{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Finding non-English newspapers in Trove\n",
"\n",
"There are a growing number of non-English newspapers digitised in Trove. However, if you're only searching using English keywords, you might never know that they're there. I thought it would be useful to generate a list of non-English newspapers, but it wasn't quite as straightforward as I thought.\n",
"\n",
"## How not to do it...\n",
"\n",
"My first thought was I could start by searching for digitised newspapers amongst the library records in Trove. My theory was that catalogue metadata would include language information. For example, you can search for newspapers using `format:Periodical/Newspaper` in the books and libraries category (or the `article` API zone). To find those that are digitised, you can add a search for 'trove.nla.gov.au'. Here's the [sort of results](https://trove.nla.gov.au/search/category/books?keyword=%22trove.nla.gov.au%22%20format%3APeriodical%2FNewspaper) you get. Unfortunately, you only get about 826 results and there are many more newspapers than that in Trove. It seems links to digitised newspapers are not consistently recorded.\n",
"\n",
"My second approach was to get the list of digitised newspapers from the API, extract the ISSN, then use this to search for catalogue records. Here's the code snippet I used.\n",
"\n",
"``` python\n",
"params = {\n",
" 'zone': 'article',\n",
" 'encoding': 'json',\n",
" 'l-format': 'Periodical/Newspaper',\n",
" 'reclevel': 'full',\n",
" 'key': TROVE_API_KEY\n",
"}\n",
"newspapers = get_newspapers()\n",
"for newspaper in newspapers:\n",
" print(f'\\n{newspaper[\"title\"]}')\n",
" issn = newspaper.get('issn')\n",
" params['q'] = f'issn:{issn}'\n",
" response = s.get('https://api.trove.nla.gov.au/v2/result', params=params)\n",
" data = response.json()\n",
" try:\n",
" works = data['response']['zone'][0]['records']['work']\n",
" except KeyError:\n",
" print('Not found')\n",
" else:\n",
" for work in works:\n",
" print(work.get('language'))\n",
" if not response.from_cache:\n",
" time.sleep(0.2)\n",
"```\n",
"\n",
"The main problem here is that not all titles have ISSNs. You could try searching on the titles is there's no ISSN, but this would involve a fair bit of disambiguation. In any case, in running this I discovered that while there is some language information in the metadata, it's not consistently applied. So basically a metadata-only approach is not going to work. Sigh...\n",
"\n",
"## How I actually did it\n",
"\n",
"If I couldn't get language details from metadata, then I had to try and extract it from the resource itself. I spent quite a bit of time looking around for Python packages that provided reliable language detection. The first one I tried regularly identified Mandarin as Korean (it turns out this was a known issue). Another one sent me into dependency hell. Finally I found [pycld3](https://pypi.org/project/pycld3/) which installed with `pip`, and *just worked*.\n",
"\n",
"My plan was to get the list of newspapers via the API as before, then fire off an empty search for each one. I'd then loop through the results, running the language detector over the article text. I set the query parameters to retrieve the maxmimum number of results in one request – 100. That seemed like a reasonable sample. To try and provide a big enough amount of text for the language detector to work with, I set the number of words parameter to return articles with between 100 and 1000 words. So the query parameters I used were:\n",
"\n",
"``` python\n",
"params = {\n",
" 'zone': 'newspaper',\n",
" 'encoding': 'json',\n",
" 'l-word': '100 - 1000 Words',\n",
" 'include': 'articletext',\n",
" 'key': TROVE_API_KEY,\n",
" 'q': ' ',\n",
" 'n': 100,\n",
"}\n",
"```\n",
"\n",
"Because some of the newspapers had short runs and the word count filter limits the results, I found that I wasn't always getting 100 results per newspaper. To work around this I found the likely language for each article, aggregated the counts, and then calculated the proportion of results for each language. This gave me the proportion of articles in each language – a number I could use across newspapers to find the non-English titles. \n",
"\n",
"In general this worked pretty well, and the result was a [list of 48 newspapers](non-english-newspapers.md) (also as a [Gist](https://gist.github.com/wragge/9aa385648cff5f0de0c7d4837896df97)) that have significant amounts of non-English content. However, I had to do a fair bit of fiddling to filter out dodgy results. All the details are included below.\n",
"\n",
"## Problems / limitations\n",
"\n",
