Marten Düring, an altogether wonderful researcher who is responsible for this brilliant bibliography of networks in history, has issues a call for papers to participate in this year’s Sunbelt Conference, which is one of the premier social network analysis conferences in the world.
Call for papers “Historical Network Research” at the XXXIII. Sunbelt Conference, May 21-26 – University of Hamburg, Germany
The concepts and methods of social network analysis in historical research are recently being used not only as a mere metaphor but are increasingly applied in practice. In the last decades several studies in the social sciences proved that formal methods derived from social network analysis can be fruitfully applied to selected bodies of historical data as well. These studies however tend to be strongly influenced by concerns, standards of data processing, and, above all, epistemological paradigms that have their roots in the social sciences. Among historians, the term network has been used in a metaphorical sense alone for a long time. It was only recently that this has changed.
We invite papers which successfully integrate social network analysis methods and historical research methods and reflect on the added value of their methodologies. Topics could cover (but are not limited to) network analyses of correspondences, social movements, kinship or economic systems in any historical period.
Submission will be closing on December 31 at 11:59:59 EST. Please limit your abstract to 250 words. Please submit your abstract here: http://www.abstractserver.com/sunbelt2013/absmgm/
and select “Historical Network Research” as session title in the drop down box on the submission site. Please put a note in the “additional notes” box on the abstract submission form that states Marten During and Martin Stark as the session organizers.
For further information on the venue and conference registration see: http://hamburg-sunbelt2013.org/, for any questions regarding the panel, please get in touch with the session organizers.
Marten During, Radboud University Nijmegen, email@example.com
Martin Stark, University of Hamburg, firstname.lastname@example.org
Early modern history! Science! Letters! Data! Four of my favoritest things have been combined in this brand new beta release of Early Modern Letters Online from Oxford University.
EMLO (what an adorable acronym, I kind of what to tickle it) is Oxford’s answer to a metadata database (metadatabase?) of, you guessed it, early modern letters. This is pretty much a gold standard metadata project. It’s still in beta, so there are some interface kinks and desirable features not-yet-implemented, but it has all the right ingredients for a great project:
Information is free and open; I’m even told it will be downloadable at some point.
The interface is fast, easy, and includes faceted browsing.
Has a fantastic interface for adding your own data.
Actually includes citation guidelines thank you so much.
Visualizations for at-a-glance understanding of data.
Links to full transcripts, abstracts, and hard-copies where available.
Lots of other fantastic things.
Sorry if I go on about how fantastic this catalog is – like I said, I love letters so much. The index itself includes roughly 12,000 people, 4,000 locations, 60,000 letters, 9,000 images, and 26,000 additional comments. It is without a doubt the largest public letters database currently available. Between the data being compiled by this group, along with that of the CKCC in the Netherlands, the Electronic Enlightenment Project at Oxford, Stanford’s Mapping the Republic of Letters project, and R.A. Hatch‘s research collection, there will without a doubt soon be hundreds of thousands of letters which can be tracked, read, and analyzed with absolute ease. The mind boggles.
Bodleian Card Catalogue Summaries
Without a doubt, the coolest and most unique feature this project brings to the table is the digitization of Bodleian Card Catalogue, a fifty-two drawer index-card cabinet filled with summaries of nearly 50,000 letters held in the library, all compiled by the Bodleian staff many years ago. In lieu of full transcriptions, digitizations, or translations, these summary cards are an amazing resource by themselves. Many of the letters in the EMLO collection include these summaries as full-text abstracts.
The collection also includes the correspondences of John Aubrey (1,037 letters), Comenius (526), Hartlib (4,589 many including transcripts), Edward Lhwyd (2,139 many including transcripts), Martin Lister (1,141), John Selden (355), and John Wallis (2,002). The advanced search allows you to look for only letters with full transcripts or abstracts available. As someone who’s worked with a lot of letters catalogs of varying qualities, it is refreshing to see this one being upfront about unknown/uncertain values. It would, however, be nice if they included the editor’s best guess of dates and locations, or perhaps inferred locations/dates from the other information available. (For example, if birth and death dates are known, it is likely a letter was not written by someone before or after those dates.)
