By my own criteria I’ve already failed… I started this series of posts with the intention of documenting the process of finding and extracting editorials as I was actually doing the work. But here I am about to describe some work I finished a few weeks back. Oh well…

In my previous instalments (here and here), I focused on the Sydney Morning Herald. Having continued the hunt for missing editorials I started in the last post, I’ve now got a CSV file with the urls of the first editorial published in every edition of the SMH from 1913. Good-o, I thought, I can now start harvesting and analysing some content.

But then ensued a crisis of faith. The whole point of this exercise was to be able to build up some comparisons  — between newspapers, between states, between the city and the bush. But the process of actually finding the editorials seemed beset with difficulties. Could the rules I developed for the SMH be applied elsewhere? Could I ever assemble a useful set of editorials without large amounts of human intervention? I decided to try a few quick experiments to see whether the whole project was worth pursuing.

I started with a few assumptions:

1. The first (and only the first) editorial in any issue is headed with the name of the newspaper.
2. Editorials are published on even numbered pages.
3. Editorials vary in length between about 100 and 1500 words.

These assumptions were based on my own experience as a long-time newspaper researcher and on some preliminary poking around. For example, when I looked at The Argus I noticed that editorials were typically followed by news summaries. Unfortunately, these are treated as a single article in Trove, resulting in large blocks of text that are only part editorial. By specifying an upper word limit I hoped to filter these sorts of articles out. Similarly, there are sometimes brief announcements or publication details headed with the name of the newspaper. The lower word limit was intended to exclude these.

The next step was to harvest every article from 1913 that was headed with the name of its publication. I created a script to generate a list of all the newspapers that published issues in 1913. Then I called my existing harvester to download all the matching articles and save the details to a series of CSV files — one CSV file per newspaper.

In the previous instalment of this series I created a script to check the CSV output of my harvester for missing or duplicate dates. I extended this to perform a series of tests on each article based on the assumptions above. First, I filtered out articles on odd-numbered pages, then articles that were too short or too long. Finally I checked the remainder for missing or duplicate issue dates.

The details of the articles in each category were written out to JSON files. Using these files and a bit of JQuery magic I could quickly build a simple web interface that allowed me to explore the results.

You can browse the summary results for the full list of newspapers, or you can drill down to view the actual articles assigned to each category.

I’ll save the full analysis for the next post, but if you play around with the results you quickly notice a few things. First, letters to the editor often include the name of the newspaper! If you look at The Mercury, for example, you’ll notice I’ve identified 1057 potential editorials — most of which are letters. Fortunately they should be fairly easy to filter out. In most cases the ‘even numbers only’ assumption worked pretty well, and the word length filters did remove quite a lot of false positives. There are still plenty of problems, but I’m encouraged enough to continue. Yes, there will be a Part #4!

As I explained in the first of this series, I’m documenting my efforts to extract every editorial published in the Sydney Morning Herald in 1913 from the Trove newspaper database. It’s an experiment both in text mining and historical writing — an attempt to put the method up front.

While I didn’t think there was anything very thrilling in the first instalment, recording my thoughts and assumptions in this way has already proved useful. In a comment, Owen Stephens noted that his attempt to reproduce my search query produced fewer results. After a little bit of poking around I realised that the fulltext modifier, which I often use to switch off fuzzy matching, counteracts the ‘search headings only’ flag. So my query was returning results that had the string ‘The Sydney Morning Herald’ anywhere in the article.

Try it for yourself.

Here’s my original query — searching for fulltext:”The Sydney Morning Herald” in headings only (supposedly). You’ll notice that it returns 335 results and it’s clear from a quick scan that a number are false positives (they don’t follow the pattern for editorials).

Here’s Owen’s query — searching for “The Sydney Morning Herald” in headings only. It returns 294 results, without any obvious false positives.

So my attempt to disable fuzzy matching actually produced a less accurate result! Weird.

Actually, I think one important benefit of this sort of text mining is that it helps you understand how the search engines you’re using actually work. Once you start poking and prodding, the idiosyncrasies start to emerge.

Anyway, I harvested Owen’s cleaner result set and opened up the resulting csv file. As it seemed in Trove, there we’re very few false positives. Indeed there were only two articles that didn’t seem to follow the standard editorial format, and these were notes added to the editorial page. On the other hand, there were obviously about 20 editorials missing. I could have manually worked through the csv file to identify the missing dates, but I thought I’d try to create some tools that would do the work for me.

What I wanted was the details of the first editorial in every edition of the newspaper in 1913 — so there should be one, and only one, article for each day on which the newspaper was published. I needed a tool that would analyse the csv file and do two things:

• identify dates that occur multiple times (false positive alert!)
• identify dates that are absent from the result set (missing in action!)

The resulting code is all on GitHub if you want follow along. I wrote a Python script that opens up the csv file, extracts all the date strings, converts them to datetime objects and then saves them to a list. Once that’s done it’s pretty easy to loop through and find duplicates:

def find_duplicates(list):
    '''
    Check a list for suplicate values.
    Returns a list of the duplicates.
    '''
    seen = set()
    duplicates = []
    for item in list:
        if item in seen:
            duplicates.append(item)
        seen.add(item)
    return duplicates

Finding missing dates was a little more complicated, but Google came to the rescue with some handy code samples. All I had to do was set a start and end date (in this case 1 January 1913 and 31 December 1913) and create a timedelta object equal to a day. Then it’s just a matter of adding the timedelta to the start date, comparing the new date to the dates extracted from the csv file, and continuing on until you hit the end. If the new date isn’t in the csv file, then it gets added to the missing list.

if year:
        start_date = datetime.date(year, 1, 1)
        end_date = datetime.date(year, 12, 31)
    else:
        start_date = article_dates[0]
        end_date = article_dates[-1]
    one_day = datetime.timedelta(days=1)
    this_day = start_date
    # Loop through each day in specified period to see if there's an article
    # If not, add to the missing_dates list.
    while this_day <= end_date:
        if this_day.weekday() not in exclude: #exclude Sunday
            if this_day not in article_dates:
                missing_dates.append(this_day)
        this_day += one_day

I’ve tried to make the code as reusable as possible, so you can either supply a year, or the script will read start and end dates from the csv file itself.

