Searching an on-line newspaper without an API.

Last week’s research focused on getting notices from the British Gazette via their API. The notices are searchable and returned as XML which could be handled easily as a dataFrame in R.

This week’s focus is a web site that I can’t find an API for, the National Library of Wales’ Welsh Newspapers Online. This site is a tremendous source of news stories and it easy for a person to search and browse. I have asked the Welsh Library if the web site has an API. In the meantime, this week’s program uses search result webpages to get data. It’s a much uglier and error prone process to reliable get data out of a web page, but I expect the benefits of doing that will save me some time in pulling together newspaper articles for research so that I can focus on reading them rather than downloading them. I hope this approach is useful for others. Here is the program.

To search Welsh Newspapers Online using R, a URL can be composed with these statements:

searchDateRangeMin = "1914-08-03"
searchDateRangeMax = "1918-11-20"
searchDateRange = paste("&range%5Bmin%5D=",searchDateRangeMin,"T00%3A00%3A00Z&range%5Bmax%5D=",searchDateRangeMax,"T00%3A00%3A00Z",sep="")
searchBaseURL = ""
searchTerms = paste("search?alt=full_text%3A%22","allotment","%22+","AND","+full_text%3A%22","society","%22+","OR","+full_text%3A%22","societies","%22",sep="")
searchURL = paste(searchBaseURL,searchTerms,searchDateRange,sep="")

These assemble the Search URL:

This search generates 1,009 results.  To loop through them in pages of 12 results at a time this loop is used:

for(gatherPagesCounter in 84:(floor(numberResults/12)+1)){

How does the program know the number of results returned by the search? It looks through the search results page line by line until it finds a line like:

<input id="fl-decade-0" type="checkbox" class="facet-checkbox" name="decade[]" value="1910" facet />

If you like, take a look at the source of this page and find the line above by searching for 1910.

The line above is unique on the page and does not change regardless of how many search results. Following that line we have the one below containing the number of results:

<label for="fl-decade-0"> 1910 - 1919 <span class="facet-count" data-facet="1910">(1,009)</span></label>

Here is the part of the program that searches for the line above and parses the second line to get the numeric value of 1009 we want:

# find number of results
 for (entriesCounter in 1:550){
      if(thepage[entriesCounter] == '<input id=\"fl-decade-0\" type=\"checkbox\" class=\"facet-checkbox\" name=\"decade[]\" value=\"1910\" facet />') {
           tmpline = thepage[entriesCounter+1]
           tmpleft = gregexpr(pattern ='"1910',tmpline)
           tmpright = gregexpr(pattern ='</span>',tmpline)
           numberResults = substr(tmpline, tmpleft[[1]]+8, tmpright[[1]]-2)
           numberResults = trimws(gsub(",","",numberResults))
           numberResults = as.numeric(numberResults)

Getting this information returned from an API would be easier to work with, but we can handle this.

For testing purposes, I’m using 3 pages of search results for now. Here is a sample of the logic used in most of the program:

for(gatherPagesCounter in 1:3){
      thepage = readLines(paste   (searchURL,"&page=",gatherPagesCounter,sep=""))
      # get rid of the tabs
      thepage = trimws(gsub("\t"," ",thepage))
      for (entriesCounter in 900:length(thepage)){
           if(thepage[entriesCounter] == '<h2 class=\"result-title\">'){
               entryTitle = trimws(gsub("</a>","",thepage[entriesCounter+2]))

A page number is appended to the search URL noted above…

thepage = readLines(paste(searchURL,"&page=",gatherPagesCounter,sep=""))

…so that we have a URL like below and the program can step through pages 1,2,3…85:

This statement removes tab characters to make each line cleaner for the purposes of looking for the lines we want:

thepage = trimws(gsub("\t"," ",thepage))

The program loops through the lines on the page looking for each line that signifies the start of an article returned from the search: <h2 class=”result-title”>

for (entriesCounter in 900:length(thepage)){
 if(thepage[entriesCounter] == '<h2 class=\"result-title\">'){

Once we know which line number <h2 class=”result-title”> is on, we can get items like the article title that happen to be 2 lines below the line we found:

entryTitle = trimws(gsub("</a>","",thepage[entriesCounter+2]))

This all breaks to smithereens if the web site is redesigned, however it’s working ok for my purposes here which are temporary. I hope it works for you. This technique can be adapted to other similar web sites.

Download each article:

For each search result returned the program also downloads the text of the linked article. As it does that, the program takes a pause for 5 seconds so as not to put a strain on the web servers at the other end of this.