In the interest of full disclosure, I should note that, much like with the CKCC letters interface, I spent some time working with the Cultures of Knowledge team on visualizations for EMLO. Their group was absolutely fantastic to work with, with impressive resources and outstanding expertise. The result of the collaboration was the integration of visualizations in metadata summaries, the first of which is a simple bar chart showing the numbers of letters written, received, and mentioned in per year of any given individual in the catalog. Besides being useful for getting an at-a-glance idea of the data, these charts actually proved really useful for data cleaning.
Because I can’t do anything with letters without looking at them as a network, I decided to put together some visualizations using Sci2 and Gephi. In both cases, the Sci2 tool was used for data preparation and analysis, and the final network was visualized in GUESS and Gephi, respectively. The first graph shows network in detail with edges, and names visible for the most “central” correspondents. The second visualization is without edges, with each correspondent clustered according to their place in the overall network, with the most prominent figures in each cluster visible.
The graphs show us that this is not a fully connected network. There are many islands of one or two letters or a small handful of letters. These can be indicative of a prestige bias in the data. That is, the collection contains many letters from the most prestigious correspondents, and increasingly fewer as the prestige of the correspondent decreases. Put in another way, there are many letters from a few, and few letters from many. This is a characteristic shared with power law and other “long tail” distributions. The jumbled community structure at the center of the second graph is especially interesting, and it would be worth comparing these communities against institutions and informal societies at the time. Knowledge of large-scale patterns in a network can help determine what sort of analyses are best for the data at hand. More on this in particular will be coming in the next few weeks.
It’s also worth pointing out these visualizations as another tool for data-checking. You may notice, on the bottom left-hand corner of the first network visualization, two separate Edward Lhwyds with virtually the same networks of correspondence. This meant there were two distinct entities in their database referring to the same individual – a problem which has since been corrected.
Notice that the EMLO site makes it very clear that they are open to contributions. There are many letters datasets out there, some digitized, some still languishing idly on dead trees, and until they are all combined, we will be limited in the scope of the research possible. We can always use more. If you are in any way responsible for an early-modern letters collection, meta-data or full-text, please help by opening that collection up and making it integrable with the other sets out there. It will do the scholarly world a great service, and get us that much closer to understanding the processes underlying scholarly communication in general. The folks at Oxford are providing a great example, and I look forward to watching this project as it grows and improves.
Last post, I talked about combining textual and network analysis. Both are becoming standard tools in the methodological toolkit of the digital humanist, sitting next to GIS in what seems to be becoming the Big Three in computational humanities.
Data as Context, Data as Contextualized
Humanists are starkly aware that no particular aspect of a subject sits in a vacuum; context is key. A network on its own is a set of meaningless relationships without a knowledge of what travels through and across it, what entities make it up, and how that network interacts with the larger world. The network must be contextualized by the content. Conversely, the networks in which people and processes are situated deeply affect those entities: medium shapes message and topology shapes influence. The content must be contextualized by the network.
At the risk of the iPhonification of methodologies 1, textual, network, and geographic analysis may be combined with each other and traditional humanities research so that they might all inform one another. That last post on textual and network analysis was missing one key component for digital humanities: the humanities. Combining textual and network analysis with traditional humanities research (rather than merely using the humanities to inform text and network analysis, or vice-versa) promises to transform the sorts of questions asked and projects undertaken in Academia at large.
Just as networks can be used to contextualize text (and vice-versa), the same can be said of networks and maps (or texts and maps for that matter, or all three, but I’ll leave those for later posts). Now, instead of starting with the maps we all know and love, we’ll start by jumping into the deep end by discussing maps as any sort of representative landscape in which a network can be situated. In fact, I’m going to start off by using the network as a map against which certain relational properties can be overlaid. That is, I’m starting by using a map to contextualize a network, rather than the more intuitive other way around.
Using Maps to Contextualize a Network
The base map we’re discussing here is a map of science. They’ve made their rounds, so you’ve probably seen one, but just in case you haven’t here’s a brief description: some researchers (in this case Kevin Boyack and Richard Klavans) take tons on information from scholarly databases (in this case the Science Citation Index Expanded and the Social Science Citation Index) and create a network diagram from some set of metrics (in this case, citation similarity). They call this network representation a Map of Science.
We can debate about the merits of these maps ’till we’re blue in the face, but let’s avoid that for now. To my mind, the maps are useful, interesting, and incomplete, and the map-makers are generally well-aware of their deficiencies. The point here is that it is a map: a landscape against which one can situate oneself, and with which one may be able to find paths and understand the lay of the land.
