Dr Luke Blaxill (Leverhulme Early Career Research Fellow, Anglia Ruskin University) - Negotiated Texts Network Seminar (29 June 2017) Historians are increasingly surrounded by an ever-growing forest of machine-readable textual sources. The old challenge of scarcity has been replaced by that of abundance. Despite this, the impact of text mining in History has been remarkably weak. Historians, who continue to be extremely interested in language, continue overwhelmingly to prize sharply focussed analyses based on close readings. Macroscopic computational approaches based on large (or even small) corpora remain at the fringes, despite the traditional barrier of cost and manpower being considerably mitigated by the march of technology. This paper explores the transformative potential of text mining in this field in two areas of political language where large corpora have become available. The first example is based on election platform speeches in the late Victorian and Edwardian era. In this age of emerging democracy, even local constituency candidates would routinely hold over a hundred public meetings in an election campaign, speaking at length to large audiences which were often reported very thoroughly by a diligent and wordy press. I argue that even very simple text mining techniques in a relatively small corpus (4 million words) can challenge historical consensus on the contents of general election campaigns, on the significance of issues such as imperialism and Irish Home Rule, and the respective visibility of party leaders such as Gladstone and Disraeli. The second is an analysis of the language of women MPs in Parliament since 1945. Drawing upon the outputs of the Digging into Linked Parliamentary Data ('Dilipad') project – which has added gender and party coding to the digital edition of Hansard – I present a wide-ranging empirical analysis of the role of gender in the 677 million words of Commons debates from 1945 to 2015. I investigate whether there is strong evidence to support the central feminist claim that women's contributions to Commons debates are substantively different to those of men, ask whether the 'gender effect' has been strengthening or weakening as the number of women in Parliament has increased since the 1997 election, and also at the effect of party, such as the oft-made claim that Labour (with its greater proportion of female MPs and ideological sympathy for feminism) was more focussed on representing women than the Conservatives. Overall, I argue these techniques, whether used conservatively in a supplementary capacity alongside traditional approaches, or more boldly to lead analysis, have considerable potential to reshape historians' work in the digital age. They allow us to analyse texts too large in size to read, help overcome flaws in human ability to intuitively estimate frequency, allow greater verifiability, more precise communication of quantity, and a more empirical approach to working.

1. Negotiated Texts Networks Seminar
A War of Words?
Text Mining Political Speeches in
Britain in the 19th and 20th Centuries
1
Dr. Luke Blaxill
Leverhulme Early Career Research Fellow
luke.blaxill@anglia.ac.uk

2. Data Deluge
• Billions of words of digitised next now
available to historians. We use these to access
and to search these archives. But the advent
of the ‘digital turn’ has not fundamentally
changed methods of analysis.

3. Political Historians, the Linguistic Turn, and
the Controversy of Text Mining…
• Great deal of recent interest in language in political history since the ‘linguistic
turn’
• The turn has given us a great deal, but its focus on deep readings of specific texts
in isolation has often caused historians, especially of language, to be gun-shy of
offering broader explanations.
• Many historians have called for a ‘reintegration’ of the explanatory ambition of the
old social-scientific historical tradition with the new sensitivity to language.
But how could such a reintegration occur?
Text Mining is a controversial answer in a field which prizes
‘close readings’, is fearful of classification and ontology, and
has an awkward relationship with computing after widespread
perceived failures in the 1960s and 1970s.

4. My Proposal: Quantitative language analysis
with a Corpus
A corpus: a huge bank of text of several million words
Candidates’ Speeches from Press SCANNED
Computerised Corpus:
Millions of words of
freely searchable text

5. My Corpora
East Anglian Speeches 1880-1910.
Subdivided per party, per election
(1 million words)
National Speeches 1880-1910.
Subdivided per party, per election
(4 million words)
Grassroots Speeches 1880-1910.
Outside East Anglia
(3 million words)
Hansard Corpus, 1800-present
Marked up by gender and party
(2.5 Billion words)

6. Corpus-driven Quantification in action:
Example One: Ireland, 1880-1900

7. Corpus-driven Quantification in action:
Example Two: Imperialism 1880-1900
How can we measure the ‘language of Imperialism’ with a corpus?
1. Historical Judgement
2. Topic Modelling
3. Seed Words

