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...
| Main Authors: | , , |
|---|---|
| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
|
| Subjects: | |
| 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 |
| Summary: | 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. |
|---|