In Boyack, Börner 2, and Klavans (2007), the three authors set out to use the map of science to explore the evolution of chemistry research. The purpose of the paper doesn’t really matter here, though; what matters is the idea of overlaying information atop a base network map.
The images above are the funding profiles of the NIH (National Institutes of Health) and NSF (National Science Foundation). The authors collected publication information attached to all the grants funded by the NSF and NIH and looked at how those publications cited one another. The orange edges show connections between disciplines on the map of science that were more prevalent within the context a particular funding agency than they were compared to the entire map of science. Boyack, Börner 3, and Klavans created a map and used it to contextualize certain funding agencies. They and other parties have since used such maps to contextualize universities, authors, disciplines, and other publication groups.
From Network Maps to Geographic Maps
Of course, the Where’s The Beef™ section of this post still has yet to be discussed, with the beef in this case being geography. How can we use existing topography to contextualize network topology? Network space rarely corresponds to geographic place, however neither of them alone can ever fully represent the landscape within which we are situated. A purely geographic map of ancient Rome would not accurately represent the world in which the ancient Romans lived, as it does not take into account the shortening of distances through well-trod trade routes.
Enter Stanford DH ninja Elijah Meeks. In two recent posts, Elijah discussed the topology/topography divide. In the first, he created a network layout algorithm which took a network with nodes originally placed in their geographic coordinates, and then distorted the network visualization to emphasize network distance. The visualization above shows the network laid out geographically. The one below shows the Imperial Roman trade routes with network distances emphasized. As Elijah says, “everything of the same color in the above map is the same network distance from Rome.”
Of course, the savvy reader has probably observed that this does not take everything into account. These are only land routes; what about the sea?
Elijah’s second post addressed just that, impressively applying GIS techniques to determine the likely route ships took to get from one port to another. This technique drives home the point he was trying to make about transitioning from network topology to network topography. The picture below, incidentally, is Elijah’s re-rendering of the last visualization taking into account both land and see routes. As you can see, the distance from any city to any other has decreased significantly, even taking into account his network-distance algorithm.
The above network visualization combines geography, two types of transportation routes, and network science to provide a more nuanced at-a-glance view of the Imperial Roman landscape. The work he highlighted in his post transitioning from topology to topography in edge shapes is also of utmost importance, however that topic will need to wait for another post.
The Republic of Letters (A Brief Interlude)
Elijah was also involved in another Stanford-based project, one very dear to my heart, Mapping the Republic of Letters. Much of my own research has dealt with the Republic of Letters, especially my time spent under Bob Hatch, and Paula Findlen, Dan Edelstein, and Nicole Coleman at Stanford have been heading up an impressive project on that very subject. I’ll go into more details about the Republic in another post (I know, promises promises), but for now the important thing to look at is their interface for navigating the Republic.
The team has gone well beyond the interface that currently faces the public, however even the original map is an important step. Overlaid against a map of Europe are the correspondences of many early modern scholars. The flow of information is apparent temporally, spatially, and through the network topology of the Republic itself. Now as any good explorer knows, no map is any substitute for a thorough knowledge of the land itself; instead, it is to be used for finding unexplored areas and for synthesizing information at a large scale. For contextualizing.
If you’ll allow me a brief diversion, I’d like to talk about tools for making these sorts of maps, now that we’re on the subject of letters. Elijah’s post on visualizing network distance included a plugin for Gephi to emphasize network distance. Gephi’s a great tool for making really pretty network visualizations, and it also comes with a small but potent handful of network analysis algorithms.
I’m on the development team of another program, the Sci² Tool, which shares a lot of Gephi’s functionality, although it has a much wider scope and includes algorithms for textual, geographic, and statistical analysis, as well as a somewhat broader range of network analysis algorithms.
This is by no means a suggestion to use Sci² over Gephi; they both have their strengths and weaknesses. Gephi is dead simple to use, produces the most beautiful graphs on the market, and is all-around fantastic software. They both excel in different areas, and by using them (and other tools!) together, it is possible to create maps combining geographic and network features without ever having to resort to programming.
