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Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 9, p e0184562 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- This study shows how liking politicians' public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60-70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.
- Subjects :
- FOS: Computer and information sciences
Online and offline
Facebook
lcsh:Medicine
Social Sciences
02 engineering and technology
Intention
Elections
Choice Behavior
Mathematical and Statistical Techniques
Sociology
Voting
Surveys and Questionnaires
050602 political science & public administration
0202 electrical engineering, electronic engineering, information engineering
Profiling (information science)
Ethnicities
lcsh:Science
media_common
Multidisciplinary
Data Collection
05 social sciences
Politics
Social Communication
Computer Science - Social and Information Networks
Danes
Democracy
0506 political science
Social Networks
Physical Sciences
Computational sociology
Network Analysis
Statistics (Mathematics)
Research Article
Computer and Information Sciences
media_common.quotation_subject
Political Science
Research and Analysis Methods
020204 information systems
Humans
Social media
Statistical Methods
Social and Information Networks (cs.SI)
Behavior
Data collection
lcsh:R
Biology and Life Sciences
Data science
Communications
Computational Sociology
People and Places
Voting behavior
lcsh:Q
Population Groupings
Social Media
Mathematics
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....80a5ea1115baf4d2401ff11417087f64