"* It's no surprise that the results of the language detection are affected by the quality of the OCR. \n",
"* In filtering out what seems to be the product of dodgy OCR, it's possible that I might be excluding some non-English content. \n",
"* I'm only detecting the predominant language for each article, so there might be articles containing a mix of languages that are being missed. \n",
"* I'm just talking the first 100 results from a blank search in each newspaper. Larger, or more randomised samples might produce different results.\n",
"* Some dodgy detection results remain in the list of newspapers, but the point of this exercise was to find non-English newspapers. If you wanted to accurately determine the quantity of non-English content, you'd have to do a lot more fine-grained analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import what we need"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import time\n",
"import requests_cache\n",
"from requests.adapters import HTTPAdapter\n",
"from requests.packages.urllib3.util.retry import Retry\n",
"from collections import Counter\n",
"import re\n",
"from langdetect import detect\n",
"from tqdm.auto import tqdm\n",
"import pandas as pd\n",
"import cld3\n",
"import pycountry\n",
"from language_tags import tags\n",
"import altair as alt\n",
"from pathlib import Path\n",
"\n",
"s = requests_cache.CachedSession()\n",
"retries = Retry(total=5, backoff_factor=1, status_forcelist=[ 502, 503, 504 ])\n",
"s.mount('https://', HTTPAdapter(max_retries=retries))\n",
"s.mount('http://', HTTPAdapter(max_retries=retries))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"TROVE_API_KEY = '[YOUR API KEY]'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Harvest the data and run language detection on articles"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_newspapers():\n",
" '''\n",
" Get a list of newspapers in Trove.\n",
" '''\n",
" response = s.get('https://api.trove.nla.gov.au/v2/newspaper/titles', params={'encoding': 'json', 'key': TROVE_API_KEY})\n",
" data = response.json()\n",
" return data['response']['records']['newspaper']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7e7df2aa932c478aaf2c1e6832b0f9fd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=1622.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"params = {\n",
" 'zone': 'newspaper',\n",
" 'encoding': 'json',\n",
" #'l-category': 'Article',\n",
" 'l-word': '100 - 1000 Words',\n",
" 'include': 'articletext',\n",
" 'key': TROVE_API_KEY,\n",
" 'q': ' ',\n",
" 'n': 100,\n",
"}\n",
"newspaper_langs = []\n",
"newspapers = get_newspapers()\n",
"for newspaper in tqdm(newspapers):\n",
" langs = []\n",
" # print(f'\\n{newspaper[\"title\"]}')\n",
" params['l-title'] = newspaper['id']\n",
" response = s.get('https://api.trove.nla.gov.au/v2/result', params=params)\n",
" data = response.json()\n",
" n = data['response']['zone'][0]['records']['n']\n",
" try:\n",
" articles = data['response']['zone'][0]['records']['article']\n",
" except KeyError:\n",
" # print('Not found')\n",
" pass\n",
" else:\n",
" # Detect language for each article in results\n",
" for article in articles:\n",
" if 'articleText' in article:\n",
" # Clean up OCRd text by removing takings and extra whitespace\n",
" text = article['articleText']\n",
" text = re.sub('<[^<]+?>', '', text)\n",
" text = re.sub(\"\\s\\s+\", \" \", text)\n",
" # Get the language\n",
" ld = cld3.get_language(text)\n",
" # If the language prediction is reliable, save it\n",
" if ld.is_reliable:\n",
" langs.append(ld.language)\n",
" # Find the count of each language detected in the sample of articles\n",
" for lang, count in dict(Counter(langs)).items():\n",
" # Calculate the language count as a proportion of the total number of results\n",
" prop = int(count) / len(langs)\n",
" newspaper_langs.append({'id': newspaper['id'], 'title': newspaper['title'], 'language': lang, 'proportion': prop, 'number': n})\n",
" if not response.from_cache:\n",
" time.sleep(0.2)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert the results into a dataframe."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" title | \n",
" language | \n",
" proportion | \n",
" number | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 166 | \n",
" Canberra Community News (ACT : 1925 - 1927) | \n",
" en | \n",
" 1.0 | \n",
" 100 | \n",
"
\n",
" \n",
" 1 | \n",
" 165 | \n",
" Canberra Illustrated: A Quarterly Magazine (AC... | \n",
" en | \n",
" 1.0 | \n",
" 29 | \n",
"
\n",
" \n",
" 2 | \n",
" 69 | \n",
" Federal Capital Pioneer (Canberra, ACT : 1924 ... | \n",
" en | \n",
" 1.0 | \n",
" 100 | \n",
"
\n",
" \n",
" 3 | \n",
" 871 | \n",
" Good Neighbour (ACT : 1950 - 1969) | \n",
" en | \n",
" 1.0 | \n",
