I opted for a simple layout.

All this is produced for a paper the be presented at the WAPOR conference in September. The next step will be to do the analysis at candidate level and extend the sample to the followers of the candidates. However, I am not confident NodeXL will be able to handle this. So, I’ll move to Pajek to do the analysis.

Tweet interactions of candidates in the 2010 general elections in the Netherlands

This summer I published two articles about the use of Twitter during the European Parliament Elections. Below are the abstracts of both articles

Online social networks and micro-blogging in political campaigning: The exploration of a new campaign tool and a new campaign style
Maurice Vergeer, Liesbeth Hermans and Steven Sams, published in Party Politics.
This study explores how candidates running for the European Parliament (EP) in 2009 used micro-blogging and online social networks – in this case Twitter (www.twitter.com) in the early stage of its adoption – to communicate and connect with citizens. Micro-blogging in general, and Twitter in particular, is one of the new and popular Web 2.0 applications, yet there has been little research focusing on the use of Twitter by politicians. After reviewing different types of campaigning strategies and introducing a new and distinct strategy, this descriptive and exploratory study focuses on political candidates’ use of micro-blogging and online social networking (i.e. Twitter) from a longitudinal, social network, and ideological perspective. The results clearly show that most candidates in 2009 still used Twitter reluctantly. Those who used Twitter did so predominantly for electoral campaigning and only sparingly for continuous campaigning. Candidates from progressive parties are the most active users of Twitter as a campaigning tool, whereas conservatives are virtually absent online. Although candidates’ first degree networks are still relatively small and unconnected, their second degree networks are quite extensive. Candidates from parties in opposition have more extensive first degree networks than those from ruling parties. Candidates from fringe parties show small online networks.
(for full article, follow this link)

Is the voter only a tweet away? Micro-blogging during the 2009 European Parliament campaign in the Netherlands
Maurice Vergeer. Liesbeth Hermans and Steven Sams, published in First Monday
This study explores the use of Twitter by candidates, in particular their networking and micro–blogging activities in the election campaign for the European Parliament elections of 2009 in the Netherlands. The main focus is on identifying what political aspects (e.g., party characteristics and candidate characteristics) influences their use of Twitter as a campaign tool. Furthermore, we explore the effectiveness of candidates’ activities on Twitter in gaining votes.
(for full article, follow this link)

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.