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Using online negative emotions
to predict risk-coping behaviors in
the relocation of Beijing municipal
government
Qihui Xie1, Hongyu Wu2 & Ruwen Zhang1
This article explores the use of online negative emotions to predict public risk-coping behaviors
during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate
advanced prediction of public risk-coping behaviors based on online negative emotions. The study’s
empirical focus is on the relocation of the Beijing municipal government, using time series data for
Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from
Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing
Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while
risk-coping behaviors are measured through public information search behaviors and the incidence
of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated
that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors
in advance. (2) Negative comments are more eective predictors than negative feelings; (3) Negative
emotions about relocation policy formulation predict risk-coping behaviors better than those related
to policy eectiveness and implementation; (4) Negative emotions from individuals better predict
public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders
better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is
recommended that relevant departments establish a real-time monitoring system to track negative
public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate
communication, and promote transparency and educational campaigns to address the challenges of
urban relocation. In future studies, methods such as expanding the sample size and adding indicators
will be used to address the limitations of potential bias in sample data.
Keywords Negative emotions online, Information search behavior, Violent behavior, Granger causality
analysis, Relocation of Beijing municipal government
In the era of big data, the advent of online public opinions and the sentiments expressed by netizens have
heightened the complexity of the urban relocation policy process. e surge in negative emotions observed
online poses a challenge, potentially leading to oine public risk-coping behaviors. However, amid these
challenges, it is crucial to recognize the positive impact of online public opinions and emotions. ese digital
expressions can serve as a mirror reecting the genuine attitudes and coping mechanisms of the public during
the urban relocation process. Moreover, they oer a unique opportunity to anticipate the occurrence of mass
incidents. is study aims to validate the hypothesis that negative emotions online can predict the public’s risk-
coping behaviors in the relocation process. If conrmed, it implies that real-time data on negative emotions
can be monitored during urban relocation policy implementation, enabling timely and eective prediction of
risk-coping behaviors. is approach serves as a supplementary method towards conventional means of policy
risk assessment.
e existing research substantiates the signicant role of negative emotions in shaping public risk-coping
behaviors1–3. Moreover, it establishes that negative emotions possess predictive capabilities regarding the public’s
risk-coping behaviors4. However, the current body of research seldom addresses two critical questions: rst,
whether negative emotions expressed online can predict the public’s risk-coping behaviors in the context of
1Department of Public Administration, School of Law and Humanities, China University of Mining and Technology
(Beijing), Beijing 100083, China. 2School of Art and Design, Beijing Forestry University, Beijing 100083, China.
email: xieqihui@cumtb.edu.cn
OPEN
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urban relocation policy; second, exploring factors that inuence the accuracy of this prediction. is study aims
to address these two research questions.
is paper posits hypotheses suggesting that negative emotions can predict the public’s risk-coping behaviors
during the Beijing Municipal Government relocation. e accuracy of prediction is proposed to be inuenced by
types, issues, participants, and stakeholders, as derived from a comprehensive literature review. ese hypotheses
are then tested using the Granger Causality Analysis of time series data. e time series data on online negative
emotions are sourced from Sina Weibo, while risk-coping behaviors are quantied through public information
search behaviors and the incidence of violent events.
Literature review and research hypotheses
The prediction of risk-coping behaviors by negative emotions
Risk-coping behaviors in urban relocation
e displacement and resettlement resulting from urban relocation5,6have garnered increasing attention from
scholars7. is phenomenon, deemed essential for enhancing sustainable urban development, concurrently
introduces social risks to urban management8. e success of relocation plans hinges on the improved quality of
life and economic well-being for the reloc ated public, along with the full lment of reas onable demands9. Successful
implementation relies on cooperative eorts among all actors, stakeholders, and the public, fostering mutual
understanding based on shared goals and visions10. is collaborative approach actively promotes the relocation
process, attains expected policy outcomes, and contributes to urban development objectives11. However, if the
living environment deteriorates post-relocation, resulting in short to medium-term livelihood stagnation and
poverty, the public may engage in various risk-coping behaviors, impacting urban sustainable development and
social stability. In situations of perceived risk, individuals undertake risk and response assessments, deciding
on actions they deem appropriate12,13. Dened as conscious, purposeful, and exible changes in emotions,
cognition, behavior, and environment in response to stress and risk, risk-coping behaviors are conscious eorts
to reduce the perceived risk of a threatening event in a given situation14,15. Although integral to individual
stress management, risk-coping behaviors also hold signicant value for eective social risk management. In
this study’s research scenario, the coping behaviors adopted by the public to safeguard their interests introduce
uncertain risks in the urban relocation process. Public risk-coping behaviors, as the focus of this paper, arise
from dissatisfaction with public demands not being met. In the context of public risk response behaviors during
urban relocation, representative actions include information-seeking behaviors and violent criminal behaviors.