" 100 | \n",
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\n",
" \n",
" 4 | \n",
" 665 | \n",
" Student Notes/Canberra University College Stud... | \n",
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"text/plain": [
" id title language \\\n",
"0 166 Canberra Community News (ACT : 1925 - 1927) en \n",
"1 165 Canberra Illustrated: A Quarterly Magazine (AC... en \n",
"2 69 Federal Capital Pioneer (Canberra, ACT : 1924 ... en \n",
"3 871 Good Neighbour (ACT : 1950 - 1969) en \n",
"4 665 Student Notes/Canberra University College Stud... en \n",
"\n",
" proportion number \n",
"0 1.0 100 \n",
"1 1.0 29 \n",
"2 1.0 100 \n",
"3 1.0 100 \n",
"4 1.0 100 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(newspaper_langs)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add full language names\n",
"\n",
"The language detector returns BCP-47-style language codes. To translate these into something that's a bit easier for humans to understand, we can use the [language-tags](https://github.com/OnroerendErfgoed/language-tags) package."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"def get_full_language(lc):\n",
" '''\n",
" Get full language names from codes\n",
" '''\n",
" lang = tags.description(lc)\n",
" if lang:\n",
" return lang[0]\n",
" else:\n",
" print(lc)\n",
" return lc\n",
"\n",
"df['language_full'] = df['language'].apply(get_full_language)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering the results\n",
"\n",
"If we just look at the numbers of languages detected we might think that Australia's cultural diversity was much greater than we expected! But the likelihood that there were ten newspapers publishing articles in Igbo (the language of the Igbo people in south-eastern Nigeria) seems small. Obviously there are a considerable number of false positives here."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"English 1565\n",
"Maltese 279\n",
"Catalan 53\n",
"Welsh 35\n",
"Japanese 31\n",
"Italian 31\n",
"Somali 24\n",
"Norwegian 23\n",
"Danish 17\n",
"German 16\n",
"Samoan 10\n",
"Igbo 10\n",
"Portuguese 9\n",
"French 9\n",
"Chinese 8\n",
"Estonian 8\n",
"Scottish Gaelic 8\n",
"Luxembourgish 8\n",
"Vietnamese 7\n",
"Western Frisian 7\n",
"Hawaiian 7\n",
"Russian 6\n",
"Modern Greek (1453-) 5\n",
"Swedish 5\n",
"Filipino 5\n",
"Afrikaans 4\n",
"Javanese 4\n",
"Indonesian 4\n",
"Polish 4\n",
"Hindi 4\n",
"Bulgarian 4\n",
"Corsican 4\n",
"Dutch 3\n",
"Malagasy 3\n",
"Haitian 3\n",
"Latin 3\n",
"Malay (macrolanguage) 3\n",
"Albanian 2\n",
"Spanish 2\n",
"Shona 2\n",
"Kurdish 2\n",
"Cebuano 2\n",
"Irish 2\n",
"Ukrainian 2\n",
"Bosnian 2\n",
"Macedonian 1\n",
"Slovak 1\n",
"Galician 1\n",
"Turkish 1\n",
"Czech 1\n",
"Lithuanian 1\n",
"Croatian 1\n",
"Slovenian 1\n",
"Zulu 1\n",
"Maori 1\n",
"Marathi 1\n",
"Name: language_full, dtype: int64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['language_full'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that for each language detected in a newspaper we calculated the proportion of articles in our results set in that language. So we can, for example, just look at newspapers where 100% of the articles are in a single language. This highlights a few non-English language newspapers, but obviously we're missing a lot of others."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"English 1112\n",
"Italian 3\n",
"German 3\n",
"Modern Greek (1453-) 1\n",
"Portuguese 1\n",
"Estonian 1\n",
"Name: language_full, dtype: int64"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[df['proportion'] == 1]['language_full'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we chart the proportions, we see them bunched up at either end of the scale. So there are lots of languages detected in only a small proportion of articles."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(df).mark_bar().encode(\n",
" x=alt.X('proportion:Q', bin=True),\n",
" y='count():Q'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we zoom in on the proportions less than 0.1 (that's 10 articles in a sample of 100) we see that they're mostly less that 0.01 (or 1 article in 100). It seems likely that these are false positives. "
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(df.loc[df['proportion'] < 0.1]).mark_bar().encode(\n",
" x=alt.X('proportion:Q', bin=True),\n",
" y='count():Q'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's be fairly conservative and filter out languages that have a proportion (per newspaper) less than 0.5. This list seems a bit more in line with what we would expect, but there are still some surprises – 48 newspapers published articles in Maltese?"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"English 1559\n",
