Tag Archives: csg

Looking for letters

In the posts I’ve written to date, I’ve learned some interesting things about my corpus of 40,000 news articles. I’ve seen how the articles are distributed over time and space. I’ve seen the locations they talk about, and how this shifts over time. And I’ve created a thematic index to see what it’s all about. But I’ve barely said anything about the articles themselves. I’ve written nothing, for example, about how they vary in their format, style, and purpose.

To some extent, such concerns are of secondary importance to me, since they are not very accessible to the methods I am employing, and (not coincidentally) are not central to the questions I will be investigating, which relate more to the thematic and conceptual aspects of the text. But even if these things are not the objects of my analysis, they are still important because they define what my corpus actually is. To ignore these things would be like surveying a large sample of people without recording what population or cohort those people represent. As with a survey, the conclusions I draw from my textual analysis will have no real-world validity unless I know what kinds of things in the real world my data represent.

In this post, I’m going to start paying attention to such things. But I’m not about to provide a comprehensive survey of the types of articles in my corpus. Instead I will focus on just one categorical distinction — that between in-house content generated by journalists and staff writers, and contributed or curated content in the form of readers’ letters and comments. Months ago, when I first started looking at the articles in my corpus, I realised that many of the articles are not news stories at all, but are collections of letters, text messages or Facebook posts submitted by readers. I wondered if perhaps this reader-submitted content should be kept separate from the in-house content, since it represents a different ‘voice’ to that of the newspapers themselves. Or then again, maybe reader’s views can be considered just as much a part of a newspaper’s voice as the rest of the content, since ultimately it is all vetted and curated by the newspaper’s editors.

As usual, the relevance of this distinction will depend on what questions I want to ask, and what theoretical frameworks I employ to answer them. But there is also a practical consideration — namely, can I even separate these types of content without sacrificing too much of my time or sanity? 40,000 documents is a large haystack in which to search for needles. Although there is some metadata in my corpus inherited from the Factiva search (source publication, author, etc.), none of it is very useful for distinguishing letters from other articles. To identify the letters, then, I was going to have to use information within the text itself. Continue reading

What do you do with a thousand place names?


My previous post was all about turning place names in news articles into dots on a map. Using a fairly straightforward method, I matched the place names in a collection of 26,863 news articles against the names and geographic coordinates in the Australian Gazetteer 2012, which lists and locates virtually every named place in Australia. Using such a comprehensive list created a fair amount of extra work, but resulted in a very rich and satisfying visualisation of how the news coverage about coal seam gas has moved over time. Ultimately however, I want to translate these rich visualisations into simpler narratives and numerical descriptions. And to do this, individual statistics for every one of the 1,448 places on my list will not be of much help. I will need some way of aggregating the locations into relevant regions or locales.

To achieve this, one could perhaps use some technique to group the locations based on spatial proximity — something akin to drawing fences around the places that form discrete clusters on the map. But there might be reasons besides proximity to group places together. Spatially distinct places might be united by common issues or events, just as proximate places might be subject to separate laws and controversies. Given that my ultimate object of study is public discourse, such non-geographical unifying factors may prove to be as important as geographical ones.

Latent Deary What?

Only some of these thoughts had crossed my mind when the idea hit me to use a topic modelling technique called Latent Dirichlet Allocation (LDA) to bring some order to my large list of locations. LDA is a technique that automatically identifies topics in large collections of documents, with a ‘topic’ in this context being defined as a set of words that tend to occur together in the documents that you are analysing. LDA uses some clever assumptions and iterative processes to find sets of words that, in most cases at least, correspond remarkably well with meaningful topics in the text. It is widely used for automated document categorisation and indexing, and more recently it has been applied to fields such as history and literary studies under the banner of the digital humanities. If you’re fluent in hieroglyphics, the Wikipedia page might be a good place to start if you want to know more about LDA. If you’re a mere mortal, pages like this one and this one offer a softer introduction.

