Category Archives: softwareproject
The New York Times is not only a very good newspaper, it is also a really, really interesting archive that provides search access to all articles since 1851 via a pretty nice API. I’ve been meaning to play with it for some time, but things were extremely busy this year. But yesterday, I had some time in the evening and looked into the system a little bit and wrote a couple of scripts to try out some quick ideas.
While the API has all kinds of interesting things – in particular access to the Times’ controlled vocabulary – I am most interested in the article archive and the different possibilities to explore it. Understandably, the API does not provide the full text of articles; but it does search in the full text and for every found article it delivers quite a number of interesting things. Here is an example of what the returned data for a query (“guantanamo bay”) looks like:
While there are many things to go with, I found the manually attributed (and controlled) keywords to be particularly interesting. So I decided to explore and visualize how a particular subject evolves over time inside of this classificatory structure. Because the request rate for the search API is quite generous (10/s, 10K/day) I wrote a short PHP script (grab.php) that grabs this metadata for every article corresponding to a given search query. It simply downloads the data and stores it in a bunch of JSON files. A second script (analyze.php) then parses these files and creates a simple CSV file that can then be visualized with something like R (which I started working with some weeks ago, much easier than I thought, lots of fun).
With the help of the amazing ggplot2 library in R, using “guantanamo bay” as query, I quickly got a first result (click for larger image):
One can quite easily see that Guantanamo Bay was discussed in the 1990s in terms of immigration, asylum, and similar terms, while the current frame (terrorism, etc.) appears just after 9/11. While this script (bubbles.R) provides overview, a second one (bubbles_numbers.R) provides a combination of bubbles and numbers (click for larger image):
There is certainly much more interesting stuff to do with this data (e.g. different types of normalization, taking into account word count and page number, etc.) and I’ll hopefully come back to this in more detail in the future. In the meantime, all scripts can be found here.
Update June 2, 2013:
I’ve added a network export feature to the scripts on github. Generated network files are not limited to subject tags, but include people, organizations, locations, and creative works (e.g. books or movies). If two tags appear on the same article, a link is created and the more often they appear together, the stronger the connection. Here’s a quick visualization, made with gephi, of the most common people (red), organizations (green), and locations (blue) for the query “climate change” (click for larger image):
Netvizz, a Facebook research app for extracting data from the dominant social networking service, has gained a new feature: page exploration. While the app has been able to get ego-networks and group networks from the start, this is the first time that data for pages can be extracted as well. The Social Network Importer for NodeXL already allows for extracting both co-engagement (users that comment or like the same post are connected) and bipartite networks (both posts and users are in the graph) from Facebook pages but requires you to use NodeXL and Microsoft Office on Windows.
The first implementation of page exploration on netvizz only provides bipartite network files only and yields less data on users, but adds information on the page posts themselves and outputs them both as a graph file and a simple tab-separated text file. For the moment, the app captures a user specified number of posts from the page and loads up to 1000 comments and 1000 likes. It also specifies the type of post in both of the files it generates. This is the (edgeless) network created from the last 100 posts of the New York Times Facebook page:
Users are gray, videos are blue, links are red, photos are yellow and status updates are green. Size is engagement. Because distance from the center indicates stronger engagement from non regular users, one can easily see that both photos and status updates are engaging a different audience than the links and videos.
Visualizing the data from the tsv file, we can explore these kind of relations further. Here, I used Mondrian‘s capacity to show highlights in one chart on all other open charts:
By selecting photos in the barchart, the scatterplot (x: likes, y: comments) shows that photos not only produce much higher engagement scores (the engagement value in both the tsv and gdf files combines numbers of likes, comments, shares, and likes for comments into a single metric) – the median for links is 453, but 1724 for photos – but that there is also a tendency for photos to provoke a comment/like ratio that trends toward the former. This is data from about 10 days of activity, so not suited to make any larger claims – interesting nonetheless.
As already mentioned here, the next step is to produce network files for multiple pages.
In my last post, I previewed a feature that I am currently building into netvizz: posts and users that comment and like them are thrown together into a bipartite graph. In this approach, it is easy to combine data from different pages, here from the 30 latest posts of the New York Times and the Wall Street Journal, plotting 27K users (bigger image behind the click):
The app will start spitting out more metrics in the next version, but it’s easy to see from the gephi graph that the NY Times (red) has a bit more users (grey) than the WSJ (blue). There is a bit of overlap in terms of (active) audience, but in general, there seem to be quite distinct populations of the short span the data covers. Interestingly, one post – talking about the space shuttle Endeavor – is a true outlier: it has succeeded in capturing a less “specific” audience.