"Maltese 48\n",
"Italian 14\n",
"German 9\n",
"Chinese 8\n",
"Catalan 6\n",
"Somali 5\n",
"Modern Greek (1453-) 4\n",
"Japanese 3\n",
"Portuguese 3\n",
"Polish 3\n",
"Western Frisian 2\n",
"Dutch 2\n",
"French 2\n",
"Spanish 1\n",
"Ukrainian 1\n",
"Malay (macrolanguage) 1\n",
"Welsh 1\n",
"Indonesian 1\n",
"Russian 1\n",
"Danish 1\n",
"Scottish Gaelic 1\n",
"Bosnian 1\n",
"Estonian 1\n",
"Vietnamese 1\n",
"Macedonian 1\n",
"Lithuanian 1\n",
"Bulgarian 1\n",
"Samoan 1\n",
"Name: language_full, dtype: int64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[df['proportion'] >= 0.05]['language_full'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we focus in on the newspapers that supposedly have a significant proportion of articles in Maltese, we see some very strange results. I seriously doubt that 80% of the *Mildura Irrigationist* from 1892-3 is in Maltese. So what's going on?"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" 100 | \n",
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\n",
" \n",
" 637 | \n",
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" 100 | \n",
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\n",
" \n",
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\n",
" \n",
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" Upper Hunter Courier (Murrurundi, NSW : 1871) | \n",
" mt | \n",
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" 14 | \n",
" Maltese | \n",
"
\n",
" \n",
" 834 | \n",
" 1207 | \n",
" The Coolangatta Chronicle (Qld. : 1926) | \n",
" mt | \n",
" 0.130435 | \n",
" 26 | \n",
" Maltese | \n",
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\n",
" \n",
" 884 | \n",
" 892 | \n",
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" 100 | \n",
" Maltese | \n",
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\n",
" \n",
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" 34 | \n",
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" mt | \n",
" 0.486111 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1513 | \n",
" 384 | \n",
" North Melbourne Gazette (Vic. : 1894 - 1901) | \n",
" mt | \n",
" 0.146341 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1577 | \n",
" 318 | \n",
" Sandringham Southern Cross (Vic. : 1914 - 1918) | \n",
" mt | \n",
" 0.312500 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1619 | \n",
" 13 | \n",
" The Argus (Melbourne, Vic. : 1848 - 1957) | \n",
" mt | \n",
" 0.629630 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1715 | \n",
" 1583 | \n",
" The Mildura Irrigationist (Vic. : 1892 - 1893) | \n",
" mt | \n",
" 0.795455 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1719 | \n",
" 1581 | \n",
" The Mildura Irrigationist and Murray River Agr... | \n",
" mt | \n",
" 0.750000 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1721 | \n",
" 1582 | \n",
" The Mildura Irrigationist and Murray River Cul... | \n",
" mt | \n",
" 0.333333 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
" 1988 | \n",
" 1543 | \n",
" Murchison Times and Cue-Big Bell-Reedy Advocat... | \n",
" mt | \n",
" 0.137500 | \n",
" 100 | \n",
" Maltese | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id title language \\\n",
"218 1596 L'Italo-Australiano = The Italo-Australian (Su... mt \n",
"308 623 Sunday News (Sydney, NSW : 1919) mt \n",
"400 224 The Castlereagh (Gilgandra, NSW : 1905 - 1907) mt \n",
"568 500 The Richmond River Express and Casino Kyogle A... mt \n",
"637 452 The Sydney Wool and Stock Journal (NSW : 1899 ... mt \n",
"710 394 Twofold Bay and Maneroo Observer (NSW : 1860) mt \n",
"719 810 Upper Hunter Courier (Murrurundi, NSW : 1871) mt \n",
"834 1207 The Coolangatta Chronicle (Qld. : 1926) mt \n",
"884 892 Warwick Daily News (Qld. : 1919 -1954) mt \n",
"1028 34 The Advertiser (Adelaide, SA : 1889 - 1931) mt \n",
"1513 384 North Melbourne Gazette (Vic. : 1894 - 1901) mt \n",
"1577 318 Sandringham Southern Cross (Vic. : 1914 - 1918) mt \n",
"1619 13 The Argus (Melbourne, Vic. : 1848 - 1957) mt \n",
"1715 1583 The Mildura Irrigationist (Vic. : 1892 - 1893) mt \n",
"1719 1581 The Mildura Irrigationist and Murray River Agr... mt \n",
"1721 1582 The Mildura Irrigationist and Murray River Cul... mt \n",
"1988 1543 Murchison Times and Cue-Big Bell-Reedy Advocat... mt \n",
"\n",
" proportion number language_full \n",
"218 0.222222 100 Maltese \n",
"308 0.219178 100 Maltese \n",
"400 0.105882 100 Maltese \n",
"568 0.168675 100 Maltese \n",
"637 0.233766 100 Maltese \n",
"710 0.139535 100 Maltese \n",
"719 0.142857 14 Maltese \n",
"834 0.130435 26 Maltese \n",
"884 0.139241 100 Maltese \n",
"1028 0.486111 100 Maltese \n",
"1513 0.146341 100 Maltese \n",
"1577 0.312500 100 Maltese \n",
