Omar Rodriguez, and I come from the University of Alberta in Canada, and here's my presentation.
In the academic community, there's been an increasing interest in the interdisciplinary
projects where academics of different backgrounds get together and approach to solve a problem
that can be either not very clearly defined within a single domain or can be approached
in different ways to try to solve it.
So we're interested in trying to study how these collaborations take place, specifically
in the field of information science, and this is of interest to us because it's studied
by different domains like library and archival studies, computer science, management, engineering,
among others.
There has been some previous work done in the past in this field, and it has been shown
that there are some similarities in concepts and research methodology.
However, it's also been shown that there are differences in co-citation patterns as well
as in the contents of the research and the focus of the research itself.
These have been very interesting results from the work.
However, the work is not without some limitations, specifically the scope of the work in some
cases where they have to focus on just a subset of academic units that publish journals only
under a certain domain.
Some other works have to do manual analysis that greatly reduces the amount of academic
papers that they can study.
Lastly, there has been little usage of information visualization techniques that can bring up
interesting patterns inside the data that has been studied.
So our objective is to first to address the previous limitations in the work done here
in the past, revisit the question of divergence and convergence within the sub-disciplines,
and propose a method for analyzing the semantic and usage patterns in information science
research articles.
Our method is as follows.
First, we defined three topics of areas.
In our case, they were trust, text mining, and web services.
We use these topic words because they cross boundaries and they are studied by different
domains.
Now, an example of the definition of one of these topic areas, for instance, trust.
We took trust and defined it as the design or implementation of tools that increase the
trust in e-commerce.
So with that definition, we bring out keywords like the word trust itself, web, online, e-commerce,
recommendations, ratings, et cetera.
And then we take those keywords and we put them inside the Thomson Reuters Web of Knowledge
to get articles across the span of 15 years.
And we do this at three years' intervals.
At every query, we take the highest-ranked 500 articles from that query and then analyze
them in both co-sanitation and content manner.
And the last step was that we developed a visualization model to try to expose the patterns
inside of the form in these relationships over time.
This is a little bit more about our method.
We try to, as I mentioned, try to show how, in a graphic way, how citations are done.
And this is usually done using the idea of graphs where each node represents a journal
and a link between those two nodes shows a citation in that journal.
However, even though we don't stray off too much of that path, we do try to make a change
recognizing that when a citation is done to an article, that article can fall under different
categories.
So with that in mind, we think there is a more organic way to show the graph where each
node represents a category.
And for instance, when doing co-citation analysis, a link between those two categories will mean
that a journal of that category, which in that category cited another journal with another
category that represents that node.
We're also interested, as I mentioned before, to try to show the communities inside of these
nodes once they're created.
And to do so, we follow the work from Stefan, where he uses the map equation to try to show
the information.
The map equation sort of brings out the communities by using what can be described as a random
reader that sort of works like as it visits a node, a category.
It tries to move away to another node based on the linkage.
And this sort of creates paths around the network.
We can think of as it's moving and revisits the same sort of nodes over time.
This will create a cluster of these nodes.
And this was done by changing the problem of segmentation to one of compression.
So we're trying to decrease the size of the overall node by simplifying it.
We're also interested in showing the importance of the categories as they evolve over time.
And to do so, we use the again factor, which takes that input, the current year's information,
namely the categories, and the previous year's information, because it needs some a priori
knowledge.
This algorithm is similar to Google's page rank in that it defines the importance of
the category based on the location of the category in the mesh.
That means if the category is linked by a lot of important categories, it will be considered
as an important category itself.
And we use this information to place our category at each time period on the y-axis.
So here is a screenshot of the results.
Here you see that each line as it moves through time represents a category.
And you can see that it's forming different clusters represented by thicker lines as it
moves through from left to right.
In this same visualization, we see here the category information science and library science
as it starts to increase in importance over time.
This is shown by the red line there following that path.
And up to the right, you'll see there in 2006, it forms a cluster with different categories,
which is also easily to visualize with our application.
And on to the results.
Here is a comparison between the content and citation-based analysis for the trust topic.
And as you can see here in the lower right portion, we have the citation-based analysis
where we see that there's one cluster, mainly one cluster that starts to break off into
different clusters over time.
But in the content which is on the left upper portion, we see that it's mainly just one
cluster that sort of keeps that structure over all time.
We can interpret this as not being a lot of change over time in content-wise when talking
about categories, but there are significant differences in citation pattern, meaning
that these categories, in one year, they can start to cite between categories a lot, but
they will break off as we change to other years.
And here is another screenshot comparing this time the text mining topic versus the
trust topic using the citation analysis.
We see that for the trust topic, again, there's a lot of breakage, even though it starts on
one big block, but for the text mining, there's a consistent structure where there's a lot
of breakage right from the beginning.
That's basically all my talk.
I can just have to add that our future work includes incrementing the amount of articles
that we study to broaden the scope, try to look for more analysis methods so we can get
more in-depth view of what the data is, and lastly, just to try out different visualization
mechanisms so we can sort of try to fish out different data patterns in the data that we get.
Thank you.