# wait 5 seconds - don't stress the server
p1 <- proc.time()
proc.time() - p1

Results of the program:

  • A comma separated value (.csv) file of each article with the newspaper name, article title, date published, URL and a rough citation.
  • A separate html file with the title and text of each article so that I can read it off-line.
  • An html index to these files for easy reference.

Reading through the articles I can make notes in a spreadsheet listing all of the articles, removing the non-relevant ones and classifying the others. I have applied natural language processing to extract people, location and organization entities from the articles, but I am still evaluating if that provides useful data due to the frequency of errors I’m seeing.

It’s time to let this loose!

The British Gazette, R and Potato Wart virus.

I heard last week from Dr. Graham that one of the grade 11 Law and Society classes at Pontiac High School are using the finding aid for The Equity. I’m glad to hear it’s being accessed for research and thank the class and its teacher for making use of this.

As noted previously, I plan to work on refining the finding aid, including correcting OCR errors. I had been thinking of using Google, but it’s against their terms of service to submit huge numbers of requests. Fair enough. Google’s director of research Peter Norviq published an article about spelling correction using an off-line method. Here is an article describing how to do this in R. To use this I will need to add in additional words, such as local place names, to Noviq’s spell check corpus big.txt. As promising as this seems to be, I will leave this work for another time.

This week’s research faces a different challenge, warts and all. I have been researching the British Gazette‎ for Dr. Y. Aleksandra Bennett’s HIST 4500 seminar on British Society and the Experience of the First World War. The Gazette is a trove of official announcements. One of my areas of inquiry concerns allotment gardens for food production in World War I. The Gazette contains information about the regulations that governed these food gardens during the Great War. The Gazette also contains announcements about the discovery of Potato Wart virus in separate allotment gardens and each notice has the location infected. With 194 of these notices, I believe this is a potentially useful body of data to derive some patterns from. At a minimum I would like to list all of the locations of the allotments in Britain and plot them on a map. Was potato wart a regional or national problem? What was the extent and time-line of the issue? This of course assumes the Gazette is a reliable source for this information.

Getting 194 pages from the Gazette is doable manually, but we can write a program to do that, and then re-use the program for other things.

Using what I learned in HIST 3814, I checked if the Gazette has an API, which it does. In fact there are lots of options to download data in json, XML and some other formats.

I started work on an R program to use the Gazette’s API to search for notices, download them and then parse them for the content I’m looking for. My fail log is here.

I tried to use the json api for the British Gazette but it gave me errors:

> json_file <- ""

> json_data <- fromJSON(file=json_file)

Error in fromJSON(file = json_file) : unexpected character: "

I decided to switch to xml, which has worked fine. Below the program accesses the Gazette and puts the xml into a data frame:

xml_file <- ""

xmlfile <- xmlTreeParse(readLines(xml_file)[1])

topxml <- xmlRoot(xmlfile)

topxml <- xmlSApply(topxml,function(x) xmlSApply(x, xmlValue))

xml_df <- data.frame(t(topxml), row.names=NULL)


As you can see, the URL provides the information to pull the material we want:

Without going on at too much length, the program works through a list of search results ten entries at a time. In this case 194 of them. For each entry, the program then downloads the pdf of the Gazette page the search results appear on. The pdf is converted into text and then parsed. Most of the time, the location of each allotment garden follows the word “SCHEDULE” and using this, we can get a list of all the allotments mentioned and when the notices were published. Here is the list in .csv and on Google docs.

I intend to use natural language processing to extract the location of the allotment as well as the name of the organization that ran it. I think I can use the program for some other extraction as well as composing citations.

More data, better searching, incremental progress.

This week’s progress is represented by incremental improvement. These improvements are not earth shattering, but are necessary to get the full value out of a resource like this.

Full text index

I added full text indexes called ftname to the entities_people, entities_organizations and entities_locations tables.

This allowed the website to present a list of possible related entities.  For example, using a full text search the database can return possible matches for Carmen Burke.  The people table has an entry for Burke Secrétoire-Trésorière.

Under organizations there is listed:

Municipal OfficeCarmen Burke Secrétaire-trésorière0S29
Municipal OfficeCarmen Burke Secrétaire-trésorièrePRIVATE INSTRUCTION
Municipal OUceCarmen Burke Secrétalre-trésoriére

Each of these entries may return an additional reference about Carmen Burke, although I expect a lot of overlap due to the names of different entities appearing multiple times in an article, yet being stored in the database with different spellings due to OCR errors. Regardless, the feature to look up possible related entities will allow a researcher to make sure more needles are found in the haystack of content.

Better search

There is now a small form to search for entities starting with the first 2 letters in the select field.

