19 research outputs found
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data
Big spatio-temporal datasets, available through both open and administrative
data sources, offer significant potential for social science research. The
magnitude of the data allows for increased resolution and analysis at
individual level. While there are recent advances in forecasting techniques for
highly granular temporal data, little attention is given to segmenting the time
series and finding homogeneous patterns. In this paper, it is proposed to
estimate behavioral profiles of individuals' activities over time using
Gaussian Process-based models. In particular, the aim is to investigate how
individuals or groups may be clustered according to the model parameters. Such
a Bayesian non-parametric method is then tested by looking at the
predictability of the segments using a combination of models to fit different
parts of the temporal profiles. Model validity is then tested on a set of
holdout data. The dataset consists of half hourly energy consumption records
from smart meters from more than 100,000 households in the UK and covers the
period from 2015 to 2016. The methodological approach developed in the paper
may be easily applied to datasets of similar structure and granularity, for
example social media data, and may lead to improved accuracy in the prediction
of social dynamics and behavior
What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
There is surprisingly little known about agenda setting for international
development in the United Nations (UN) despite it having a significant
influence on the process and outcomes of development efforts. This paper
addresses this shortcoming using a novel approach that applies natural language
processing techniques to countries' annual statements in the UN General Debate.
Every year UN member states deliver statements during the General Debate on
their governments' perspective on major issues in world politics. These
speeches provide invaluable information on state preferences on a wide range of
issues, including international development, but have largely been overlooked
in the study of global politics. This paper identifies the main international
development topics that states raise in these speeches between 1970 and 2016,
and examine the country-specific drivers of international development rhetoric
Application of Natural Language Processing to Determine User Satisfaction in Public Services
Research on customer satisfaction has increased substantially in recent
years. However, the relative importance and relationships between different
determinants of satisfaction remains uncertain. Moreover, quantitative studies
to date tend to test for significance of pre-determined factors thought to have
an influence with no scalable means to identify other causes of user
satisfaction. The gaps in knowledge make it difficult to use available
knowledge on user preference for public service improvement. Meanwhile, digital
technology development has enabled new methods to collect user feedback, for
example through online forums where users can comment freely on their
experience. New tools are needed to analyze large volumes of such feedback. Use
of topic models is proposed as a feasible solution to aggregate open-ended user
opinions that can be easily deployed in the public sector. Generated insights
can contribute to a more inclusive decision-making process in public service
provision. This novel methodological approach is applied to a case of service
reviews of publicly-funded primary care practices in England. Findings from the
analysis of 145,000 reviews covering almost 7,700 primary care centers indicate
that the quality of interactions with staff and bureaucratic exigencies are the
key issues driving user satisfaction across England
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Availability, collection and access to quantitative data, as well as its
limitations, often make qualitative data the resource upon which development
programs heavily rely. Both traditional interview data and social media
analysis can provide rich contextual information and are essential for
research, appraisal, monitoring and evaluation. These data may be difficult to
process and analyze both systematically and at scale. This, in turn, limits the
ability of timely data driven decision-making which is essential in fast
evolving complex social systems. In this paper, we discuss the potential of
using natural language processing to systematize analysis of qualitative data,
and to inform quick decision-making in the development context. We illustrate
this with interview data generated in a format of micro-narratives for the UNDP
Fragments of Impact project
Big Data and AI – A transformational shift for government: So, what next for research?
Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed
Database of parliamentary speeches in Ireland, 1919-2013
We present a database of parliamentary debates that contains the complete
record of parliamentary speeches from D\'ail \'Eireann, the lower house and
principal chamber of the Irish parliament, from 1919 to 2013. In addition, the
database contains background information on all TDs (Teachta D\'ala, members of
parliament), such as their party affiliations, constituencies and office
positions. The current version of the database includes close to 4.5 million
speeches from 1,178 TDs. The speeches were downloaded from the official
parliament website and further processed and parsed with a Python script.
Background information on TDs was collected from the member database of the
parliament website. Data on cabinet positions (ministers and junior ministers)
was collected from the official website of the government. A record linkage
algorithm and human coders were used to match TDs and ministers.Comment: The database is made available on the Harvard Dataverse at
http://dx.doi.org/10.7910/DVN/6MZN7