8. Corpus-driven Quantification in action:
Example Two: Imperialism 1880-1900
A five word Taxonomy of Imperialism:
• Imperial (and all variants)
• Empire
• Flag
• British (and all variants)
• Colony (and all variants)

9. Corpus-driven Quantification in action:
Example Two: Imperialism 1880-1900
To ensure the ascriptions of ‘the language of imperialism’
are correct, each word is checked in its original context:

10. Corpus-driven Quantification in action:
Example Two: Imperialism 1880-1900

12. ‘Gladstone’: Top Contexts
42% Foreign Policy Weakness
17% Inferiority to Disraeli
8% Good orator
16% Disunity
11% Disestablishment
7% General Gordon
7% Financial incompetence
57% Irish Home Rule
11% Liberal Unionists
7% Abandoning Land Reform
56% Irish Home Rule
14% Disunity
12% Newcastle Programme
11% General Greatness
General Greatness 20%
Financial competence 17%
Bringer of Peace 14%
Superiority to Disraeli 14%
Manifesto/Programme 21%
Party unity 21%
General Greatness 20%
Irish Home Rule 39%
General Greatness 30%
Newcastle Programme 12%
1880
1885
1892
Irish Home Rule 40%
General Greatness 29%
Party unity 17%
Superiority to Chamberlain 13%
1886

13. The Case for using Corpus-driven quantification
to study Political Language
Four Arguments
1. Difficult to measure quantity by intuition.
2. Corpora can help establish typicality better than
selected quotations from speech.
3. Numerical conclusions easy to verify
4. More empirical approach to working can illuminate
unexpected patterns otherwise hard to detect

14. Digging into Linked Parliamentary Data
(Dilipad) Project
0.7 Billion
Words
HANSARD
CORPUS
1945-present
Enhanced Corpora for UK, Canada, the
Netherlands since c.1800 for all three
counties.
Data Semantically enhanced…
• Coded By Gender
• Coded by Constituency
• Coded by Party
• Coded by whether MP was minister or
backbencher.

15. Women’s Parliamentary Language

16. Women’s Parliamentary Language

17. Women MPs: Keywords

19. Women’s Parliamentary Language

20. Topic %
Macroeconomics 12,30
Civil Rights, Immigration 3,26
Health 6,27
Agriculture 4,02
Labour and Employment 3,78
Education 4,93
Environment 1,76
Energy 3,25
Transportation 6,39
Law, Crime, and Family 6,36
Social Welfare 3,90
Planning and Housing 5,43
Banking, Finance 4,12
Defence 11,61
Space, Science, Technology 1,41
Foreign Trade 0,61
International Affairs 8,36
Government Operations 11,10
Colonial and Territorial Issues 1,14
Table 1: Percentage[L1] of speeches delivered in the Commons since 1945 by CAP topic codes

21. Variable Name Explanation
Gender (Female= 1) Dummy variable: the main explanatory variable of
interest.
Party (Labour=1) Dummy variable, with Conservative= 0, Labour= 1, and
Liberal= 2. We excluded the other parties because they
didn’t have a consistent presence in the House of
Commons since 1945.
Party Status (Government= 1) Dummy variable that indicates if the member sits on the
government or opposition benches.
Seniority Integer variable that counts the number of times a
member was elected.
Portfolio (Responsible=1) Dummy variable: set to 1 for members whose portfolio
applies to the specific policy area. It is not surprising, for
example, that the Health Secretary will talk more about
Health issues, and this variable allows us to control for
this.
Topic Topology Dummy variable that aligns with the topology introduced
in table ??. Soft issues are coded as 0; hard issues as 1;
and all others as 2.
Parliamentary Session Categorical Variable: Each parliamentary s
Total Speeches (Exposure) Integer variable that records the total number of speeches
across all topics
Table 3: Variables that are included in our model