As this method could be applied to a potentially infinite number of pages, this is really becoming quite problematic in terms of privacy. I have cut the labels for users, but they are in the data. I am unsure about this for the moment, but this feature may not make it in full into the next version.
Pages are part of Facebook’s project to suck up the Web. They are also full of data. In the next version of netvizz I will add a feature that allows to dig into that data a little bit. Here is a preview:
This network (visualized with gephi) shows interactions on the Facebook page of the Guardian. I extracted all the likes and comments for the last 80 posts. On the whole, there are 9.500 users liking and commenting away. Each dark and labeled node is a post while all the others are users. A heat scale (blue => yellow => red) shows how often a user interacts with the page; size shows how often a node was liked or commented on (for pages) or liked and commented (for users).
One can see a a core of regulars in the middle of the graph, but the main engagement comes from a large majority of users that have only interacted with a single posts. These users drag the big subjects out to the margins in this specific spatialization. Engagement, here, comes from a fleeting audience rather than a more stable group or community.
There is still some testing to do, but I hope to get this feature ready soon for general use.
I am sick this weekend and that’s a justification to stay in bed and play around with the computer a bit. Over these last weeks, I was thinking that it may be interesting to get back to the aging netvizz application and make some direly needed revisions and updates, especially concerning some of the quantitative measures concerning individual users’ activity. Maintenance work is not fun, however, so I decided to add a new feature instead: the bipartite like network.
The idea is pretty simple: instead of graphing friend relationships between users, the new output basically just throws users and likes (liked pages that is – external objects are not available through the API) into the same graph. If a user likes something a link is created. That’s also how Facebook’s opengraph architecture works on the inside. The result – done with gephi – is pretty interesting though (click for bigger image):
The small turquoise dots are users and the bigger red ones liked objects. I eliminated users that did not like anything (or have strong privacy settings), as well as all things liked by a single person only. The data field “likesize” in the output file indicates how often an object has been liked and makes it possible to size likes separately from users (the “type” field distinguishes the two). It is not surprising that, at least in my case, the network of friendship connections and the like network are quite similar. People from Austria do not like the same things as my French friends – although there is a cluster of international stuff in the middle: television shows, music, wikileaks, and so on; these things cannot be clearly attributed to a user group.
One can actually use the same output file for something quite different. The next image shows the same graph but with nodes sized for number of connections (degree). This basically shows the biggest “likers” (anonymized for the purpose of this post) in the network and still keeps the grouped by similar like patterns.
The new feature is already live and can be tried out. If you want to do more than make pretty pictures, I highly recommend checking out the work by my colleagues Carolin Gerlitz and Anne Helmond on what they call the “like economy”.
And now back to bed.
When it comes to analyzing and visualizing data as a graph, we most often select only one unit to represent nodes. When working with social networks, nodes commonly represent user accounts. In a recent post, I used Twitter hashtags instead and established links by looking at which hashtags occurred in the same tweets. But it is very much possible to use different “ontological” units in the same graph. Consider this example from the IPRI project (a click gives you the full map, a 14MB png file):
Here, I decided to mix Twitter user accounts with hashtags. To keep things manageable, I took only the accounts we identified as journalists that posted at least 300 tweets between February 15 and April 15 from the 25K accounts we follow. For every one of those accounts, I queried our database for the 10 hashtags most often tweeted by the user. I then filtered the graph to show only hashtags used by at least two users. I was finally left with 512 user accounts (the turquoise nodes, size is number of tweets) and 535 hashtags (the red nodes, size is frequency of use). Link strength represents the frequency with which a user tweeted a hashtag. What we get, is still a thematic map (libya, the regional elections, and japan being the main topics), but this time, we also see, which users were most strongly attached to these topics.
Mapping heterogeneous units opens up many new ways to explore data. The next step I will try to work out is using mentions and retweets to identify not only the level of interest that certain accounts accord to certain topics (which you can see in the map above), but the level of echo that an account produces in relation to a certain topic. We’ll see how that goes.