Information-seeking behaviors refer to actions taken by the public to understand relevant information, policies,
and impacts of the relocation, which helps them better cope with the uncertainties and risks brought about by
the relocation. On the other hand, violent criminal behaviors, which arise in some cases due to social unrest,
dissatisfaction, or conicts triggered by the relocation, represent extreme coping mechanisms adopted by some
members of the public. Such behaviors are detrimental to both social stability and the smooth progress of the
relocation.
Lindell and Perry16identied information searching behavior as a manifestation of risk-coping behavior. In
such contexts, individuals tend to engage in information search when perceiving a genuine threat and sensing an
unacceptable level of personal risk. When presented with highly credible sources, the public may unquestionably
comply with instructions, while ambiguity may prompt increased eorts in seeking and processing information17.
In the Internet era, given the uncertainty surrounding relocation information and the perceived threat associated
with it, the public frequently turns to online platforms to acquire relevant policy information—specically,
searching for relocation policies. Hu et al18.corroborated the correlation between the public’s information search
behavior and their risk perception through a questionnaire survey. In essence, heightened information search
activity may signal increased risk perception, correlating with a greater likelihood of resistance against the
relocation.
Violent conict behavior represents another coping strategy that may introduce social risks during the
relocation process. Dissatisfaction with relocation compensation and resettlement may lead to violent protests
against the relocation, or large-scale conicts between the involved parties. Some scholars argue that certain
forms of relocation inherently involve violence. For instance, Ortega’s19research suggests that Manila, in
its pursuit of becoming a modern and investment-friendly city, forcefully relocates informal settlers to the
outskirts—an inherently violent form of suburbanization. Consequently, the public may resort to corresponding
violent actions in response. Begega and Kohler20discussed protest behavior and the mobilization process in
enterprise production relocation through case studies. Con et al21. examined relocation delays stemming from
conict behaviors in utility relocation processes. Furthermore, a decrease in poverty and quality of life resulting
from relocation may contribute to an increase in violent crimes in the relocated area. Casas et al22. argue that
areas of concentrated poverty and public housing face persistent issues with high rates of violent crime, while
relocated residents may not necessarily experience less violence or improved safety in their new communities.
Popkin et al23. contend that while most relocations reduce violent crimes, the relocation of medium and high-
density communities may negatively impact domestic violence crimes.
Negative emotions online and risk-coping behaviors
Research on emotions originated in psychology, attributing emotions to physiological mechanisms such as the
impact of the prefrontal cortex on the amygdala. Sociologists have also extensively studied emotions, recognizing
their integration with social network relations, people, places, time, space, and events, giving rise to the concept
of social emotions. It is believed that the formation of social emotions relies on individuals’ perception of society.
Once formed, social sentiments inuence social behavior, with social emotions driving scal, macroeconomic,
and political behaviors, according to Prechter24.
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e internet’s global reach, breaking time and space constraints, signicantly enhances individual social
cognition levels, contributing to the formation of social emotions. On social media platforms, Internet users’
emotions, oen magnied innitely, shape and adjust netizens’ emotions and impact public social behavior25,26.
It is posited that internet users’ emotions inuence risk-coping behaviors, challenging the notion that social
behaviors drive emotions27.
When focusing on negative emotions, studies suggest their impact on risk-coping behaviors. For instance,
Ramli et al1. found that controlling negative emotions during group gatherings reduces aggressive and dangerous
behaviors. Lei and Yang2proposed that stronger negative emotions correlate with higher likelihoods of choosing
high-risk coping behaviors. Hu et al3. quantitatively established a positive correlation between risk perception,
negative emotions, and rumor-spreading behavior, with negative emotions playing mediating roles.
It has been shown that negative emotions have a certain correlation with public risk coping behaviours, and
that negative emotions increase the emergence of risk coping behaviours to a certain extent and increase the
number of violent incidents in society. e research object chosen for this study is online negative emotions,
and the collection of negative emotions is limited to the scope of the Internet, mainly because there are certain
dierences between online negative emotions and negative emotions. From the point of view of the scope of
dissemination, online negative emotions can be quickly spread to a wider audience through the Internet and
can cross geographical boundaries, which makes their inuence wider. On the other hand, ordinary negative
emotions are usually conned to face-to-face communication, which has a relatively small scope of inuence.