"1619 0.629630 100 Maltese \n",
"1715 0.795455 100 Maltese \n",
"1719 0.750000 100 Maltese \n",
"1721 0.333333 100 Maltese \n",
"1988 0.137500 100 Maltese "
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[(df['proportion'] > 0.1) & (df['language_full'] == 'Maltese')]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you look at results for the *Mildura Irrigationist* [in Trove](https://trove.nla.gov.au/search/advanced/category/newspapers?l-advtitle=1583&l-advWord=100%20-%201000%20Words) you'll see that many of the page images are blurry, and as a result the OCR is very, very bad. Here's a sample:\n",
"\n",
"> ill Tatr W lyltwililUmt aat aa«v aa MwOkaWtOPMlkMrf faiflftMMRltitlWBfMNM fmiMW^M^K IMIOHIpM^fQBMMI ft tWMmrwl tWWiltjfNMStW ffw aailwt«M wtMitiar«lH*a ifcmH af tlw ial«««l ion «M««f ffantoif wwtMaaM. tto tf h «frwringmhw torf M hr toaiy. Im*4. ar, fc> mmirf awlUW wefllaM aA. aaytMaa. l «Wa A tfc» tow waliw Macks b aaM, b wil fVfbH Ja ^IMntaam* Mm' ls tolliac. rt Tto aad nf ttoar UhKMimiw*a afM» ftjrwl ans W l OtfWOar jpaaofTwSi aJwwr la'aahS^*— attor aakwt mm rvfimMiMh* ttoai. day - Why. aa IH thrf t«fl almd yaa.\"iw. aal wwifciha m OiO all tto laM amnavaA, fawawNl I r aa4 f wa* tm enr a Mtcfc tto watrr tto wiaaal m a* a* day pfaMat. aa4 (h* ilj amintir* ilm tTtsjtvL.f**' \"\"j •fria—lhati* tow ««4M k.\" tlml t | r 4m» wtn .aa rUa* I h ha«« t ctoantaf InMM* aM*toclt ttopnaMaf II It la Mat rtgM, t jmi awl a 1 : af but d awtliqg a Mr. Jafc Matwa-(MMa M t «wl y gha yaar «toa anl yaar (ma as «fpai ta af t«l. i pwwiaf Mtan (tot jw. twy MwUI «*a1 a«ry ftajr «ndl tar tlw aad annaH* a*«r aarf a««r aaria. tiaa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens when we feed this fragment of bad OCR to the language detector? Remarkably, the language detector is 96% sure that it's Maltese! To find out why this is the case, we'd probably have to dig into the way the language detection model was trained. But for our purposes it's enough to know that some of the languages detected seem to be the result of bad OCR."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LanguagePrediction(language='mt', probability=0.960280179977417, is_reliable=True, proportion=1.0)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ocr = '''ill Tatr W lyltwililUmt aat aa«v aa MwOkaWtOPMlkMrf faiflftMMRltitlWBfMNM fmiMW^M^K IMIOHIpM^fQBMMI ft tWMmrwl tWWiltjfNMStW ffw aailwt«M wtMitiar«lH*a ifcmH af tlw ial«««l ion «M««f ffantoif wwtMaaM. tto tf h «frwringmhw torf M hr toaiy. Im*4. ar, fc> mmirf awlUW wefllaM aA. aaytMaa. l «Wa A tfc» tow waliw Macks b aaM, b wil fVfbH Ja ^IMntaam* Mm' ls tolliac. rt Tto aad nf ttoar UhKMimiw*a afM» ftjrwl ans W l OtfWOar jpaaofTwSi aJwwr la'aahS^*— attor aakwt mm rvfimMiMh* ttoai. day - Why. aa IH thrf t«fl almd yaa.\"iw. aal wwifciha m OiO all tto laM amnavaA, fawawNl I r aa4 f wa* tm enr a Mtcfc tto watrr tto wiaaal m a* a* day pfaMat. aa4 (h* ilj amintir* ilm tTtsjtvL.f**' \"\"j •fria—lhati* tow ««4M k.\" tlml t | r 4m» wtn .aa rUa* I h ha«« t ctoantaf InMM* aM*toclt ttopnaMaf II It la Mat rtgM, t jmi awl a 1 : af but d awtliqg a Mr. Jafc Matwa-(MMa M t «wl y gha yaar «toa anl yaar (ma as «fpai ta af t«l. i pwwiaf Mtan (tot jw. twy MwUI «*a1 a«ry ftajr «ndl tar tlw aad annaH* a*«r aarf a««r aaria. tiaa'''\n",
"cld3.get_language(ocr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course there might actually be newspapers with articles in Maltese, so we don't want to filter them all out. So let's do some manual inspection of the newspapers that *seem* to have non-English content. First we'll filter our results to include only languages with proportions of more than 0.05, and then drop out newspapers that seem to be only in English. We end up with 105 different titles. "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"105"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The filter on the groupby drops out newspapers that only have articles in English.\n",
"filtered = df.loc[df['proportion'] >= 0.05].groupby(by=['title', 'id']).filter(lambda x: (len(x) > 1) or (len(x)== 1 and x['language'] != 'en'))\n",
"papers = filtered.groupby(by=['title', 'id'])\n",
"len(papers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's list those 105 newspapers. From the list below, I think it's pretty easy to pick out the results that are likely to be the product of bad OCR."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"A Voz de Timor (Dili, East Timor : 1970 - 1975) (1498)\n"
]
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"Geraldton Advocate and Johnstone River Guardian (Qld. : 1895 - 1896) (1103)\n"
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" 1879 | \n",
