The Impact of Candidates’ Profile and Campaign Decisions in Electoral Results: A Data Analytics Approach
1. Introduction and Motivation
2. Data and Methods
2.1. Parliamentary Elections in Chile in 2017
2.2. Data and Variables
- Personal information: The gender variable is used, with the objective of detecting, on the one hand, the significant differences in access to resources by the candidates and, on the other hand, the effect of incorporating gender quotas (see [23,24], for details). Thus, if women have access to a lower campaign budget than men and this difference in resources explains the different electoral strategies, then it is likely that gender has an impact on electoral performance.
- Political orientation: Since 1990, the party system in Chile has been built on the basis of two large coalitions . Therefore, it is expected that the change to the electoral system-implemented by the center-left coalition-will favor the electivity of the most institutionalized parties. For this, the party or political conglomerate of the candidate is identified with the attribute coalition.
- Political experience: The incumbency attribute is introduced to represent whether the candidate has held the same position to which he or she is running in the period prior to the elections under study, an idea that is based on his systematic superiority in electoral outcomes . Additionally, the attribute of years in office is incorporated to study the impact of the time spent in the position for which he or she is running and an elected official by popular vote attribute to analyze the impact of candidates who have held positions of local representation (city councilors, mayors, governors, etc.) prior to the elections. The values of all the variables of this dimension were extracted from the Library of the National Congress of Chile .
- Percentage of communes visited, which corresponds to the fraction of communes in the district visited by the candidate.
- Percentage of individual visits, which corresponds to the fraction of visits that the candidate makes without other candidates of his or her coalition.
- Percentage of visits to new communes, which corresponds to the fraction of visits made by the candidate to new communes in his district.
- Percentage of new communes visited, which corresponds to the fraction of new communes in the district that are visited by the candidate.
2.3. Data Analytics Algorithms
2.4. Method Outline and Implementation
- Compile information on the candidates for deputies in the 2017 elections in Chile from their official Facebook accounts, the library of the National Congress, and the SERVEL website.
- Refine and pre-process data obtained from Facebook using text extraction, examination, and data visualization techniques. This step is carried out following the methodology proposed in :
Additionally, in this step, all the data are accumulated and sorted to generate a sample where each candidate is represented by a class and a set of attributes.
- Preprocessing the files by term extraction.
- Structuring and storage of the contents as intermediate representation, through lists of communes associated with the district to which each candidate runs.
- Application of analysis techniques on the intermediate representation through distribution analysis.
- Visualization of the results.
- Train the methods described in Section 2.3 with a subsample extracted from Step 2.
- Apply the classification methods trained in Step 3 to the candidates of the prediction sample (in the case of the variables associated with territorial deployment, for each candidate the mean values of these variables observed in the candidates from the training list are imputed).
- Compare the accuracy of the classification methods on the prediction sample. Select the method with the best accuracy value.
- Simulate different scenarios of territorial deployment by coalition, to analyze the impact of campaign strategies in the outcomes of the electoral process.
- Analyze the results obtained from two perspectives: first, from fitting the algorithms on the sample considered and, then, from the impact of the campaign strategies of the main coalitions in the outcomes of the electoral process.
3. Results and Discussion
4. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Territorial Deployment Simulations
Appendix B. Classification Tree
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|Profile of the candidate||1||gender||Categorical|
|3||years in office||Quantitative|
|4||elected official by popular vote||Categorical|
|Campaign effort||6||percentage of visits to new communes||Quantitative|
|7||percentage of new communes visited||Quantitative|
|8||percentage of communes visited||Quantitative|
|9||percentage of individual visits||Quantitative|
|10||percentage of budget used||Quantitative|
|Elected||Not elected||Total||Elected||Not elected||Total|
|Chile Vamos (ChV)||41||50||91||32||58||90|
|Coalición Regionalista Verde (CRV)||1||0||1||3||29||32|
|Convergencia Democrática (CD)||5||31||36||8||69||77|
|Frente Amplio (FA)||14||48||62||6||87||93|
|La Fuerza de la Mayoría (LFM)||19||41||60||24||90||114|
|Por Todo Chile (PTCh)||0||28||28||1||91||92|
|Partido de Trab. Rev. (PTR)||NA||NA||NA||0||4||4|
|U. Patriótica (UP)||NA||NA||NA||0||56||56|
|a Training sample.|
|b Prediction sample.|
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Campos-Valdés, C.; Álvarez-Miranda, E.; Morales Quiroga, M.; Pereira, J.; Liberona Durán, F. The Impact of Candidates’ Profile and Campaign Decisions in Electoral Results: A Data Analytics Approach. Mathematics 2021, 9, 902. https://doi.org/10.3390/math9080902
Campos-Valdés C, Álvarez-Miranda E, Morales Quiroga M, Pereira J, Liberona Durán F. The Impact of Candidates’ Profile and Campaign Decisions in Electoral Results: A Data Analytics Approach. Mathematics. 2021; 9(8):902. https://doi.org/10.3390/math9080902Chicago/Turabian Style
Campos-Valdés, Camilo, Eduardo Álvarez-Miranda, Mauricio Morales Quiroga, Jordi Pereira, and Félix Liberona Durán. 2021. "The Impact of Candidates’ Profile and Campaign Decisions in Electoral Results: A Data Analytics Approach" Mathematics 9, no. 8: 902. https://doi.org/10.3390/math9080902