In completely unrelated news, I read an interesting piece by Rocky Agrawal on why he blocked tech blogger Robert Scoble from his Google+ account. At the very end, he mentions a little experiment that delicious.com founder Joshua Schachter did a couple of days ago: he asked his 14K followers on Twitter and 1.5K followers on Google+ to respond to a post, getting 30 answers the former and 42 from the latter. Sitting on still largely unexplored bit.ly click data for millions of urls posted on Twitter, I can only confirm that Twitter impact may be overstated by an order of magnitude…
There are many different ways of making sense of large datasets. Using network visualization is one of them. But what is a network? Or rather, which aspects of a dataset do we want to explore as a network? Even social services like Twitter can be graphed in many different ways. Friend/follower connections are an obvious choice, but retweets and mentions can be used as well to establish links between accounts. Hashtag similarity (two users who share a tag are connected, the more they share, the closer) is yet another method. In fact, when we shift from interactions to co-occurrences, many different things become possible. Instead of mapping user accounts, we can, for example, map hashtags: two tags are connected if they appear in the same tweet and the number of co-occurrences defines link strength (or “edge weight”). The Mapping Online Publics project has more ideas on this question, including mapping over time.
In the context of the IPRI research project we have been following 25K Twitter accounts from the French twittersphere. Here is a map (size: occurrence count / color: degree / layout: gephi with OpenOrd) of the hashtag co-occurrences for the 10.000 hashtags used most often between February 15 2011 and April 15 2011 (clicking on the image gets you the full map, 5MB):
The main topics over this period were the regional elections (“cantonales”) and the Arab spring, particularly the events in Libya. The japan earthquake is also very prominent. But you’ll also find smaller events leaving their traces, e.g. star designer Galliano’s antisemitic remarks in a Paris restaurant. Large parts of the map show ongoing topics, cinema, sports, general geekery, and so forth. While not exhaustive, this map is currently helping us to understand which topics are actually “inside” our dataset. This is exploratory data analysis at work: rather than confirming a hypothesis, maps like this can help us get a general understanding of what we’re dealing with and then formulate more precise questions from there.
I just saw that the good people from sociomatic have prepared a nice little slideshow on how to use gephi to analyze social network data extracted from Facebook (using netvizz). This is a great way to start playing around with network analysis and the slides should really help with the first couple of steps…
The Association of Internet Researchers (AOIR) is an important venue if you’re interested in, like the name indicates, Internet research. But it is also a good primary source if one wants to inquire into how and why people study the Internet, which aspects of it, etc. Conveniently for the lazy empirical researcher that I am, the AOIR has an archive of its mailing-list, which has about 22K mails posted by 3K addresses, enough for a little playing around with the impatient person’s tool, the algorithm. I have downloaded the data and I hope I can motivate some of my students to build something interesting with it, but I just had to put it into gephi right away. Some of the tools we’ll hopefully build will concentrate more on text mining but using an address as a node and a mail-reply relationship as a link, one can easily build a social graph.
I would like to take this example as an occasion to show how different algorithms can produce quite different views on the same data:
So, these are the air-l posters with more than 60 messages posted since 2001. Node size indicates the number of posts, a node’s color (from blue to red) shows its connectivity in the graph (click on the image to see a much larger version). Link strength, i.e. number of replies between two people, is taken into account. You can download the full .gdf here. The only difference between the four graphs is the layout algorithm used (Force Atlas, Force Atlas with attraction distribution, Yifan Hu, and Fruchterman Reingold). You can instantly notice that Yifan Hu pushes nodes with low link count much more strongly to the periphery than the others, while Fruchterman Reingold as always keeps its symmetrical sphere shape, suggesting a more harmonious picture than the rest. Force Atlas’ attraction distribution feature will try to differentiate between hubs and authorities, pushing the former to the periphery while keeping the latter in the center; just compare Barry Wellman’s position over the different graphs.
I’ll probably repeat this experiment with a more segmented graph, but I think this already shows that layout algorithms are not just innocently rendering a graph readable. Every method puts some features of the graph to the forefront and the capacity for critical reading is as important as the willingness for “critical use” that does not gloss over the differences in tools used.
When it comes to search interfaces, there are a lot of good ideas out there, but there is also a lot of potential for further experimentation. Search APIs are a great field for experimentation as they allow developers to play around with advanced functionality without forcing them to work on a heavy backend structure.
Together with Alex Beaugrand, a student of mine, I have built (a couple of month ago) another little search mashup / interface that allows users to switch between a tag cloud view and a list / cluster mode. contextDigger uses the delicious and Bing APIs to widen the search space using associated searches / terms and then Yahoo BOSS to download a thousand results that can be filtered through the interface. It uses the principle of faceted navigation to shorten the list : if you click on two terms, only the results associated with both of them will appear…