The above image was generated by combining the Sci² Tool with Gephi. It is the correspondence network of Hugo Grotius, a dataset I worked on while at Huygens ING in The Hague. They are a great group, and another team doing fantastic Republic of Letters research, and they provided this letters dataset. We just developed this particular functionality in Sci² yesterday, so it will take a bit of time before we work out the bugs and release it publicly, however as soon as it is released I’ll be sure to post a full tutorial on how to make maps like the one above.
This ends the public service announcement.
These maps are not without their critics. Especially prevalent were questions along the lines of “But how is this showing me anything I didn’t already know?” or “All of this is just an artefact of population densities and standard trade routes – what are these maps telling us about the Republic of Letters?” These are legitimate critiques, however as mentioned before, these maps are still useful for at-a-glance synthesis of large scales of information, or learning something new about areas one is not yet an expert in. Another problem has been that the lines on the map don’t represent actual travel routes; those sorts of problems are beginning to be addressed by the type of work Elijah Meeks and other GIS researchers are doing.
To tackle the suggestion that these are merely representing population data, I would like to propose what I believe to be a novel idea. I haven’t published on this yet, and I’m not trying to claim scholarly territory here, but I would ask that if this idea inspires research of your own, please cite this blog post or my publication on the subject, whenever it comes out.
We have a lot of data. Of course it doesn’t feel like we have enough, and it never will, but we have a lot of data. We can use what we have, for example collecting all the correspondences from early modern Europe, and place them on a map like this one. The more data we have, the smaller time slices we can have in our maps. We create a base map that is a combination of geographic properties, statistical location properties, and network properties.
Start with a map of the world. To account for population or related correlations, do something similar to what Elijah did in this post, encoding population information (or average number of publications per city, or whatever else you’d like to account for) into the map. On top of that, place the biggest network of whatever it is that you’re looking at that you can find. Scholarly communication, citations, whatever. It’s your big Map of YourFavoriteThingHere. All of these together are your base map.
Atop that, place whatever or whomever you are studying. The correspondence of Grotius can be put on this map, like the NIH was overlaid atop the Map of Science, and areas would light up and become larger if they are surprising against the base map. Are there more letters between Paris and The Hague in the Grotius dataset then one would expect if the dataset was just randomly plucked from the whole Republic of Letters? If so, make that line brighter and thicker.
By combining geography, point statistics, and networks, we can create base maps against which we can contextualize whatever we happen to be studying. This is just one possible combination; base maps can be created from any of a myriad of sources of data. The important thing is that we, as humanists, ought to be able to contextualize our data in the same way that we always have. Now that we’re working with a lot more of it, we’re going to need help in those contextualizations. Base maps are one solution.
It’s worth pointing out one major problem with base maps: bias. Until recently, those Maps of Science making their way around the blogosphere represented the humanities as a small island off the coast of social sciences, if they showed them at all. This is because the primary publication venues of the arts and humanities were not represented in the datasets used to create these science maps. We must watch out for similar biases when constructing our own base maps, however the problem is significantly more difficult for historical datasets because the underrepresented are too dead to speak up. For a brief discussion of historical biases, you can read my UCLA presentation here.
putting every tool imaginable in one box and using them all at once ↩
Full disclosure: she’s my advisor. She’s also awesome. Hi Katy! ↩
According to Google Scholar, David Blei’s first topic modeling paper has received 3,540 citations since 2003. Everybody’s talking about topic models. Seriously, I’m afraid of visiting my parents this Hanukkah and hearing them ask “Scott… what’s this topic modeling I keep hearing all about?” They’re powerful, widely applicable, easy to use, and difficult to understand — a dangerous combination.
Since shortly after Blei’s first publication, researchers have been looking into the interplay between networks and topic models. This post will be about that interplay, looking at how they’ve been combined, what sorts of research those combinations can drive, and a few pitfalls to watch out for. I’ll bracket the big elephant in the room until a later discussion, whether these sorts of models capture the semantic meaning for which they’re often used. This post also attempts to introduce topic modeling to those not yet fully converted aware of its potential.
A brief history of topic modeling
In my recent post on IU’s awesome alchemy project, I briefly mentioned Latent Semantic Analysis (LSA) and Latent Dirichlit Allocation (LDA) during the discussion of topic models. They’re intimately related, though LSA has been around for quite a bit longer. Without getting into too much technical detail, we should start with a brief history of LSA/LDA.