Like many computational text analysis methods, LDA views each document as an unordered ‘bag of words’. (This might sound like the surest way to render a document meaningless, but the payoff is that it makes the text amenable to all kinds of statistical techniques.) So I figured, why not instead feed the LDA algorithm bags of places, which is exactly what I had created from my collection of news articles when preparing my last post. I saw no reason why LDA couldn’t turn this data into groups of locations that were both spatially and discursively meaningful. Places that are mentioned together in articles are likely to be physically close to one another, linked by social context, or most likely, both. Meaningful groupings of these places could be called geographic topics, or geotopics for short. Continue reading

Mapping the news

Where did the last 12 months go? All I can really remember is something about being confirmed as a PhD candidate. I read a lot, and wrote a lot, but did very little of what I originally set out to do — namely, visualising and analysing text data. Now, finally, I am back in the sandpit. I’ve amassed a truckload of data in the form of news articles and blogs about coal seam gas development in Australia, and I intend to spend the next short while sifting through it and seeing what sort of sandcastles I can build before the tide of my next PhD milestone forces me to construct something more substantial.

The ultimate aim of my PhD is to explore how computational text analysis techniques such as topic modelling can assist in the analysis of public discourse. But for now, my objective is to get acquainted with my data. This data is divided into two piles, each representing a part of the discursive landscape around coal seam gas (or CSG) in Australia (if you’re American, think coalbed methane). One pile of data consists of texts published on the web by a range of actors (the sociology kind, not the Hollywood kind) including community groups, activists, lobbyists and politicians. I’ve siphoned these texts from a variety of websites using a data-crawling tool called import.io. The second, much larger, pile of data consists of news articles from hundreds of Australian mainstream media publications, from the national broadsheet right down to the local rags. I gathered these articles from the online news database Factiva, with the help of a script, available at the website for the conversation analysis tool Discursis, which converts Factiva’s HTML outputs into tabular format in the form of CSV files.

This post is devoted to exploring the second pile of data — the many thousands of news articles that I gathered from Factiva. Without attempting any fancy text analysis, I aim to get a first look at the overall volume, scope and diversity of the content. The focus in this post is on the overall volume and the geographic distribution of the content. In a future post, I plan to explore the the specific news sources in more detail. Continue reading

The bottom-right cluster. All of these documents except Submission 0655 draw on the same template.

Using Junk words to find recycled text

Newton’s third law of motion — that for every action, there is an equal and opposite reaction — would appear to apply to the coal seam gas industry in Australia. The dramatic expansion of the industry in recent years has been matched by the community’s equally dramatic mobilisation against it. As my previous post showed, there are literally dozens of organisations on the web (and probably even more on Facebook) concerned in some way with the impacts of coal seam gas development. Some of these are well-established groups that have incorporated coal seam gas into their existing agendas, but many others seem to have popped up out of nowhere.

Most of these groups could be classified as community organisations insofar as they are concerned with a specific region or locality. But to think of them all as ‘grassroots’ organsiations, each having emerged organically on its own accord, might be a mistake. As the website network in my last post suggests, many of these groups might better be thought of as ‘rhizomatic’ (or lateral) offshoots inspired by the Lock the Gate Alliance. Lock the Gate emerged in 2010 and quickly reconfigured the landscape of community opposition to coal seam gas. Its campaigns, strategies and symbolism provided a handy template upon which locally focussed organisations could form. You’ll be hard-pressed to find a community-based anti-CSG group without a link to Lock the Gate on their website.

The lesson here is that voices that appear to be independent may to some extent be influenced or assisted by a small handful of highly motivated (or well resourced) groups or individuals. Having observed this possibility in the network of anti-CSG websites, I recently encountered it again while sifting through a very different dataset that I am preparing for  textual analysis. The dataset in question is the 893 public submissions that the Parliament of New South Wales received in response to its 2011 inquiry into the environmental, health, economic and social impacts of coal seam gas activities. The submissions came from all kinds of stakeholders, including community groups, gas companies, scientific and legal experts, government agencies, and individual citizens. Of particular interest to me were the 660 submissions from individual citizens. Here was a sizable repository of views expressed straight from the minds and hearts of individual people, undistorted by the effects of groupthink or coordinated campaigns. Or so I thought. Continue reading