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Data Collection

Overview

The data is politicians speaking to eachother about matters of concern of the Irish government.

https://www.oireachtas.ie/en/debates/

Dáil (House of Representatives)

  • 174 TDs, Lower house, elected by public, most power.

Seanad (Senate)

  • Upper house.
  • Can debate, amend but not stop Dáil legislation.
  • The 60 members are chosen by panels of industry specific politicians (43), the Taoiseach (11) and university graduates (6).

Committee

  • Task groups of TDs and senators focused on specific topics (e.g. healthcare).

PQs (Parliamentary Questions)

  • Questions submitted and directed to specific people for answers.
  • Oral/Written but majority are written.
  • Oral PQs overlap with Dáil debates
  • Can filter written using URL/API to only select written PQs to avoid Dáil debate duplication.

Timeline

Data availibility

Dáil

  • 1919 to Present

Seanad

  • 1929 to Present

Committee

  • 1924 to present

PQs

  • Jan 2025? to present

XML -> CSV (AI Generated Description)

This document describes how the Akoma Ntoso XML from the Oireachtas is transformed into the flat CSV debates_all.csv, and how to interpret each column—especially the text field which holds the raw debate content.

  1. XML → CSV Mapping

  • Top‐level <debate type="…">
    Each of the four debate types (Dáil, Seanad, Committee, Questions) becomes one set of rows.

  • Document‐level metadata
    Extracted from <preface> and <FRBRWork>:

    • doc_id/identification/FRBRWork/FRBRthis/@value
    • date<debate date="…">
    • title_ga, title_en<preface>/<block name="title_ga|en">/<docTitle>
    • proponent_ga,proponent_en<preface>/<block name="proponent_ga|en">/<docProponent>
    • status_ga, status_en<preface>/<block name="status_ga|en">/<docStatus>
    • document_date<preface>/<block name="date_en">/<docDate>@date
    • volume, number<preface>/<docNumber>@refersTo="#vol_…|#no_…"
  • Per‐element rows
    Inside each <debateBody>, elements are normalized to rows:

    • element_type = summary
      One row per <summary>: prelude notes, suspension, interruptions.
    • element_type = speech
      One row per paragraph (<p>) in a <speech>: includes speaker id/name/role and timestamp.
    • element_type = attendance (committees only)
      One row per committee sitting: the attendance column holds a semicolon‐joined list of all <person> names from the <rollCall>.
    • element_type = question (questions only)
      One row per <question>: with separate question and written_answer columns and a combined text field.
  1. CSV Columns Overview

Common metadata:
doc_id, source_type, date, title_ga, title_en,
proponent_ga, proponent_en, status_ga, status_en,
document_date, volume, number, committee_name,
question_type, question_number.

Structural context:
section_name, section_id, heading_text, heading_time.

Element descriptors:
element_type, element_id.

Speaker or person:
speaker_id, speaker_name, speaker_role, attendance.

Timing:
recorded_time.

Content:
- text ← Raw text of this row’s element.
- question ← The question text (only when element_type=question).
- written_answer ← The written answer text (only when element_type=question).

Topic (questions only)
The to="…” attribute from <question>.

  1. Understanding the text Field

The text column is your primary access to the debate content:

  • For summaries: shows the summary line exactly as in the XML.
  • For speeches: shows the full paragraph as spoken by a Deputy or Minister.
  • For attendance: repeats the roll‑call header (e.g. “Members present:”).
  • For question rows: concatenates the question and the written answer, so you can search Q&A in one go.

If you need to analyze just the spoken words, filter to element_type="speech" and read text.
For committee attendance, filter to element_type="attendance" and parse the semicolon‐separated attendance list.
For written questions/answers, filter to element_type="question" and use question vs. written_answer or the combined text.

  1. Example Queries

  • All speeches by a given Deputy:
    SELECT text
      FROM debates_all
     WHERE element_type = 'speech'
       AND speaker_name = 'Deputy Mary Lou McDonald';
    
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