In terms of communication persistence, once posted, online negative sentiment may be permanently recorded
unless it is deleted or hidden. is makes negative content highly persistent. In contrast, negative emotions
tend to be temporary in real life and may fade over time. From the perspective of speaking anonymity, online
negative emotions can sometimes be expressed anonymously or under a pseudonym, which may prompt some
people who are afraid to speak out in real life to choose to speak out on the Internet and express their true
emotions. Finally, from the perspective of information interactivity, online negative emotions can trigger chain
reactions, such as follow-on comments and retweets by netizens, creating collective negative emotions. Whereas
oine negative emotions may also resonate with others, but the spreading speed and scope of such resonance
is relatively limited. Online negative emotions are to some extent more authentic and widespread than oine
negative emotions. With the popularity of the Internet, many researchers choose to capture online comments
and data for data analysis28,29. erefore, this paper chooses to study the impact of online negative emotions on
public risk response behavior.
Despite evidence of negative emotions driving risk-coping behaviors, there is limited research exploring
whether online negative emotions can predict public risk-coping behaviors in urban relocation processes.
Building on prior studies, this paper hypothesizes that negative emotions online can eectively predict public
risk-coping behaviors during urban relocation.
H1: Negative emotions online can predict public risk-coping behaviors in the urban relocation process.
Factors inuencing the prediction eect
According to the existing research, there is a certain correlation between negative emotions and risk coping30–32.
is study further explores the dierences in the predictive eects of various dimensions of negative emotions on
risk coping behaviors. On one hand, we delve into the generation of the online text content containing negative
emotions. e types of negative emotions are categorized based on the internal formation mechanisms of those
online contents, while emotions related to dierent issues are classied according to the external problems that
evoke these contents. On the other hand, we focus on the distinctions among the subjects that express online
negative emotions. e negative emotions from dierent participants are categorized based on the organizational
attributes of those subjects, whereas the negative emotions from dierent stakeholders are classied according to
the interest attributes of those subjects. Based on the formation of contents containing negative emotions and the
dierences among subjects expressing negative emotions, this study identied four dimensions of the negative
emotions, including the dierent types of negative emotions, the negative emotions about dierent issues, the
negative emotions from dierent participants, and the negative emotions from dierent stakeholders.
Dierent types of negative emotions
Initially, emotional analysis methods were predominantly employed by computer and information science
scholars, utilizing coarse-grained analysis focused solely on positive and negative attitudes. Watson and
Tellegen33categorized self-reported emotions into positive and negative emotions, introducing the PANAS
scale for emotional measurement. Subsequent research included intensity analysis based on this coarse-grained
dichotomy. Russell et al34. developed a circular emotion model, classifying emotions according to pleasure
and intensity, resulting in four categories: high-intensity pleasure, medium-intensity pleasure, high-intensity
displeasure, and medium-intensity displeasure. As emotional analysis technology matured, scholars transitioned
from a simple dichotomy to a more systematic, ne-grained analysis of public emotional information. Ekman35,
for instance, divided emotions into six basic states: joy, sorrow, anger, fear, surprise, and disgust.
Chinese text analysis also identies various negative emotions. One approach is ne-grained, as seen in
Liu’s 36classication of negative emotions, including anxiety, sadness, anger, disgust, and fear. Another approach,
based on coarse-grained dichotomy, involves dierentiating negative emotions by degree. For instance, the
HowNet Dictionary separates negative feeling words from negative comment words37,38. In comparison to
ne-grained analysis, HowNet’s classication accentuates distinctions within negative emotions. is study
employs HowNet’s classication to analyze the inuence of dierent negative emotions on prediction eects.
Negative feelings refer to the expression of one’s subjective feelings, such as sadness, anger, or disappointment,
while negative comments refer to criticisms, accusations, or dissatisfaction expressed towards a certain event or
phenomenon based on one’s personal values. Negative emotions belong to the realm of individual psychology,
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emphasizing the expression of personal feelings. ese emotions may be private and subjective, and do not
necessarily directly reect or inuence external social reality. erefore, although the negative feelings expressed
by individuals online may be genuine and intense, they may not necessarily immediately or signicantly change
the opinions or behaviors of the general public39. Negative comments fall under the scope of social psychology,
emphasizing the expression of societal perspectives. Rational and well-grounded negative comments tend to
be more easily accepted by the public, as they may be based on facts, logic, or shared values, rather than mere
emotional venting40. Xie and Peng41 also conrmed that negative comments signicantly impact risk perception,
while negative feelings show no signicant impact. Given the signicant relationship between risk perception
and risk-coping behaviors, research hypothesis 2 is as follows:
H2: Negative comments online can more eectively predict public risk-coping behaviors than negative
feelings online in the urban relocation process.
