Liebe Twitter-Nutzerin,
Lieber Twitter-Nutzer,

Ich bin Sprachwissenschaftler an der Universität Düsseldorf und beschäftige mich schwerpunktmäßig mit Internetkommunikation. Als Teil der Studie “Aspekte privater Twitter-Kommunikation” möchte die Nutzungsgewohnheiten von deutschsprachigen Twitter-Nutzern untersuchen, die Twitter nicht ausschließlich beruflich einsetzen (im Gegensatz zu z.B. Journalisten, Wissenschaftlern, Politikern, und anderen Menschen in Kommunikationsberufen). Zu diesem Zweck würde ich gerne deine öffentlichen Tweets einen Monat lang aufzeichnen und auswerten. Anschließend würde ich dir gerne per Mail einige Fragen (nicht mehr als 10) zu deiner Twitter-Nutzung stellen.

Es werden ausschließlich öffentliche Tweets (also keine DMs) aufgezeichnet. Sämtliche Daten werden anonymisiert (d.h. Namen — auch Twitter-Nicknames — entfernt) und nicht an Dritte weitergegeben. Einzelne Tweets können über das Hashtag #exclude jeder Zeit aus der Aufzeichnung ausgeschlossen werden. Am Ende des Untersuchungszeitraum schicke ich dir bei Interesse gerne ein Archiv deiner aufgezeichneten Tweets zu.

Neben deinem Beitrag zur wissenschaftlichen Forschung winkt auch eine (kleine) Aufwandsentschädigung: ich verlose am Ende des Untersuchungszeitraum unter den Teilnehmern einen Amazon-Gutschein im Wert von 50 Euro. :-)

Wenn du zu einer Teilnahme bereit bist, schicke bitte eine kurze Mail an (Edit: natürlich kannst du dich auch per Twitter melden). Falls du nicht teilnehmen möchtest, musst du nichts weiter tun. Fragen zur Studie beantworte ich gerne per Mail.

Schon jetzt vielen Dank für dein Interesse und deine Unterstützung!

Dr. Cornelius Puschmann
Nachwuchsforschergruppe “Wissenschaft und Internet”
Heinrich-Heine-Universität Düsseldorf

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As part of the research we’re doing in Düsseldorf on the use of Twitter at academic conferences, here’s a poster we’re presenting in a few days at GOR ’11:

Here’s the citation for the poster:

Puschmann, C., Weller, K., & Dröge, E. (2011). Studying Twitter conversations as (dynamic) graphs: visualization and structural comparison. Presented at General Online Research, 14-16 March 2011, Düsseldorf, Germany. Retrieved from

See this older post for more information on how to visualize dynamic graphs of retweets with Gephi.

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Academic replacements for

On February 23, 2011, in Thoughts, by cornelius

Update: I’ve written a follow-up to this post.

A few days ago, the people behind Twitter archival site announced that they will be discontinuing the export feature of the service on March 20, 2011. Apparently the feature is in violation of Twitter’s terms of service, at least in the form it’s currently implemented in TwapperKeeper.

Unfortunately this cuts off a number of academics who are investigating communication on Twitter for scientific purposes from a convenient data source. While it’s fairly easy to get data directly via the Twitter API (which is what TwapperKeeper was doing), I know many people who want to concentrate on the data itself, rather than running their own servers to scrape Twitter on a regular basis. What’s more is that Twitter’s attitude is worrisome: many of us have tried to get an exemption from API rate limits in the past, to no avail. Twitter doesn’t give researchers privileged access to their data, and now they’re crippling TwapperKeeper on top of that.

Bottom line: what will we use after March 20? Ideally, a replacement would provide the following:

  • the hashtag/search query functionality of TwapperKeeper,
  • the export functionality of TwapperKeeper,
  • exclusive use for academic purposes (on the grounds that this might keep Twitter from shutting it down),
  • stability and reliability,
  • long-term viability.

The last point is important, because I don’t think it will be difficult to set up a server somewhere to suit the needs of a few people, but a larger-scale solution seems more sensible in the long run. Maybe JISC can do something like that, based on yourTwapperKeeper (which they supported)? Or one of the big institutes (OII, Berkman)? Either way it would be nice to find an alternative that doesn’t give those of us with devs and major IT support behind them a huge edge over the rest…

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Thanks to Lambert for pointing out this highly recommended piece by danah boyd to me. I like it so much that I’ve decided to assemble some favorite quotes.

On interpreting (big) quantitative social science data:

“Just because you see traces of data doesn’t mean you always know the intention or cultural logic behind them. And just because you have a big N doesn’t mean that it’s representative or generalizable.”

“Many computational scientists believe that because they have large N data that they know more about people’s practices than any other social scientist. Time and time again, I see computational scientists mistake behavioral traces for cultural logic.”

“Big Data is going to be extremely important but we can never lose track of the context in which this data is produced and the cultural logic behind its production.”

On interdisciplinarity and methods:

“Each methodology has its strength and weaknesses. Each approach to data has its strengths and weaknesses. Each theoretical apparatus has its place in scholarship. And one of the biggest challenges in doing “interdisciplinary” work is being about to account for these differences, to know what approach works best for what question, to know what theories speak to what data and can be used in which ways.”

Which is why working in interdisciplinary teams where people really listen to each other is so important. Which is why learning beyond gradschool is so important.

On funding agencies and interdisciplinarity:

“I actually think that the funding agencies are going to play a huge role in this, not just in demanding cross-disciplinary collaboration, but in setting the stage for how research will be published.”

This is an important point — and one where I wonder whether the situation over here in Germany isn’t more difficult than in the U.S. Funding agencies over here are incredibily reluctant to make demands to researchers. This has both upsides and downsides, a downside being that there are fewer incentives to cooperate.

On social scienctists and computational scientists joining forces to approach Big Data:

“[..]every discipline has its arrogance and far too many scholars think that they know everything. We desperately need a little humility here.”

Amen. And, interestingly enough, I sense a connection between danah’s argument and Frank Schirrmacher’s views:

Die Informatiker müssen aus den Nischen in die Mitte der Gesellschaft geholt werden. Sie müssen die Scripts erklären, nach denen wir handeln und bewertet werden. Was ist voraussagende Suche und was kann sie? Was ist „profiling“? Wer liest uns, während wir lesen? Technologien sind neutral, es kommt darauf an, wie wir sie benutzen. Um das zu können, brauchen wir Dolmetscher aus der technologischen Intelligenz.

Interestingly enough, danah is the one who’s more critical. Schirrmacher (who isn’t talking about Big Data, but about digital technology in general and about it’s impact on society) demands that computational scientists explain their code to the public — what ranking algorithms do and how context-sensitive ads work. danah criticizes drawing conclusions from automated computational analysis without taking other methods into account. If we start out with simplistic assumptions (e.g. “the people we spend the most time with are the ones closest to us”) we are prone to drawing entirely wrong conclusions, even if our data is beautifully modeled.

I could go on and on here why danah is spot-on here, but instead I’ll just point to the piece itself again.

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