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" mt | \n",
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"Guang yi hua bao = The Chinese Australian Herald (Sydney, NSW : 1894 - 1923) (704)\n"
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},
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" 170 | \n",
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" zh | \n",
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" \n",
" 173 | \n",
" Western Frisian | \n",
" fy | \n",
" 0.075758 | \n",
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"Hamilton Spectator and Grange District Advertiser (South Melbourne, Vic. : 1860 - 1870) (927)\n"
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},
{
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" 1409 | \n",
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"text": [
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"Healesville Guardian (Vic. : 1893 - 1898) (140)\n"
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{
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" 1416 | \n",
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"text": [
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"Hellenic Echo (Perth, WA : 1967 - 1968) (1389)\n"
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"Il Risveglio = The Awakening (Sydney, NSW : 1944 - 1954) (1601)\n"
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" 189 | \n",
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" 200 | \n",
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" 204 | \n",
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" en | \n",
" 0.090909 | \n",
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"Kyabram Union (Vic. : 1886 - 1894) (196)\n"
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"text": [
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"L'Italo-Australiano = The Italo-Australian (Surry Hills, NSW : 1885) (1596)\n"
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" it | \n",
" 0.928571 | \n",
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" \n",
" 1943 | \n",
" English | \n",
" en | \n",
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"Le Courrier Australien (Sydney, NSW : 1892 - 2011) (829)\n"
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" fr | \n",
" 0.816327 | \n",
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" 229 | \n",
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" en | \n",
" 0.173469 | \n",
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" el | \n",
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" \n",
" 1955 | \n",
" English | \n",
" en | \n",
" 0.281250 | \n",
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" Portuguese | \n",
" pt | \n",
" 0.104167 | \n",
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" 1956 | \n",
" French | \n",
" fr | \n",
" 0.062500 | \n",
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" 0.052083 | \n",
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"text": [
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"Meie Kodu = Our Home (Sydney, NSW : 1949 - 1956) (280)\n"
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" 238 | \n",
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" et | \n",
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" English | \n",
" en | \n",
" 0.8250 | \n",
"
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" \n",
" 1988 | \n",
" Maltese | \n",
" mt | \n",
" 0.1375 | \n",
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"Mu̇sų Pastogė = Our Haven (Sydney, NSW : 1950 - 1954) (1594)\n"
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" 250 | \n",
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" lt | \n",
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"text": [
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"Narandera Argus and Riverina Advertiser (NSW : 1893 - 1953) (431)\n"
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},
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" en | \n",
" 0.940476 | \n",
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" 255 | \n",
" Maltese | \n",
" mt | \n",
" 0.059524 | \n",
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"text": [
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"Nasza droga (Adelaide, SA : 1952 - 1954) (1323)\n"
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" 948 | \n",
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" Danish | \n",
" da | \n",
" 0.413043 | \n",
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" mt | \n",
" 0.065217 | \n",
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" 269 | \n",
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" it | \n",