Negative emotions on dierent issues
Issue setting is a form of agenda setting42, wherein the media accentuates specic matters to inuence the public’s
attitudes or understanding. It is widely acknowledged that framing issues signicantly impact public awareness
and behavior43,44. Similarly, government policy making and synergies can inuence and guide public behavior45.
erefore, the paper posits that negative emotions related to dierent issues will dier signicantly in predicting
risk-coping behaviors. Grounded in policy process theory, public policy undergoes stages such as formulation,
implementation, evaluation, and termination46. us, issues within a public policy can be classied into three
types: policy formulation, policy implementation, and policy eectiveness. e policy formulation process is
more likely to draw public attention and discussion than other stages47. e outcome of the policy formulation
will aect the policy implementation and the policy eectiveness48.us, the third hypothesis of this paper is
presented as follows:
H3: Negative emotions related to the issue of relocation policy formulation can more eectively predict
public risk-coping behaviors than those related to issues of relocation policy eectiveness and relocation policy
implementation.
Negative emotions from dierent participants
Online emotions serve as a reection of the public’s mindset and behaviors. Numerous studies have conrmed
that personal characteristics signicantly inuence behavioral judgments and public risk-coping behaviors49–51.
erefore, variations in the subjects of emotions may result in dierent risk-coping behaviors. Public opinion
participants can be classied in various ways, such as categorizing them as opinion leaders, active users, or
inactive users based on the enthusiasm of their online behaviors52, or as government, media, and the public
based on their roles in online posting53. Other researchers divided public opinion participants into individuals,
groups and communities based on the perspectives of dierent groups54.
In contrast to negative emotions expressed by individual citizens, emotions emanating from government
and media are subject to “gatekeepers” and may not directly mirror public opinion55. us, this paper proposes
that emotions expressed by individuals are more eective in predicting risk-coping behaviors compared to those
from government or media, leading to the formulation of the fourth hypothesis.
H4: Negative emotions expressed by individuals are more eective in predicting public risk-coping behaviors
than those expressed by institutions in the urban relocation process.
Negative emotions from dierent stakeholders
Stakeholders, initially an economic concept, refer to individuals or entities with claims to a company’s cash
ow. In the realm of social science or management research, stakeholders encompass any entity aected by
organizational decisions and actions in the external environment56. As per stakeholder theory, numerous risk
factors inuencing social stability oen escalate into signicant social conicts not solely due to the actual
magnitude of the risk, which can be challenging to control and resolve. Instead, these conicts are largely rooted
in varying risk perceptions among dierent stakeholders57.
From a relevance perspective, stakeholders can be categorized into three groups: key stakeholders, non-key
stakeholders, and marginal stakeholders58,59. Key stakeholders are essential resources that institutions rely on for
survival and development; their support crucial for the institution’s continued existence and growth60. While the
existence and development of institutions are intertwined with the support of non-key stakeholders, the degree
of dependence on them for enterprise survival and development is notably lower than that of key stakeholders.
Marginal stakeholders, on the other hand, are not indispensable for the institution’s existence and development.
Numerous studies have explored the dierences in risk perception among various stakeholders61. e majority
of research ndings indicate that the risk perception of key stakeholders surpasses that of other stakeholders62,63.
As a result, risk-coping behaviors originating from key stakeholders are anticipated to be higher than those from
other stakeholders. erefore, this paper proposes Hypothesis 5.
H5: Negative emotions expressed by key stakeholders are more eective in predicting public risk-coping
behaviors than those expressed by non-key stakeholders or marginal stakeholders in the urban relocation
process.
Materials and methods
Case study
Beijing’s population had already exceeded 20million in 2012, as shown in Fig.1. If not controlled, overpopulation
will lead to a series of megacity-related issues, such as trac congestion, a decline in service levels, resource
shortages, and the destruction of the ecological environment. In this context, both the central government
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and the Beijing government are contemplating the possibility of seeking a breakthrough. e relocation policy
implemented by the Beijing Municipal Government is a public policy aimed at evacuating the population.
Figure2 summarizes the policy process of the Beijing Government Relocation. Since the beginning of 2015,
rumors about the relocation by the Beijing Municipal Government had been circulating among the public.
