We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion
Motivated by the significant decline in citizen’s trust in governments over the past decades, this paper explores how policy decision makers and researchers can use social media analytics to investigate trust, specifically the relationship among tr...
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World Bank, Washington, DC
2015
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| Online Access: | http://documents.worldbank.org/curated/en/2015/09/25022358/feel-fine-big-data-observations-citizen-sentiment-state-institutions-social-inclusion http://hdl.handle.net/10986/22778 |
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okr-10986-227782021-04-23T14:04:10Z We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion Lemieux, Victoria PRESS TECHNOLOGY SOCIAL RELATIONS INFERENCE KNOWLEDGE COMMUNICATIONS ICT INSTITUTIONS PERCEPTION DATA STUDY HUMAN FACTORS INFORMATION SCIENCE BUSINESS SCIENCE TRAINING VISUALIZATION SOFTWARE PUBLIC ADMINISTRATION PSYCHOLOGY INTELLIGENCE PROCESSES DATA COLLECTION EXPERTS INFORMATION RESEARCHERS ANALYTICAL REASONING LEARNING CONCEPTS PROCESS DATA ANALYSIS CHARTS REASONING COGNITIONS MODELING ENTRY CLASSIFICATION MACHINE LEARNING SOCIAL SCIENCES MEDIA ADMINISTRATION Motivated by the significant decline in citizen’s trust in governments over the past decades, this paper explores how policy decision makers and researchers can use social media analytics to investigate trust, specifically the relationship among trust in government, trust in state institutions, and citizens’ collective behavior. Analysis of these complex socio-political issues using online social data requires a human in the inference loop while also benefiting from computational methods to handle large amounts of unstructured data and the inference of relevant data features. To highlight the power of a mixed-initiative visual analytics-data science approach, this technical note describes the exploratory analysis work undertaken for analysis of collections of Tweets from Brazil, and describes further work that conceives data science methods to assist the analysis process by supporting definition of constructs of concepts of interest using social media data, and assisting the evaluation of evidence for hypotheses evaluation in an interactive-machine learning fashion. The outcomes of this project aim to support social sciences inquiry using observational social media data and World Bank operations. 2015-10-19T20:36:07Z 2015-10-19T20:36:07Z 2015-06 Brief http://documents.worldbank.org/curated/en/2015/09/25022358/feel-fine-big-data-observations-citizen-sentiment-state-institutions-social-inclusion http://hdl.handle.net/10986/22778 English en_US Technical knowledge note; CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Brief |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English en_US |
| topic |
PRESS TECHNOLOGY SOCIAL RELATIONS INFERENCE KNOWLEDGE COMMUNICATIONS ICT INSTITUTIONS PERCEPTION DATA STUDY HUMAN FACTORS INFORMATION SCIENCE BUSINESS SCIENCE TRAINING VISUALIZATION SOFTWARE PUBLIC ADMINISTRATION PSYCHOLOGY INTELLIGENCE PROCESSES DATA COLLECTION EXPERTS INFORMATION RESEARCHERS ANALYTICAL REASONING LEARNING CONCEPTS PROCESS DATA ANALYSIS CHARTS REASONING COGNITIONS MODELING ENTRY CLASSIFICATION MACHINE LEARNING SOCIAL SCIENCES MEDIA ADMINISTRATION |
| spellingShingle |
PRESS TECHNOLOGY SOCIAL RELATIONS INFERENCE KNOWLEDGE COMMUNICATIONS ICT INSTITUTIONS PERCEPTION DATA STUDY HUMAN FACTORS INFORMATION SCIENCE BUSINESS SCIENCE TRAINING VISUALIZATION SOFTWARE PUBLIC ADMINISTRATION PSYCHOLOGY INTELLIGENCE PROCESSES DATA COLLECTION EXPERTS INFORMATION RESEARCHERS ANALYTICAL REASONING LEARNING CONCEPTS PROCESS DATA ANALYSIS CHARTS REASONING COGNITIONS MODELING ENTRY CLASSIFICATION MACHINE LEARNING SOCIAL SCIENCES MEDIA ADMINISTRATION Lemieux, Victoria We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| relation |
Technical knowledge note; |
| description |
Motivated by the significant decline in
citizen’s trust in governments over the past decades, this
paper explores how policy decision makers and researchers
can use social media analytics to investigate trust,
specifically the relationship among trust in government,
trust in state institutions, and citizens’ collective
behavior. Analysis of these complex socio-political issues
using online social data requires a human in the inference
loop while also benefiting from computational methods to
handle large amounts of unstructured data and the inference
of relevant data features. To highlight the power of a
mixed-initiative visual analytics-data science approach,
this technical note describes the exploratory analysis work
undertaken for analysis of collections of Tweets from
Brazil, and describes further work that conceives data
science methods to assist the analysis process by supporting
definition of constructs of concepts of interest using
social media data, and assisting the evaluation of evidence
for hypotheses evaluation in an interactive-machine learning
fashion. The outcomes of this project aim to support social
sciences inquiry using observational social media data and
World Bank operations. |
| format |
Brief |
| author |
Lemieux, Victoria |
| author_facet |
Lemieux, Victoria |
| author_sort |
Lemieux, Victoria |
| title |
We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| title_short |
We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| title_full |
We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| title_fullStr |
We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| title_full_unstemmed |
We Feel Fine : Big Data Observations of Citizen Sentiment about State Institutions and Social Inclusion |
| title_sort |
we feel fine : big data observations of citizen sentiment about state institutions and social inclusion |
| publisher |
World Bank, Washington, DC |
| publishDate |
2015 |
| url |
http://documents.worldbank.org/curated/en/2015/09/25022358/feel-fine-big-data-observations-citizen-sentiment-state-institutions-social-inclusion http://hdl.handle.net/10986/22778 |
| _version_ |
1764451985155686400 |