" 0.425532 | \n",
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" mt | \n",
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" 286 | \n",
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" 288 | \n",
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" mt | \n",
" 0.084337 | \n",
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" 1565 | \n",
" English | \n",
" en | \n",
" 0.93617 | \n",
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" 1566 | \n",
" Maltese | \n",
" mt | \n",
" 0.06383 | \n",
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"text": [
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"Rockhampton Bulletin and Central Queensland Advertiser (Qld. : 1861 - 1871) (92)\n"
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"The Port Phillip Patriot and Morning Advertiser (Vic. : 1845 - 1848) (937)\n"
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"The Richmond River Express and Casino Kyogle Advertiser (NSW : 1904 - 1929) (500)\n"
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"The Voice of Freedom = Elefthera Phoni (Perth, WA : 1956 - 1957) (1381)\n"
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"text": [
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" it | \n",
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"Upper Hunter Courier (Murrurundi, NSW : 1871) (810)\n"
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" 2234 | \n",
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" mk | \n",
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"
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" \n",
" 2233 | \n",
" English | \n",
" en | \n",
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" 2235 | \n",
" Bulgarian | \n",
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]
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" 720 | \n",
" Ukrainian | \n",
" uk | \n",
" 0.82 | \n",
"
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" 721 | \n",
" English | \n",
" en | \n",
" 0.18 | \n",
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"Warwick Daily News (Qld. : 1919 -1954) (892)\n"
]
},
{
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" 884 | \n",
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"text": [
"\n",
"Williamstown Trade Circular (Vic. : 1855 - 1856) (213)\n"
]
},
{
"data": {
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" proportion | \n",
"
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" \n",
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" 1792 | \n",
" English | \n",
" en | \n",
" 0.875 | \n",
"
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" \n",
" 1793 | \n",
" Portuguese | \n",
" pt | \n",
" 0.125 | \n",
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"
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"
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],
"source": [
"for n, l in papers:\n",
" if not l.loc[(~df['language'].isin(['en'])) & (df['proportion'] >= 0.05)].empty:\n",
" print(f'\\n{n[0]} ({n[1]})')\n",
" display(l[['language_full', 'language', 'proportion']].loc[(l['proportion'] > 0.05)].sort_values(by='proportion', ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I went through the titles above and compiled a list of title identifiers that seem to be producing dodgy results. We can use this to filter these newspapers out of our results."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Titles where dodgy OCR causes false positives in language detection\n",
"# This was manually created after scanning results\n",
"dodgy = ['1036', '1043', '1103', '116', '1207', '1265', '13', '1320', '1336', '140', '1400', '145', '1488', '1543', '1546', '1581', '1582', '1583', '1623', '1626', '1678', '171', '196', '213', '224', '286', '292', '318', '329', '34', '384', '389', '394', '418', '430', '431', '452', '479', '499', '500', '570', '623', '763', '810', '860', '886', '892', '906', '92', '926', '927', '935', '937', '94', '946', '970', '986']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we'll add the dodgy title ids into our filter. It seems that we have 48 newspapers with significant amounts of non-English content."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"48"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The filter removes titles that only have one language, which is English\n",
"filtered = df.loc[(~df['id'].isin(dodgy)) & (df['proportion'] >= 0.05)].groupby(by=['title', 'id']).filter(lambda x: (len(x) > 1) or (len(x)== 1 and x['language'] != 'en'))\n",
"papers = filtered.groupby(by=['title', 'id'])\n",
"len(papers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's list them."