The story starts, more or less, with a tf-idf matrix. Basically, tf-idf ranks words based on how important they are to a document within a larger corpus. Let’s say we want a list of the most important words for each article in an encyclopedia.
Our first pass is obvious. For each article, just attach a list of words sorted by how frequently they’re used. The problem with this is immediately obvious to anyone who has looked at word frequencies; the top words in the entry on the History of Computing would be “the,” “and,” “is,” and so forth, rather than “turing,” “computer,” “machines,” etc. The problem is solved by tf-idf, which scores the words based on how special they are to a particular document within the larger corpus. Turing is rarely used elsewhere, but used exceptionally frequently in our computer history article, so it bubbles up to the top.
LSA and pLSA
LSA utilizes these tf-idf scores 1 within a larger term-document matrix. Every word in the corpus is a different row in the matrix, each document has its own column, and the tf-idf score lies at the intersection of every document and word. Our computing history document will probably have a lot of zeroes next to words like “cow,” “shakespeare,” and “saucer,” and high marks next to words like “computation,” “artificial,” and “digital.” This is called a sparse matrix because it’s mostly filled with zeroes; most documents use very few words related to the entire corpus.
With this matrix, LSA uses singular value decomposition to figure out how each word is related to every other word. Basically, the more often words are used together within a document, the more related they are to one another. 2 It’s worth noting that a “document” is defined somewhat flexibly. For example, we can call every paragraph in a book its own “document,” and run LSA over the individual paragraphs.
The method was significantly improved by Puzicha and Hofmann (1999), who did away with the linear algebra approach of LSA in favor of a more statistically sound probabilistic model, called probabilistic latent semantic analysis (pLSA). Now is the part of the blog post where I start getting hand-wavy, because explaining the math is more trouble than I care to take on in this introduction.
Essentially, pLSA imagines an additional layer between words and documents: topics. What if every document isn’t just a set of words, but a set of topics? In this model, our encyclopedia article about computing history might be drawn from several topics. It primarily draws from the big platonic computing topic in the sky, but it also draws from the topics of history, cryptography, lambda calculus, and all sorts of other topics to a greater or lesser degree.
Now, these topics don’t actually exist anywhere. Nobody sat down with the encyclopedia, read every entry, and decided to come up with the 200 topics from which every article draws. pLSA infers topics based on what will hereafter be referred to as black magic. Using the dark arts, pLSA “discovers” a bunch of topics, attaches them to a list of words, and classifies the documents based on those topics.
Blei et al. (2003) vastly improved upon this idea by turning it into a generative model of documents, calling the model Latent Dirichlet allocation (LDA). By this time, as well, some sounder assumptions were being made about the distribution of words and document length — but we won’t get into that. What’s important here is the generative model.
Imagine you wanted to write a new encyclopedia entry, let’s say about digital humanities. Well, we now know there are three elements that make up that process, right? Words, topics, and documents. Using these elements, how would you go about writing this new article on digital humanities?
First off, let’s figure out what topics our article will consist of. It probably draws heavily from topics about history, digitization, text analysis, and so forth. It also probably draws more weakly from a slew of other topics, concerning interdisciplinarity, the academy, and all sorts of other subjects. Let’s go a bit further and assign weights to these topics; 22% of the document will be about digitization, 19% about history, 5% about the academy, and so on. Okay, the first step is done!
Now it’s time to pull out the topics and start writing. It’s an easy process; each topic is a bag filled with words. Lots of words. All sorts of words. Let’s look in the “digitization” topic bag. It includes words like “israel” and “cheese” and “favoritism,” but they only appear once or twice, and mostly by accident. More importantly, the bag also contains 157 appearances of the word “TEI,” 210 of “OCR,” and 73 of “scanner.”
So here you are, you’ve dragged out your digitization bag and your history bag and your academy bag and all sorts of other bags as well. You start writing the digital humanities article by reaching into the digitization bag (remember, you’re going to reach into that bag for 22% of your words), and you pull out “OCR.” You put it on the page. You then reach for the academy bag and reach for a word in there (it happens to be “teaching,”) and you throw that on the page as well. Keep doing that. By the end, you’ve got a document that’s all about the digital humanities. It’s beautiful. Send it in for publication.
Alright, what now?
