Recently, I’ve been looking into analysis and visualization of Twitter networks. So, David Bauer posting a list of 300+ German-speaking, Twitter-using journalists came just right. Scroll down to see the resulting network. By they way, you can find more information on the technical background of the production of these Twitter network visualizations in this post.

[German]

In letzter Zeit habe ich an der Extraktion, Analyse und Visualisierung von Twitter-Netzwerken herumgepröbelt. Da kam es gerade recht, dass David Bauer eine Liste von über 300 deutschsprachigen, twitternden Journalistinnen und Journalisten gepostet hat. Hier präsentiere ich das resultierende Netzwerk. Achtung: Die resultierenden Darstellung sind optisch komplex und erfordern deshalb eine genügend grosse Auflösung. Die Grafiken sind also nicht ideal für die Betrachtung auf mobilen Geräten mit kleinen Bildschirmen.

Übrigens: In diesem Post finden Sie mehr Angaben zu den technischen Hintergründen der Erstellung dieser Twitter-Netzwerkvisualisierungen.

Netzwerk von Journalistinnen und Journalisten auf Twitter, Knotengrösse gemäss "Branchen-Followern" // Network of tweeting journalists, node size scaled according to fellow journalist followers

Die erste Grafik zeigt das Netzwerk der tweetenden Journalistinnen und Journalisten. Die Knoten sind nach Gruppen eingefärbt, die sich aus den Follower-Beziehungen ergeben. Die Knotengrösse richtet sich nach der Anzahl Follower des einzelnen Users unter den Journalistinnen und Journalisten.

Man sieht, dass die meisten Journalistinnen und Journalisten in einem grossen, aber kompakten Bereich des Netzwerks liegen, der aber in unterschiedliche Gruppen zerfällt. Der Urheber der Liste, David Bauer, ist gemessen an den Brachen-Followern sehr gut vernetzt. Er bildet zudem mit Ronnie Grob und Peter Hogenkamp eine Art Brückenkopf zur blauen Gruppe. Ich vermute, bei der blauen Gruppe handelt es sich um deutsche Journalistinnen und Journalisten. Sie sind nicht gut mit dem Rest des Netzwerks vernetzt.

Auf den ersten Blick sind T. Benkö, M. Binswanger, P. Müller, S. Brotz, N. Lüthi, W. de Schepper, S. Bärtschi, M. Daum, K. Weber, C. Moser und A. Sautter Journalistinnen und Journalisten mit vielen “Branchen-Followern”.

Erhellend ist auch die zweite Visualisierung, in der die Knoten anhand der Twitter-Follower (also nicht nur brancheninterne Follower) skaliert sind:

Netzwerk von Journalistinnen und Journalisten auf Twitter, Knotengrösse gemäss Twitter-Followern // Network of tweeting journalists, node size scaled according to total Twitter followers

Die zweite Visualisierung zeigt anhand der allgemeinen Follower auf Twitter, wer viele Follower ausserhalb des Journalismus anziehen kann. Natürlich ist das nicht ganz präzise, denn die Grafik zeigt einfach alle Follower, also:

• Follower auf der Liste (Journalistinnen und Journalisten) und
• Follower, die nicht auf der Liste figurieren. Diese können Journalistinnen/Journalisten sein oder auch nicht.

Die Visualisierung zeigt deutlich, dass zum Beispiel Nik Hartmann und Tom Brühwiler in dieser Interpretation mehr Gewicht erhalten, sie verfügen also über einen atypisch hohen Anteil von Nicht-JournalistInnen unter ihren Followern. Peter Hogenkamp, Thomas Benkö und Michèle Binswanger sind andere, die auch immer noch gross angezeigt werden. Es fällt weiter auf, dass die Knoten der blauen Gruppe besonders gross dargestellt sind – daher meine Vermutung, dass es sich hierbei um deutsche Journalistinnen und Journalisten handelt, die über einen grösseren Follower-Pool verfügen als die scheizerischen Kolleginnen und Kollegen.

Mich würde interessieren, was es sonst noch in diesen Darstellungen zu entdecken gibt. Was sehen InsiderInnen in diesen Darstellungen? Haben Sie Korrekturen? Was sind ihre Interpretationen und Hypothesen? Diskutieren Sie mit!

Recently, I’ve been working on a Twitter-related project with two friends of mine. As there’s nothing to present yet, I won’t go into detail regarding that project. But working on Twitter-related stuff led me to explore the generation, modelling, analysis and visualization of Twitter networks.

An excerpt from a Twitter network

Then, some weeks back, Swiss journalist/author/blogger David Bauer started a Google Doc to collect Twitter handles of journalists (read his post here, in German). Two weeks later David Bauer’s list featured 300 accounts from German-speaking, mostly Swiss journalists (as of now there are 360 accounts) – a nice crowdsourcing success!

I think David Bauer had an interesting idea there. And some people even took to simple analyses such as gender proportions of journalists on Twitter (see below – it’s disappointingly biased!).

Gender proportion of journalists using Twitter (based on David Bauer's list)

Now, I wanted to visualize the network of these tweeting journalists. The tools of trade in this case are:

• Python and some custom modules
• Twitter REST API and
• Gephi.

