Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments

Evaluating service delivery needs in data-poor environments presents a particularly difficult problem for policymakers. The places where the need for social services are most acute are often the very same places where assessing policy interventions...

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Main Authors:	Braley, Alia, Fraiberger, Samuel P., Tas, Emcet O.
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2021
Subjects:	SOCIAL MEDIA TWITTER SERVICE DELIVERY NATURAL LANGUAGE PROCESSING SENTIMENT ANALYSIS ACCESS TO EDUCATION
Online Access:	http://documents.worldbank.org/curated/en/759721615384486443/Using-Twitter-to-Evaluate-the-Perception-of-Service-Delivery-in-Data-Poor-Environments http://hdl.handle.net/10986/35253

id	okr-10986-35253
recordtype	oai_dc
spelling	okr-10986-352532022-09-20T00:09:59Z Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments Braley, Alia Fraiberger, Samuel P. Tas, Emcet O. SOCIAL MEDIA TWITTER SERVICE DELIVERY NATURAL LANGUAGE PROCESSING SENTIMENT ANALYSIS ACCESS TO EDUCATION Evaluating service delivery needs in data-poor environments presents a particularly difficult problem for policymakers. The places where the need for social services are most acute are often the very same places where assessing policy interventions is the most challenging. This paper uses Twitter data to gain insights into service delivery needs in a data-poor environment. Specifically, it examines the development priorities of citizens in the north- western region of Pakistan between 2007 and 2020, using natural language processing techniques (NLP) and sentiment analysis of 9.5 million tweets generated by 20,000 unique Twitter users. The analysis reveals that service delivery priorities in this context are centered on access to education, healthcare, food, and clean water. The findings provide baseline data for future on-the-ground research and development initiatives. In addition, the methodology used in this paper demonstrates both current resources and areas in need of future work in the use of NLP techniques in analyzing social media data in other contexts. 2021-03-11T15:15:25Z 2021-03-11T15:15:25Z 2021-03 Working Paper http://documents.worldbank.org/curated/en/759721615384486443/Using-Twitter-to-Evaluate-the-Perception-of-Service-Delivery-in-Data-Poor-Environments http://hdl.handle.net/10986/35253 English Policy Research Working Paper;No. 9575 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper South Asia Pakistan
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	SOCIAL MEDIA TWITTER SERVICE DELIVERY NATURAL LANGUAGE PROCESSING SENTIMENT ANALYSIS ACCESS TO EDUCATION
spellingShingle	SOCIAL MEDIA TWITTER SERVICE DELIVERY NATURAL LANGUAGE PROCESSING SENTIMENT ANALYSIS ACCESS TO EDUCATION Braley, Alia Fraiberger, Samuel P. Tas, Emcet O. Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
geographic_facet	South Asia Pakistan
relation	Policy Research Working Paper;No. 9575
description	Evaluating service delivery needs in data-poor environments presents a particularly difficult problem for policymakers. The places where the need for social services are most acute are often the very same places where assessing policy interventions is the most challenging. This paper uses Twitter data to gain insights into service delivery needs in a data-poor environment. Specifically, it examines the development priorities of citizens in the north- western region of Pakistan between 2007 and 2020, using natural language processing techniques (NLP) and sentiment analysis of 9.5 million tweets generated by 20,000 unique Twitter users. The analysis reveals that service delivery priorities in this context are centered on access to education, healthcare, food, and clean water. The findings provide baseline data for future on-the-ground research and development initiatives. In addition, the methodology used in this paper demonstrates both current resources and areas in need of future work in the use of NLP techniques in analyzing social media data in other contexts.
format	Working Paper
author	Braley, Alia Fraiberger, Samuel P. Tas, Emcet O.
author_facet	Braley, Alia Fraiberger, Samuel P. Tas, Emcet O.
author_sort	Braley, Alia
title	Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
title_short	Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
title_full	Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
title_fullStr	Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
title_full_unstemmed	Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments
title_sort	using twitter to evaluate the perception of service delivery in data-poor environments
publisher	World Bank, Washington, DC
publishDate	2021
url	http://documents.worldbank.org/curated/en/759721615384486443/Using-Twitter-to-Evaluate-the-Perception-of-Service-Delivery-in-Data-Poor-Environments http://hdl.handle.net/10986/35253
_version_	1764482647392780288

Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor Environments

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