So why is the generative nature of the model so important? One of the key reasons is the ability to work backwards. If I can generate an (admittedly nonsensical) document using this model, I can also reverse the process an infer, given any new document and a topic model I’ve already generated, what the topics are that the new document draws from.
Another factor contributing to the success of LDA is the ability to extend the model. In this case, we assume there are only documents, topics, and words, but we could also make a model that assumes authors who like particular topics, or assumes that certain documents are influenced by previous documents, or that topics change over time. The possibilities are endless, as evidenced by the absurd number of topic modeling variations that have appeared in the past decade. David Mimno has compiled a wonderful bibliography of many such models.
While the generative model introduced by Blei might seem simplistic, it has been shown to be extremely powerful. When a newcomer sees the results of LDA for the first time, they are immediately taken by how intuitive they seem. People sometimes ask me “but didn’t it take forever to sit down and make all the topics?” thinking that some of the magic had to be done by hand. It wasn’t. Topic modeling yields intuitive results, generating what really feels like topics as we know them 3, with virtually no effort on the human side. Perhaps it is the intuitive utility that appeals so much to humanists.
Topic Modeling and Networks
Topic models can interact with networks in multiple ways. While a lot of the recent interest in digital humanities has surrounded using networks to visualize how documents or topics relate to one another, the interfacing of networks and topic modeling initially worked in the other direction. Instead of inferring networks from topic models, many early (and recent) papers attempt to infer topic models from networks.
Topic Models from Networks
The first research I’m aware of in this niche was from McCallum et al. (2005). Their model is itself an extension of an earlier LDA-based model called the Author-Topic Model (Steyvers et al., 2004), which assumes topics are formed based on the mixtures of authors writing a paper. McCallum et al. extended that model for directed messages in their Author-Recipient-Topic (ART) Model. In ART, it is assumed that topics of letters, e-mails or direct messages between people can be inferred from knowledge of both the author and the recipient. Thus, ART takes into account the social structure of a communication network in order to generate topics. In a later paper (McCallum et al., 2007), they extend this model to one that infers the roles of authors within the social network.
Dietz et al. (2007) created a model that looks at citation networks, where documents are generated by topical innovation and topical inheritance via citations. Nallapati et al. (2008) similarly creates a model that finds topical similarity in citing and cited documents, with the added ability of being able to predict citations that are not present. Blei himself joined the fray in 2009, creating the Relational Topic Model (RTM) with Jonathan Chang, which itself could summarize a network of documents, predict links between them, and predict words within them. Wang et al. (2011) created a model that allows for “the joint analysis of text and links between [people] in a time-evolving social network.” Their model is able to handle situations where links exist even when there is no similarity between the associated texts.
Networks from Topic Models
Some models have been made that infer networks from non-networked text. Broniatowski and Magee (2010 & 2011) extended the Author-Topic Model, building a model that would infer social networks from meeting transcripts. They later added temporal information, which allowed them to infer status hierarchies and individual influence within those social networks.
Many times, however, rather than creating new models, researchers create networks out of topic models that have already been run over a set of data. There are a lot of benefits to this approach, as exemplified by the Newton’s Chymistry project highlighted earlier. Using networks, we can see how documents relate to one another, how they relate to topics, how topics are related to each other, and how all of those are related to words.
Elijah Meeks created a wonderful example combining topic models with networks in Comprehending the Digital Humanities. Using fifty texts that discuss humanities computing, Elijah created a topic model of those documents and used networks to show how documents, topics, and words interacted with one another within the context of the digital humanities.
Elijah Jeff Drouin has also created networks of topic models in Proust, as reported by Elijah.
Peter Leonard recently directed me to TopicNets, a project that combines topic modeling and network analysis in order to create an intuitive and informative navigation interface for documents and topics. This is a great example of an interface that turns topic modeling into a useful scholarly tool, even for those who know little-to-nothing about networks or topic models.
If you want to do something like this yourself, Shawn Graham recently posted a great tutorial on how to create networks using MALLET and Gephi quickly and easily. Prepare your corpus of text, get topics with MALLET, prune the CSV, make a network, visualize it! Easy as pie.
Networks can be a great way to represent topic models. Beyond simple uses of navigation and relatedness as were just displayed, combining the two will put the whole battalion of network analysis tools at the researcher’s disposal. We can use them to find communities of similar documents, pinpoint those documents that were most influential to the rest, or perform any of a number of other workflows designed for network analysis.