Using the API I could get the User IDs of each journalist (rather than the Twitter handles which can be changed, the User IDs are numerical, stable IDs) and the User IDs of the people who follow them as well as the people they follow. Obviously, as a side-product of this process I also got the current follower numbers for each journalist.

Now, all that was left to do to derive the Twitter network was to find for each pair of journalists, if one of them followed the other or both followed each other or no one followed the other. Using Python with custom modules I could generate this structure and export it to a GraphML file that can be read by Gephi. Using this programme I did some network analysis and created visualizations, check them out in this post.

[German]

In letzter Zeit bin ich daran, mit zwei Freunden eine Art Twitter-Projekt aufzubauen. Es gibt da aber noch nichts zu präsentieren, dieser Post handelt aber von einer Art Spin-Off-Projekt. Ich habe mich nämlich vermehrt für die Erzeugung und Modellierung, Analyse und Visualisierung von Twitter-Netzwerken interessiert und in diesen Bereichen einiges ausprobiert.

Auszug aus einem Twitter-Netzwerk

Dann hat der Schweizer Journalist/Autor/Blogger David Bauer vor ein paar Wochen ein Google Doc eröffnet, in dem er die Twitter-Handles deutschsprachicher (und mehrheitlich schweizerischen) Journalistinnen und Journalisten per Crowdsourcing sammelt (vgl. auch seinen Post hier). Zwei Wochen später zählte die Liste bereits 300 Einträge, aktuell sind es 360 – also ein schöner Crowdsourcing-Erfolg!

Ich finde David Bauers Idee sehr interessant. Auch andere Leute liessen sich davon begeistern und haben sogar einfache Analysen durchgeführt, zum Beispiel des Geschlechterverhältnisses (welches in enttäuschendem Mass unausgelichen ist):

Geschlechterverhältnis der twitternden Journalistinnen und Journalisten (basierend auf David Bauers Liste)

Ich für meinen Teil wollte das Netzwerk der twitternden Journalistinnen und Journalisten sehen. Die Tools, die ich dazu verwendet habe, sind:

• Python und einige spezielle Module
• die Twitter REST API und
• Gephi für die Visualisierung.

Von der Twitter API konnte ich die User-IDs aller Journalistinnen und Journalisten abrufen (diese numerischen IDs sind – anders als die veränderbaren Twitter-Handles – über die Zeit stabil). Weiter konnte ich die User-IDs der Leute abrufen, die einer Journalistin/einem Journalisten folgen bzw. denen eine Journalistin/ein Journalist folgt. Als ein Nebenprodukt erhielt ich dabei natürlich auch die Anzahl Follower jeder Journalistin und jedes Journalisten.

Als letzter Schritt zur Ableitung des Netzwerks von Journalistinnen und Journalisten musste ich für jedes Paar von Personen auf der Liste herausfinden, ob die eine der anderen folgt, sich beide gegenseitig folgen oder sich beide nicht folgen. Mit Python und einem speziellen Modul konnte ich dann das Netzwerk aufbauen und als eine GraphML-Datei exportieren. Diese wiederum konnte ich in Gephi einlesen, um weitere Analysen durchzuführen und einige Visualisierungen zu erstellen. Die Resultate finden Sie in diesem Post.

Africa Twitter map by Portland Communications, Economist

Mark Graham raises these interesting points regarding this map:

• 11m Tweets in Africa over a three months period is probably vastly underestimated, since the joint Portland Communications/Tweetminster analysis looked only at geocoded tweets.
• The analysis doesn’t account for the provencance of the tweets: are many of them issued by few users or are actually many people behind the many tweets of a country? This is likely a very relevant point, since it is found with many crowdsourcing projects that a small minority of the users contributes the majority of the content. It may be the same with Twitter, the only question which remains then is: could it be that the proportion of heavy contributors varies between countries (thus harming comparability of countries)
• The analysis doesn’t relate the number of tweets to the number of inhabitants. We have thus no way of knowing whether a big number of tweets means an extraordinarily high proportion of Twitter users in the population, or not.

Mark states that in a study conducted by him and his team using the Twitter Streaming API, it was found that only 0.7% of all tweets indeed contain geolocation information. (and thus the Africa Twitter map is based on a really small sample of the tweets which have been sent from within African countries!). That proportion was something I have wondered about since I have started to tinker with the Twitter REST API a few weeks ago. Other than the Streaming API (the so-called “firehose”), the REST API has tight query limits, so I haven’t acquired a big enough sample of tweets to actually make the judgment regarding the prevalence of location information in tweets (acquiring a random sample of tweets is also not the aim of my studies).

As Mark further points out this shortcoming on the data side makes the map potentially useless, in the worst case even misleading: Users in different countries may expose location in their tweets with different probabilities, due to for example:

• different brand mix of end user devices (for example, different prevalence of smartphones versus dumbphones (which can use Twitter via SMS)
• different mix of Twitter clients. Twitter clients may expose the location sharing settings in different ways and may rather encourage or discourage a user to opt into or out of location sharing
• varying awareness of, or views on, privacy issues around location sharing
• different societal norms towards location sharing

If the prevalence of location sharing is different in different countries, the Africa Twitter map cannot serve even as a proxy of the true numbers of Tweets sent from African countries.