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A Voz de Timor (Dili, East Timor : 1970 - 1975)\n",
"Adelaider Deutsche Zeitung (SA : 1851 - 1862)\n",
"Australische Zeitung (Adelaide, SA : 1875 - 1916)\n",
"Berita Repoeblik (Djakarta, Indonesia : 1945 - 1946)\n",
"Chinese Republic News (Sydney, NSW : 1914 - 1937)\n",
"Chinese Times (Melbourne, Vic. : 1902 - 1922)\n",
"Chung Wah News (Perth, WA : 1981 - 1987)\n",
"Der Australische Spiegel = The Australian Mirror (Perth, WA : 1952)\n",
"Deutsch-Australische Post : Wochenschrift = German-Australian Post : Weekly (Sydney, NSW : 1893 - 1906)\n",
"Deutsche Zeitung für Sud-Australien = German Times for South Australia (Tanunda, SA : 1851)\n",
"Die Brucke = The Bridge (Sydney, NSW : 1934 - 1939)\n",
"Die Deutsche Post für die Australischen Colonien = The German Australian Post (Adelaide, SA : 1848 - 1851)\n",
"Dutch Australian Weekly (Sydney, NSW : 1951 - 1993)\n",
"Dutch Weekly (Sydney, NSW : 1993 - 2004)\n",
"Echo : Polski Tygodnik Niezalezny (Perth, WA : 1950 - 1952)\n",
"Eco Italiano (Perth, WA : 1958 - 1959)\n",
"Guang yi hua bao = The Chinese Australian Herald (Sydney, NSW : 1894 - 1923)\n",
"Hellenic Echo (Perth, WA : 1967 - 1968)\n",
"Il Canguro = The Kangaroo (Perth, WA : 1955 - 1957)\n",
"Il Giornale Italiano (Sydney, NSW : 1932 - 1940)\n",
"Il Risveglio = The Awakening (Sydney, NSW : 1944 - 1954)\n",
"Italian Bulletin of Australia (Sydney, NSW : 1922 - 1928, 1935 - 1940)\n",
"Italian Bulletin of Commerce (Sydney, NSW : 1929 - 1935)\n",
"Italo-Australian (Sydney, NSW : 1927 - 1940)\n",
"Japanese Perth Times (Subiaco, WA : 1989 - 1996)\n",
"L'Italo-Australiano = The Italo-Australian (Surry Hills, NSW : 1885)\n",
"L'Italo-Australiano = The Italo-Australian (Sydney, NSW : 1905 - 1909)\n",
"La Rondine (Perth, WA : 1969 - 1994)\n",
"Le Courrier Australien (Sydney, NSW : 1892 - 2011)\n",
"Mediterranean Voice (Perth, WA : 1971 - 1972)\n",
"Meie Kodu = Our Home (Sydney, NSW : 1949 - 1956)\n",
"Mu̇sų Pastogė = Our Haven (Sydney, NSW : 1950 - 1954)\n",
"Nasza droga (Adelaide, SA : 1952 - 1954)\n",
"Norden (Melbourne, Vic. : 1914 - 1918)\n",
"Oceania (Sydney, NSW : 1913 - 1915)\n",
"Stampa Italiana = The Italian Press (Perth, WA : 1931 - 1932)\n",
"Suedaustralische Zeitung (Adelaide, SA : 1850 - 1851)\n",
"Sunday Times Edizione Italiana (Perth, WA : 1958 - 1959)\n",
"Süd Australische Zeitung (Tanunda and Adelaide, SA : 1860 - 1874)\n",
"The Chinese Advertiser (Ballarat, Vic. : 1856)\n",
"The English and Chinese Advertiser (Vic. : 1856 - 1858)\n",
"The Voice of Freedom = Elefthera Phoni (Perth, WA : 1956 - 1957)\n",
"To Ethnico Vema = Greek National Tribune (Arncliffe, NSW : 1931 - 1954)\n",
"Tung Wah News (Sydney, NSW : 1898 - 1902)\n",
"Tung Wah Times (Sydney, NSW : 1901 - 1936)\n",
"Uniamoci (Sydney, NSW : 1903 - 1904)\n",
"Vesnik (Perth, WA : 1975 - 1994)\n",
"Vil'na Dumka = Free Thought (Sydney, NSW : 1949 - 1954)\n"
]
}
],
"source": [
"for n, l in papers:\n",
" print(n[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's looking pretty good. Let's save the results as a Markdown file to make it easy to explore. We'll include links into Trove. Here's the [list of all 48 newspapers](non-english-newspapers.md) (also as a [Gist](https://gist.github.com/wragge/9aa385648cff5f0de0c7d4837896df97))."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"with open(Path('non-english-newspapers.md'), 'w') as md_file:\n",
" i = 1\n",
" for n, l in papers:\n",
" md_file.write(f'\\n### {i}. [{n[0]}](http://nla.gov.au/nla.news-title{n[1]})\\n\\n')\n",
" md_file.write('| Language | Language code | Proportion of sample |\\n')\n",
" md_file.write('|---|---|---|\\n')\n",
" for row in l[['language_full', 'language', 'proportion']].loc[(l['proportion'] > 0.05)].sort_values(by='proportion', ascending=False).itertuples():\n",
" md_file.write(f'| {row.language_full} | {row.language} | {row.proportion} |\\n')\n",
" i += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you look at the Markdown files you'll see that there are still some dodgy results – for example, 16% of the *Chinese Advertiser* is detected as 'Scottish Gaelic'. But the point of this exercise was to find non-English newspapers, rather than accurately detect the proportion of non-English content, so I think we can live with it for now."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"\n",
"Created by [Tim Sherratt](https://timsherratt.org/) for the [GLAM Workbench](https://glam-workbench.github.io/)."
]
}
],
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