Big data-assisted urban governance: forecasting social events with a periodicity by employing different time series algorithms
| Date | 04 July 2023 |
| Pages | 1930-1955 |
| DOI | https://doi.org/10.1108/LHT-12-2022-0550 |
| Published date | 04 July 2023 |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
| Author | Zicheng Zhang,Xinyue Lin,Shaonan Shan,Zhaokai Yin |
Big data-assisted urban
governance: forecasting social
events with a periodicity by
employing different time
series algorithms
Zicheng Zhang and Xinyue Lin
School of Information Management, Nanjing University, Nanjing, China
Shaonan Shan
Shenyang University, Shenyang, China, and
Zhaokai Yin
School of Information Management, Nanjing University, Nanjing, China
Abstract
Purpose –This study aims to analyze government hotline text data and generating forecasts could enable the
effective detection of public demands and help government departments explore, mitigate and resolve social
problems.
Design/methodology/approach–In this study, social problems were determined and analyzed by using the
time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed
via the Prophet model. The Prophet model is decided after running a comparison study with other widely
applied time series models. The validation of modeling and forecast was conducted for social events such as
travel and educational services, human resources and public health.
Findings –The results show that the Prophet algorithm could generate relatively the best performance.
Besides, the four types of social events showed obvious trends with periodicitiesand holidays and have strong
interpretable results.
Originality/value –The research could help government departments pay attention to time dependency and
periodicity features of the hotline data and be aware of early warnings of social events following periodicity
and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems
and better promoting the role of the big data structure of government hotline data sets in urban governance
innovations.
Keywords Government hotline, Urban governance, Public event, Time series algorithm,
Periodic social problem
Paper type Research paper
1. Introduction
The government hotlines, also called the 12,345 government service convenience hotline, in
China (12,345 hotlines) refers to public service platforms set up by municipal governments
LHT
42,6
1930
Funding: This research did not receive any specific grants from funding agencies in the public,
commercial or not-for-profit sectors.
Submission declaration and verification: The authors declare that the material has not been published
in whole or in part elsewhere; this paper is not currently being considered for publication elsewhere and
all authors were actively involved in substantial work leading to the submitted version and will hold
themselves jointly and individually responsible for its content.
Compliance with ethical standards: This article does not contain any studies with human participants
or animals performed by any of the authors.
Conflicts of interest: The authors declare no conflicts of interest.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0737-8831.htm
Received 20 November 2022
Revised 10 May 2023
Accepted 30 May 2023
Library Hi Tech
Vol. 42 No. 6, 2024
pp. 1930-1955
© Emerald Publishing Limited
0737-8831
DOI 10.1108/LHT-12-2022-0550
to better engage with the public through the utilization of channels such as the phone number
12345, WeChat mini programs, microblogs, mayor’s mailbox, short messaging service,
government mobile clients, etc. The government hotline plays a vital role in safeguarding the
interests of legal persons, citizens and other social organizations, advancing law-based
administration and innovating social governance Luehrmann (2003),Zhang et al. (2019).
Rong and Xu (2017) researched Government Affair Microblogs (GAM) which are found to be
an ideal means for presenting online opinions, listening to public voices and constructing a
government image to succeed in enhancing the communication efficacy between the
government and the public. Huang et al. (2021) investigated the information-seeking behavior
of Chinese college students during the coronavirus disease 2019 (COVID-19) pandemic and
found that the information gathered from microblogs and government websites was found to
be a more reliable information source than the others. Hao et al., (2016) examined the
utilization of social media to strengthen e-government interactivity between the government
and the public by utilizing social media and found that social media could strengthen the
interactivity between the government and the general public through the structural features
and the content features of posts. Foster (2016) found that government organizations like
business and social media companies focus on considering the development of a data value
chain and identifying systematic processes that transform the data into information and
insights. Thus, they can be implemented to improve services. Al-Aufi et al. (2022) suggested
that utilizing social media for crowdsourcing also reinforces the moves by governments
toward boosting public involvement of citizens in activities such as planning, decision-
making and a better design of public services by collecting a broad range of expectations,
opinions, ideas, thoughts and suggestions through such platforms. Wu et al. (2022) suggested
that the efforts conducted by governments facilitate the interaction and understanding
between the government and the public greatly while enhancing the government’s capability
and efficacy to better govern and help generate early warnings for outbreaks and epidemics
based on government open data. Thus, the management of the health system would better
benefit. Chang et al. (2022) proposed that smart cities utilizing the available data collected
from citizens could help create smart organizations, cities, transportation and public services
that finally maximize the efficiency of services. Ma et al. (2020) found that the tasks conducted
by government service departments could be enhanced when public feedback is analyzed.
Thus, real-time supervision could be implemented by effectively collecting, storing and
analyzing public opinions on government services. Asogwa (2013) found that
implementations of information technology in government operations have been
increasingly referred to as means to establish an efficient delivery of services to citizens
and to better create an interaction between citizens and government departments, resulting in
better satisfaction on the side of users in Nigeria. Li and Hu (2022) studied the issues pertinent
to privacy, confidentiality and protection of the information provided by users to government
departments, business entities and enterprises and what types of measures should be
implemented were discussed.
A mass amountof data is produced every day in governmenthotline systems. According to
the Government Hotline Intelligent Development Report (2022) published by the Center on
Data and Governance of Tsinghua University, 32.86 million appeals from citizens had been
accepted by the 12,345 hotlines of Beijing from 2019 to the end of 2021 (Data Governance
Research Center of Tsinghua University, 2022). Government hotline data (GHD), which
accounts for a large portionof the whole hotline data, possess general properties of big data,
namely, they containa large quantity and multiple forms, are generatedrapidly and have low
value (Cotteret al., 2019). GHD mainly originates fromconsultations, help requests, complaints,
denouncements and opinions and suggestions concerning economic regulations, market
supervision,social management, public services and ecologicaland environmental protections
and issues. In addition, GHD deals withsocial events and problems in various fields.
Big data-
assisted urban
governance
1931
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