All posts by angusv

A thesis relived: using text analytics to map a PhD journey

 

Your thesis has been deposited.

Is this how four years of toil was supposed to end? Not with a bang, but with a weird sentence from my university’s electronic submission system? In any case, this confirmation message gave me a chuckle and taught me one new thing that could be done to a thesis. A PhD is full of surprises, right till the end.

But to speak of the end could be premature, because more than two months after submission, one thing that my thesis hasn’t been yet is examined. Or if it has been, the examination reports are yet to be deposited back into the collective consciousness of my grad school.

The lack of any news about my thesis is hardly keeping me up at night, but it does make what I am about to do in this post a little awkward. Following Socrates, some people would argue that an unexamined thesis is not worth reliving. At the very least, Socrates might have cautioned against saying too much about a PhD experience that might not yet be over. Well, too bad: I’m throwing that caution to the wind, because what follows is a detailed retrospective of my PhD candidature.

Before anyone starts salivating at the prospect of reading sordid details about about existential crises, cruel supervisors or laboratory disasters, let me be clear that what follows is not a psychodrama or a cautionary tale. Rather, I plan to retrace the scholastic journey that I took through my PhD candidature, primarily by examining what I read, and when.

I know, I know: that sounds really boring. But bear with me, because this post is anything but a literature review. This is a data-driven, animated-GIF-laden, deep-dive into the PhD Experience. Continue reading

Tracking and comparing regional coverage of coal seam gas

In the last post, I started looking at how the level of coverage of specific regions changed over time — an intersection of the Where and When dimensions of the public discourse on coal seam gas. In this post I’ll continue along this line of analysis while also incorporating something from the Who dimension. Specifically, I’ll compare how news and community groups cover specific regions over time.

Regional coverage by news organisations

One of the graphs in my last post compared the ratio of coverage of locations in Queensland to that of locations in New South Wales. Figure 1 below takes this a step further, breaking down the data by region as well. What this graph shows is the level of attention given to each region by the news sources in my database (filtered to ensure complete coverage for the period — see the last post) over time. In this case, I have calculated the “level of attention” for a given region by counting the number of times a location within that region appears in the news coverage, and then aggregating these counts within a moving 90-day window. Stacking the tallies to fill a fixed height, as I have done in Figure 1, reveals the relative importance of each region, regardless of how much news is generated overall (to see how the overall volume of coverage changes over time, see the previous post). The geographic boundaries that I am using are (with a few minor changes) the SA4 level boundaries defined by the Australian Bureau of Statistics. You can see these boundaries by poking around on this page of the ABS website.

The regions in Figure 1 are shaded so that you can see the division at the state level. The darker band of blue across the lower half of the graph corresponds with regions in Queensland. The large lighter band above that corresponds with regions in New South Wales. Above that, you can see smaller bands representing Victoria and Western Australia. (The remaining states are there too, but they have received so little coverage that I haven’t bothered to label them.) I have added labels for as many regions as I can without cluttering up the chart.

Figure 1. Coverage of geographic regions in news stories about coal seam gas, measured by the number of times locations from each region are mentioned in news stories within a moving 90-day window. The blue shadings group the regions by state. Hovering over the image shows a colour scheme suited to identifying individual regions. You can see larger versions of these images by clicking here and here.

Continue reading

It’s time

The last two posts have updated my progress in understanding the Where and the Who of public discourse on coal seam gas, but didn’t say much about the When. Analysing the temporal dynamics of public discourse — in other words, how things change — has been one of my driving interests all along in this project, so to complete this series of stock-taking articles, I will now review where I’m up to in analysing the temporal dimension.

At least, I had hoped to complete the stock-taking process with this post. But in the course of putting this post together, I made some somewhat embarrassing discoveries about the temporal composition of my data — discoveries that have significant implications for all of my analyses. This post is dedicated mostly to dealing with this new development. I’ll present the remainder of what I planned to talk about in a second installment.

The experience I describe here contains important lessons for anyone planning to analyse data obtained from news aggregation services as Factiva.

The moving window of time

The first thing to mention — and this is untainted by the embarrassment that I will discuss shortly — is that I’ve changed the way I’m making temporal graphs. Whereas previously I was simply aggregating data into monthly or quarterly chunks, I am now using KNIME’s ‘Moving Aggregation’ node to calculate moving averages over a specified window of time. This way, I can tailor the level of aggregation to the density of the data and the purpose of the graph. And regardless of the size of the time window, the time increments by which the graph is plotted can be as short as a week or a day, so the curve is smoother than a simple monthly or quarterly plot.

One reason why this feature is so useful is that the volume of news coverage on coal seam gas over time is very peaky, as shown in Figure 1 (and even the 30-day window hides a considerable degree of peakiness). Smoothing out the peaks to see long-term trends is all well and good, but it’s important never to lose touch with the fact that the data doesn’t really look that way.

Figure 1. The number of articles in my corpus over time, aggregated to a 30-day moving window. Hovering over the image shows the same data aggregated to a 90-day window. Continue reading