Further takeaways thanks to Mark Graham:

• Using the location information in description fields of Twitter users’ profiles is a bad substitute for actual location information attached to tweets.
• Time zone information as another approach to rough positioning of a Twitter user isn’t a feasible alternative route either, since many users don’t bother to set it in their profile.
• And, most importantly and generally applicable: Any analysis of data from social media or crowdsourcing initiatives has to scrutinise the data for potential confounding variables, inherent biases, flaws in data collection (sampling), data processing and analysis. No analysis is complete without these questions asked – if they’re not clarified in the analysis, it’s the end user’s duty, though unfortunately it can be difficult without access to the raw data.

By the by, I also just learned about this nice five part tutorial by the Chicago Tribune which deals with TileMill and many other tools to create an attractive online map.

(via GIS Lounge)

Visurus: ZIPScribble Map Italy Level 3, alla Italiana

Those were inspired by the original ZIPScribble Maps by Robert Kosara. He’s back with an interactive version of many ZIPScribble Maps (interactive in this context meaning that you can pan and zoom the map).

Robert Kosara: Interactive ZIPScribble Maps of various countries

Unlike the earlier, non-interactive version, the new maps are built using Polymaps and employ Geonames datasets like my ZIPScribble Maps. In the case of Switzerland the latter improves the quality of the map considerably. Like in the earlier versions, the interactive maps can also be coloured by state or administrative region. Thus, these ZIPScribble maps deviate from other implementations (including mine) which use the actual post codes to colour the ZIPScribbles (and in that way try to elucidate the numerical pattern of post codes of a country).

Borderlines writes about interesting stories around country borders. So far, I’ve read the superbly entertaining and well informed “Where is Europe?“, which deals with the problems geographers face(d) with regards to defining the geographic extent of “Europe”. It’s all there: the perspectives of the Britons, Swiss, Croats and Eurocrats, Turks, Russians, …, the back and forth of the European boundaries, especially (but not only) those in the east and south and some surprises, even for geographers.

There's a plethora of pitfalls when dealing with this beast... (NY Times)

Is this an off-topic post on my blog?

Eh, depends: because, here we have great examples of the fiat and bona fide divide when it comes to geographic regions and objects, the fuzziness or spatial vagueness of toponyms and the regions they denote, qualitative spatial reasoning, vernacular geography, etc. – some of which I have dealt with also in my PhD thesis and which also pertain to stuff you do with GIS, spatial analysis and geovisualization.

Very interesting stuff! But for now I’ll spare you the details and recommend ”Where is Europe?” and Borderlines.

made me chuckle.

There is indeed a plethora of badly designed infographics out there (often these bad specimens are images 300 px wide and 3000 px high). Honestly, I have my doubts about this format anyway: e.g. I think infographics often don’t give enough context to the statistics they depict to do a topic justice. Rather they go for spectacular numbers. When these come along in a too isolated manner, they may still manage to impress, but they lose their informative value. To make things worse, infographics are sometimes created as link bait in an SEO strategy.

I wonder: Is there or will there ever be an infographic about the rise and demise of infographics on the web? – Infographics in an eternal loop.

Brain. Hurts. Must. Stop. Now.

(via Yes Machine)

For simplicity, I will re-list Cédric’s 13 points here:

1. Because it’s not possible to make a map for all zoom levels
2. Because the finances are not secured on long term
3. Because the data model is not defined
4. Because the precision is heterogeneous
5. Because the reliability is heterogeneous
6. Because the completeness is heterogeneous
7. Because it requires attribution
8. Because the data are difficult to extract
9. Because noone takes the responsibility about the data
10. Because it lacks a QA step by an accountable body
11. Because it is not multilingual
12. Because first acquisition is fun and data update is boring
13. Because Google Map Maker Workflow is for the broad public and OpenStreetMap workflow for the map enthusiasts

Numerous reactions (not further detailed) motivated Cédric to post some clarifications.

Enters Stefan Keller. He is a professor at the Hochschule für Technik in Rapperswil (Switzerland) and (I think) could be described as an open source and OSM enthusiast and evangelist. In agreement with Cédric Moullet, Stefan Keller started a thread on the Swiss GIS/geospatial industry forum geowebforum. In his post he objects to various of Cédric’s points. Marc Wick (founder of Geonames) also weighs in on the debate with some interesting points and finally, Stephan Heuel and I, also contribute our view of the topic (spoiler: we agree with most of Cédric’s points).

Of course, I’m biased, but I think the thread which developed is definitely worth reading. If you feel like it, please do also contribute (everybody can on geowebforum) with your insights!

If you are a cartography buff, you have to check out this offer. Topics span:

• Cartography in Prehistoric, Ancient, and Medieval Europe and the Mediterranean
• Cartography in the Traditional Islamic and South Asian Societies
• Cartography in the Traditional East and Southeast Asian Societies
• Cartography in the Traditional African, American, Arctic, Australian, and Pacific Societies

(via All Points Blog)