As with anything, however, there are a few setbacks. Topic models are rich with data. Every document is related to every other document, if some only barely. Similarly, every topic is related to every other topic. By deciding to represent document similarity over a network, you must make the decision of precisely how similar you want a set of documents to be if they are to be linked. Having a network with every document connected to every other document is scarcely useful, so generally we’ll make our decision such that each document is linked to only a handful of others. This allows for easier visualization and analysis, but it also destroys much of the rich data that went into the topic model to begin with. This information can be more fully preserved using other techniques, such as multidimensional scaling.
A somewhat more theoretical complication makes these network representations useful as a tool for navigation, discovery, and exploration, but not necessarily as evidentiary support. Creating a network of a topic model of a set of documents piles on abstractions. Each of these systems comes with very different assumptions, and it is unclear what complications arise when combining these methods ad hoc.
Although there may be issues with the process, the combination of topic models and networks is sure to yield much fruitful research in the digital humanities. There are some fantastic tutorials out there for getting started with topic modeling in the humanities, such as Shawn Graham’s post on Getting Started with MALLET and Topic Modeling, as well as on combining them with networks, such as this post from the same blog. Shawn is right to point out MALLET, a great tool for starting out, but you can also find the code used for various models on many of the model-makers’ academic websites. One code package that stands out is Chang’s implementation of LDA and related models in R.
Ted Underwood rightly points out in the comments that other scoring systems are often used in lieu of tf-idf, most frequently log entropy. ↩
Yes yes, this is a simplification of actual LSA, but it’s pretty much how it works. SVD reduces the size of the matrix to filter out noise, and then each word row is treated as a vector shooting off in some direction. The vector of each word is compared to every other word, so that every pair of words has a relatedness score between them. Ted Underwood has a great blog post about why humanists should avoid the SVD step. ↩
They’re not, of course. We’ll worry about that later. ↩
The inimitable Elijah Meeks recently shared his reasoning behind joining Google+ over Twitter or Facebook. “G+ seems to be self-consciously a network graph that happens to let one connect and keep in touch.” For those who haven’t made the jump, Google+ feels like a contact list on steroids; it lets you add contacts, organize them into different (often overlapping) “circles,” and ultimately you can share materials based on those circles, video chat, send messages, and so forth. By linking your pre-existing public Google profile (and rolling in old features like Buzz and Google Reader), Google has essentially socialized web presences rather than “web presencifying” the social space.
It’s a wishy-washy distinction, and not entirely true, but it feels true enough that many who never worried about social networking sites are going to Google+. This is also one of the big distinctions between the loved-but-lost Google Wave, which was ultrasocial but also ultraprivate; it was not an extended Twitter, but an extended AIM or gmail — really some Frankenstein of the two. It wasn’t about presences and extending contacts, but about chatting alone.
True to Google form, they’ve already realized the potential of sharing in this semi-public space. If Twitter weren’t so minimalistic, they too would have caught on early. Yesterday, via G+ itself, Ripples rippled through the social space. Google+ Ripples describes itself as “a way to visualize the impact of any public post.” This link1 shows the “ripples” of Ripples itself 2, or the propagation of news of Ripples through the G+ space.
They do a great job invoking the very circles used to organize contacts. Nested circles show subsequent generations of the shared post, and in most cases nested circles also represent followers of the most recent root node. Below the graph, G+ displays the posting frequency over time and allows the user to rewind the clock, seeing how the network grew. Hidden at the bottom of the page, you can find the people with the most public reshares (“influencers”), basic network statistics (average path length, not terribly meaningful in this situation; longest chain; and shares-per-hour), and languages of reshared posts. You can also read the reshares themselves on the right side of the screen, which immediately moved this from my mental “toy” box to the “research tool” box.
Make no mistake, this is a research tool. Barring the lack of permanent links or the ability to export the data into some manipulable file 3, this is a perfect example of information propagation done well. When doing similar research on Twitter, one often requires API-programming prowess to get even this far; in G+, it’s as simple as copying a link. By making information-propagating-across-a-network something sexy, interesting, and easily accessible to everyone, Google is making diffusion processes part of the common vernacular. For this, I give Google +1.
One feature I would like would be the ability to freeze Ripples links. The linked content will change as more people share the initial post – this is potentially problematic. ↩