All that left me with two more lists of dates: ‘duplicates’ and ‘missing’. At first I just wrote these out to a text file, but then I decided it would be useful to write the results to an html page. That way I could add links that would take me to the actual issue within Trove, helping me to quickly find the missing editorial.

Unfortunately there’s no direct way to go from a date to an issue — you first need to find the issue identifier. How do you do this? If you dig around in the code beneath the page for each newspaper title, you’ll find that the ajax interface pulls in a json file with issue information. You can access this through a url like: http://trove.nla.gov.au/ndp/del/titlesOverDates/[year]/[month]. Here’s an example for January 1913.

The json includes all issues for all titles in the specified month. So you then have to loop through to find a specific title and day. Once you have the issue identifier you can just attach it to a url:

def get_issue_url(date, title_id):
    '''
    Gets the issue url given a title and date.
    '''
    year, month, day = date.timetuple()[:3]
    url = 'http://trove.nla.gov.au/ndp/del/titlesOverDates/%s/%02d' % (year, month)
    issues = json.load(urllib2.urlopen(url))
    for issue in issues:
        if issue['t'] == title_id and int(issue['p']) == day:
            issue_id = issue['iss']
    return 'http://trove.nla.gov.au/ndp/del/issue/%s' % issue_id

Finally, to save myself having to cut and paste the missing dates back into the csv file, I added a few lines to write them in automatically.

So now I have a handy little html page, complete with dates and links, that I’m working through to find all the missing editorials. All I need for the next stage are the urls for the editorial and the page on which it’s published. I’m just cutting and pasting these from the citation box in Trove into the csv file. Once this is done I can start trying to find all the editorials.

PS: I noted in my first post that one benefit in finding the editorials was that the main news articles usually appeared on the page after the editorials. I’ve been thinking some more about ways to identify ‘major’ news stories. Word length perhaps? But not always. Hmmm, but major stories do seem to be published at the top of the page. After a bit more poking around in the code I found that there’s a ‘y value’ assigned to each article that indicates its position on the page. So if I harvest all the articles on the page after the editorials and then rank them by their y values? Interesting…

In their chapter in Writing History in the Digital Age, Trevor Owens and Fred Gibbs encourage historians to write about the ways they work with data — to document their methods, their working assumptions, their dead ends and their discoveries. It’s an important argument and one that makes me wonder again about forms of publication that might integrate narrative, methods and sources.

In the meantime though we have blogs. My problem is that I’m easily bored so by the time I get to the end of a project or experiment I’m already thinking about the next one. Going back and trying to write things up seems a bit of a chore (which is why I’m always way behind in my blog writing). Also leaving the writing to the end means that I tend to take shortcuts — leaving out some of the ‘boring’ procedural stuff or the ‘stupid’ ideas that just didn’t work.

But Trevor and Fred’s chapter has made me think I should be a bit more diligent, so as I start a new series of text-mining experiments I’ve decided to write things up as I’m doing them. So be warned, this could get messy…

So what do I want to do? You might not be surprised to learn that it’s another Trove newspaper database experiment. I want to see if I can harvest newspaper editorials over a certain period and then analyse these to build up a picture of what issues, events or ideas were perceived as important. As I’m currently looking at ways of harvesting digital sources relating to 1913 for an exhibition being developed by the National Museum of Australia, I’m going to start by focusing on 1913.

But editorials are opinion pieces, wouldn’t it be better to harvest ‘news’ articles?

First of all, I’m thinking that editorials will be fairly easy to identify and extract — there’s no real way in Trove to separate out current news from other sorts of articles. Secondly, I’m assuming that the issues that make it into editorials have some importance attached to them. Attached by whom, you may well ask — whose voice is being represented in the editorial? This is an important question and I’m thinking that it could be explored in interesting ways by harvesting editorials from a range of papers and regions. Thirdly, finding the editorials might actually help me find the major news articles, simply because in this period the main news stories were often on the page after the editorials.

So how do I find them? Looking at the Sydney Morning Herald for 1913, you can see that the editorials follow a regular pattern:

• the first editorial is always headed with the name of the paper and the date, followed by the title
• subsequent editorials that follow have a title but no subtitle (most other types of articles have a subtitle)
• editorials are published on an even-numbered page, usually about half way through the newspaper

To check this I conducted a search for articles including ‘The Sydney Morning Herald’ in their title. The search returns 335 results. Of course we’d expect there to be 312 (6 x 52), but it looks like there’s quite a few false positives and some days missing altogether (presumably due to OCR errors). You can see there’s a fair bit of consistency in the pages that editorials appear on, but it doesn’t quite seem consistent enough to rely on. So I’ve decided that as a first step I’ll harvest all the articles from this query. I’ll then do some manual cleaning to remove the articles that aren’t editorials and try and identify and retrieve the missing days.

Remember, this won’t give me all the editorials, only the first editorial from each day. To get all the editorials, I’ll have to write a new script that will take this first result set, retrieve all the articles from the editorial page and then try to work out which of the articles are editorials — they should be the ones that come after the first editorial and have no subtitle. Or that’s the theory.

I’ve harvested the query. You can view the spreadsheet on Google Docs if you feel so moved.