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Issue Competition Comparative Project (ICCP)

Version
1.0.0
Resource Type
Dataset
Creator
  • De Sio, Lorenzo (LUISS University Rome)
  • Emanuele, Vincenzo (LUISS University Rome)
  • Maggini, Nicola (University of Florence)
  • Paparo, Aldo (LUISS University Rome)
  • Angelucci, Davide (LUISS University Rome)
  • D´Alimonte, Roberto (LUISS University Rome)
Publication Date
2019-08-01
Contributor
  • Demetra Opinioni.net Srl, Venice, Italy (Data Collector)
Language
German
Classification
  • ZA:
    • Political Issues
    • Political Attitudes and Behavior
    • Political Parties, Organizations
  • CESSDA Topic Classification:
    • Mass political behaviour, attitudes / opinion
    • Government, political systems and organisation
    • Elections
Description
  • Abstract

    The Issue Competition Comparative Project (ICCP) is a comparative research project about party competition. The aim is to analyze party competition through an issue competition perspective, i.e. by conceptualizing political parties and leaders as rational, vote-maximizing political entrepreneurs that strategically exploit available issue opportunities in a context where voters are available across ideological boundaries. The first ICCP data collection round has covered six West European countries (Netherlands, France, United Kingdom, Germany, Austria, Italy) that held general elections in 2017 and 2018. The electoral campaign of political parties and party leaders was studied by monitoring, collecting, and analysing their activity on Twitter in the four month preceding the election date. For each party in the 6 ICCP countries, the monitoring activity was carried out on the public profile of the party and on the public profile of the main frontrunners/leaders of the party. 1. Survey Dataset Topics: interest in politics; vote intention; rating of current economic situation; expected economic situation in 12 months; propensity to vote different political parties; party identification; strength of party identification; party closeness to different parties; party that is credible for achieving different policy goals; respondent assigns a high, average or low priority to the policy goal; position to positional issues (self-placement on a 1-6 scale, with values 1-3 corresponding to one goal, and values 4-6 corresponding to the rival goal); shared policy goals (valence issues); left-right self-placement; approvement or disapprovement of the government’s record to date; candidate traits (knowledgeable about politics, strong, honest, and careful) were applicable. Demography: sex; age (year of birth, age class); church attendance; education; city size; profession; sector; self-assessment of social class; living standard. Additionally coded: respondent ID, weigthing factor. 2. Twitter Dataset Topics: study (country and year); abbreviation of party; Issue ID (within country); issue type (Positional or Valence); dimension (cultural or economic); Issue (short description); rival goal (on the issue) assigned to classical left-wing orientation; rival goal (on the issue) assigned to classical right-wing orientation; right-wing positional goal; systematic issue salience; absolute count of tweets the party dedicated to the issue; total number of issue-related party tweets; total number of party tweets dedicated to positional issues; total number of party tweets dedicated to valence issues; proportion of party tweets the party dedicated to the issue, over the total of issue-related tweets; orientation (left/right) of the goal with a higher issue yield for the party; party size in survey sample; whole sample and within party: goal support for positional issues; whole sample and within party: party credibility on goal; (credibility weighted) Issue Yield for goal; Issue Yield cross-party ranking.
Temporal Coverage
  • 2017-09-08 / 2017-10-11
    Austria
  • 2017-08-28 / 2017-09-12
    Germany
  • 2017-03-21 / 2017-04-11
    France
  • 2018-02-06 / 2018-02-12
    Italy
  • 2017-02-27 / 2017-03-10
    Netherlands
  • 2017-05-12 / 2017-05-31
    United Kingdom
Geographic Coverage
  • Austria (AT)
  • Germany (DE)
  • France (FR)
  • Italy (IT)
  • Netherlands (NL)
  • United Kingdom (GB)
Sampled Universe
Persons aged 18 and over (Austria: 16 and over) currently residing in the respective country and belonging to an opt-in web panel partner of Opinione.net (Demetra). Tweets on the public profile of the party and the main frontrunners/leaders of the party in the respective country during the last four months of the election campaign.
Sampling
Survey data were collected by a quota sampling procedure. Social media data comprise monitoring of the public profile of the party and on the public profile of the main frontrunners/leaders of the party during the last four months of the election campaign.
Availability
Download
A - Data and documents are released for academic research and teaching.
Rights
All metadata from GESIS DBK are available free of restriction under the Creative Commons CC0 1.0 Universal Public Domain Dedication. However, GESIS requests that you actively acknowledge and give attribution to all metadata sources, such as the data providers and any data aggregators, including GESIS. For further information see https://dbk.gesis.org/dbksearch/guidelines.asp
Alternative Identifiers
  • ZA7499 (Type: ZA-No.)
Publications
  • CISE - Italian Center for Electoral Studies: The year of challengers? Issues, public opinion, and elections in Western Europe in 2017 Edited by: De Sio, Lorenzo and Paparo, Aldo. https://cise.luiss.it/cise/2018/02/20/the-year-of-challengers-the-cise-e-book-on-issues-public-opinion-and-elections-in-2017/
  • De Sio, Lorenzo; De Angelis, Andrea; Emanuele, Vincenzo (2017): Issue yield and party strategy in multi-party competition. Comparative Political Studies, Available online https://doi.org/10.1177/0010414017730082

Update Metadata: 2019-09-16 | Issue Number: 5 | Registration Date: 2019-08-01

De Sio, Lorenzo; Emanuele, Vincenzo; Maggini, Nicola; Paparo, Aldo; Angelucci, Davide et. al. (2019): Issue Competition Comparative Project (ICCP). Version: 1.0.0. GESIS Datenarchiv. Dataset. https://doi.org/10.4232/1.13328