22. Topic Gender(Female=1
)
Party(Lab)
Macroeconomics -4,941 *** -0,732
Civil Rights,
Immigration
7,712 *** 0,458
Health 6,703 *** 1,975 *
Agriculture 0,507 -4,153 ***
Labour and
Employment
-2,532 * 7,423 ***
Education 2,458 * 1,401
Environment 0,336 -3,381 **
Energy -4,559 *** 6,038 ***
Transportation -2,131 * -0,508
Law, Crime, and
Family
4,628 *** -0,306
Social Welfare 5,534 *** 2,422 *
Planning and Housing 0,371 3,089 **
Banking, Finance -2,631 ** -3,332 **
Defence -5,037 *** -2,466 *
Space, Science -3,419 ** 0,261
Foreign Trade -0,940 -0,357
International Affairs -1,646 -3,703 ***
Government
Operations
-4,352 *** 1,568
Commonwealth Issues -1,111 -2,079 *
Table 4: z-scores and significance for the gender and party variables. * > 0.05, ** > 0.01 *** > 0.001. Standard errors are clustered by MP.
We therefore code as
‘Male Topics’
Macroeconomics
Energy
Defence
Space Science
Govt Operations
We therefore code as
‘Female Topics’
Civil Rights + Immigration
Health
Law, Crime and Family
Social Welfare
Education

23. Figure 1: ratio of speeches on civil rights, health, education and social welfare. Red
line represents the ration for all MPs, black line for male MPs and grey line for
female MPs

25. z-score topic words HEATH
DEBATES
6,31 abort pregnanc women foetus termin contracept woman rape babi gynaecologist
2,87 cancer research transplant cell organ screen fluorid human donor blood
2,83 smoke tobacco alcohol cigarett advertis drink ban smoker pub product
0,98 vaccin osteopath immunis whoop acupunctur hairdress osteopat regi cough measl
-5,29 nhs bill trust mental amend nurs carer communiti doctor claus
Table 5: Z-scores for topics most related to gender. Topic model is trained on all health debates.

26. Figure 7: predicted number of speeches for male (left) and female MPs (rights). The blue line represents the ‘male’ topics, the
red line the ‘female’ topics. Dotted lines show the 95% confidence intervals.

27. Thank You.

28. • Note: The following few pages show extras tables and graphs, which aren’t part of the paper
but might be referred to in discussions.

29. Corpus-driven Quantification in action:
Gladstone, 1880-1892
EAST ANGLIA NATIONAL SPEAKERS

30. Education Issue in 1885: top contexts
Liberals mentions of
School, Child, Education (163
mentions) Score
Percentage of total
mentions
Poor people priced out of education 34 21%
General expressions of support in favour
of free education 34 21%
Will give dignity to poor/ will help poor 19 12%
Improve social mobility of poor 14 9%
Reassurance that religious aspect to
education will be kept 9 6%
Conservative mentions of school,
Child, Education (146 mentions) Score
Percentage of total
mentions
Weaken voluntary schools 26 18%
Expensive, wasteful 24 16%
Highlighting Liberal attack on religious
basis of education 19 13%
Stops people being stakeholders* 13 9%
Criticising compulsory and universal
education 16 11%
Poor standard of board Schools 5 3%

31. Church Disestablishment in 1885: top
contexts
Context of Liberal Mentions of Church (105
total) Score Percentage of total mentions
Proclamations in favour of Disestablishment 37 35%
Disestablishment as a route to Religious
Equality 17 16%
Candidate distancing themselves from
Disestablishment 10 10%
Attacks on Conservatives for politicising the
Church 9 9%
Context of Conservative Mentions of
Church (111) Score Percentage of total mentions
Attacks on Liberals for tying to weaken/
abolish Church 36 32%
General vows to protect Church 31 28%
Benefits of Church (Education) 5 5%
Benefits of Church (Church sponsored
charities) 11 10%
Benefits of Church (Classless, available for all
Classes) 10 9%
Benefits of Church (Education) 3 3%
Benefits of Church (Improvement of character) 3 3%

32. Mentions of Tariff Reform in East Anglia,
Jan 1910 compared to 1906: Liberals

33. Mentions of Tariff Reform in East Anglia,
Jan 1910 compared to 1906: Liberals + Conservatives