Characters in names being misinterpreted as HTML

A simple improvement was made to the listing of entity names from the database. Due to OCR errors some characters were represented by less-than brackets (<) and an entity named OOW<i resulted in <i being interpreted as the start of an <i> italic HTML tag, which meant that all the content that followed on the web page was in italics. I didn’t want to tamper with the data itself in order to preserve its integrity so I looked at some options in php to deal with presenting content. The php function htmlspecialchars resulted in a lot of data just not being returned by the function and so empty rows were listed rather than content. Using the following statement


was the least harmful way to present data that had a < in it by replacing it with the HTML glyph &amp;.

Accents in content were mishandled

As noted in last week’s blog, the web pages were presenting Carmen Burke’s French language title of Présidente incorrectly, per below:

Carmen Burke Pr�sidente

Luckily, the database had stored the data correctly:

Carmen Burke Présidente

I say luckily because I did not check that the processing program was storing accented characters correctly and I should have given I know that paper has French language content too. Lesson learned.

Setting the character set in the database connection fixed the presentation, per below.


‘utf8’ is the character set supporting accented characters I want to use. $link represents the database connection.

Completed processing of Equity editions 2000-2010

Yesterday I downloaded the editions of the Equity I was missing from 2000-2010 using wget.

wget -A .txt -r --no-parent -nd –w 2 --limit-rate=20k

I also completed processing the 2000-2010 editions in the R program. This ran to completion while I was out at a wedding so this is much faster than it used to be.

I backed up the database on the web and then imported all of the data again from my computer so that the website now has a complete database of entities from 1883-2010.  According to the results, there are 629,036 person, 500,055 organization and 114,520 location entities in the database.



A web site to browse results and better processing performance.

Database indexes make a difference.

Since my last blog post the R program that is processing The Equity files has been running 24 hours a day and as of Saturday it reached 1983. However, I noticed that the time it took to process each issue was getting longer and it seemed that this is taking far too long in general.

I went back to an earlier idea I had to add more indexes to the tables in the MySql database of results. I had been reluctant to do this since adding an additional index to a database table can make updating that table take longer due to the increased time to update the additional index. At first I added an index just to the `entities_people` table to have an index on the name column. [KEY `names` (`name`)] Adding this index made no visible difference to the processing time, likely because this table already had an index to keep the name column unique. [UNIQUE KEY `name_UNIQUE` (`name`)]

Then I added indexes to the cross reference tables that relate each of the entities (people, locations, organizations) to the source documents. [KEY `ids` (`id_entities_person`,`id_source_document`)]

After adding these indexes, processing time really sped up. During the short time I have spent writing these paragraphs three months of editions have been processed. Its no surprise that adding indexes also improved the response time of web pages returning results from the database.

Browse The Equity by topic on this basic web site.

A simple web site is now available to browse by topic and issue. As you can see, thousands of person, location and organization entities have been extracted. Currently only the first 10,000 of them are listed in search results given I don’t want to cause the people hosting this web site any aggravation with long running queries on their server. I plan to improve the searching so that it’s possible to see all of the results but in smaller chunks. I would like to add a full text search, but I am somewhat concerned about that being exploited to harm the web site.

As of today there is a list of issues and for each issue there is a list of the people, organizations and locations that appear in it. All of the issues that an entity’s name appears in can also be listed, such as the Quyon Fair. Do you see any problems in the data as presented? Are there other ways you would like to interact with it? I plan to make the database available for download in the future, once I get it to a more finalized form.

There is a lot of garbage or “diamonds in the rough” here. I think it’s useful to see that in order to show the level of imperfection of what has been culled from scanned text of The Equity, but also to find related information. Take, for example, Carmen Burke:

Carmen Burke President
Carmen Burke Pr�sidente
Carmen Burke Sec TreesLiving
Carmen Burke Secr�ta
Carmen Burke Secr�tair
Carmen Burke Secr�taire
Carmen Burke Secr�taire-tr�sori�re
Carmen Burke Secr�taire-tr�sori�re Campbell
Carmen Burke Secr�taire-tr�sorl�re Campbell
Carmen Burke Secr�taire-Tr�sort�re
Carmen Burke Secr�talre-tr�sori�reWill
Carmen Burke Secr�talre-tr�sori�reX206GENDRON
Carmen Burke Secr�talre-tr�sorl�reOR
Carmen Burke Secretary
Carmen Burke Secretary Treasurer
Carmen Burke Secretary-treasurerLb Premier Jour
Carmen Burke Seter

Cleaning these results is a challenge I continue to think about. A paper I will be looking at in more depth is OCR Post-Processing Error Correction Algorithm Using Google’s Online Spelling Suggestion by Youssef Bassil and Mohammad Alwani.

Happy searching, I hope you find some interesting nuggets of information in these preliminary results. Today the web database has the editions from 1883-1983 and I will be adding more in the coming weeks.