Many people initially reacted with disbelief, but those who were well-informed began to purchase houses in the
Tongzhou District of Beijing. By June 9 2015, Beijing’s stance on the implementation of the Beijing-Tianjin-Hebei
Cooperative Development Plan was adopted, conrming the government’s policy of relocating to Tongzhou. On
November 25, 2015, during the Eighth Plenary Session of the Eleventh Beijing Municipal Committee of the
Communist Party of China, it was announced that all municipal administrative institutions in Beijing would
collectively or partially move to the Tongzhou Administrative Vice-Center in 2017, with the relocation schedule
being determined for the rst time.
Since December 2015, the negative eects of the policy have surfaced one aer another. e most immediate
consequence of the policy is the surge in housing prices in Tongzhou and its neighboring areas. e spike in house
prices has led to sudden wealth for some individuals, while others have experienced a loss of income, resulting
in social injustice and exacerbating social conicts. For instance, on November 8–9, 2015, four consecutive
intentional injury cases related to demolition and relocation took place in Xinzhuang Village, Yanjiao Town,
Sanhe City, Hebei Province64.
Although fraught with numerous risks, the relocation of the Beijing Municipal Government proceeded.
By the end of December 2017, only a few departments had adhered to the relocation schedule outlined in
November 2015, and the initial batch of ministries and commissions commenced the move to Tongzhou. On
January 11, 2019, four groups comprising the Beijing Municipal Committee of the Communist Party of China,
the Beijing Municipal People’s Government, the Beijing People’s Congress, and the Beijing Political Consultative
Conference collectively relocated to Tongzhou, ocially implementing the policy.
Spanning nearly four years from its initial announcement to implementation, the policy had been widely
discussed on the Internet since the emergence of rumors. Simultaneously, risk-related issues such as rising
housing prices, education, and healthcare garnered public interest, with the repercussions of policy risks still
ongoing. is paper selects this policy as a representative case of relocation policies to explore solutions for
various challenges arising from China’s rapid economic development and investigate the relationship between
online negative emotions and public risk perception. Based on the case analysis, the data for analysis was
obtained from June 9, 2015 to April 28, 2019.
e reason that we choose the period of data collection is: rumors about the relocation by the Beijing
Municipal Government had been circulating among the public in June 9 and the Beijing Municipal Government
Fig. 2. Policy progress.
Fig. 1. Population Change in Beijing (Ten thousands).(Source: China National Bureau of Statistics).
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relocation policy was ocially introduced in July 2015, while the Beijing Municipal Government ocially
relocated into Tongzhou in January 2019 and the Beijing Municipal Government have settled down in Tongzhou
in April 28. is study is concerned with whether there is any public opposition to this policy and negative
emotions in cyberspace during this four-year period, which ultimately led to the policy not being implemented,
so it is considered that the ocial relocation in 2019 means that the policy has already been implemented and
has produced eects. is paper will investigate the public’s negative emotions on the internet during these four
years.
Variables of negative emotions
Acquisition of analytical data
Presently, Sina Weibo remains as the most popular and inuential microblogging platform in China; thus, it
was chosen as the sample source. Using keywords such as “Beijing government relocation,” “Beijing relocation
to Tongzhou,” “Beijing government eastward relocation,” and “Tongzhou New Town,” searches were conducted
on Sina Weibo from June 9, 2015 to April 28, 2019. Over 20,000 public posts about this policy were collected.
rough the emotional judgment system developed by Boryou Technology (https://www.boryou.com/, accessed
on August 12, 2022), 3,133 posts on Sina Weibo related to negative emotions were identied. Aer manual data
cleaning, which involved removing information unrelated to the policy, eliminating duplicates, and excluding
non-negative emotions, 1420 posts related to the relocation policy of the Beijing Municipal Government were
retained. e time series data for the number of posts containing negative emotions were organized to align
with the data on public risk-coping behaviors. e data used in this study were all open data and were obtained
from the Internet. e data on online negative emotions and risk-coping behaviors are shown in the Figs.3 and
4, containing a total of 1420 data between June 9,2015 and April 28,2019.To make the data presentation more
scientic and aesthetically pleasing, this study has undergone data normalization.
Ethical approval and consent to participate
All procedures performed in studies involving human participants followed the ethical standards of the
institutional and national research committee along with the 1964 Helsinki declaration and its later amendments
or comparable ethical standards. e study protocol was approved by the Ethics Committee of the school of Law
and Humanities, China University of Mining and Technology (Beijing). All participants provided voluntary
informed consent upon survey completion. Furthermore, our data underwent anonymization procedures to
ensure participant privacy.
Manual coding of variables
Table 1shows the list of variable codes. To ensure the validity of the coding, two coders with backgrounds
in linguistics and public administration were selected and trained to perform the coding independently.
If the repetition rate between the two coders exceeds 90%, the condence level of the results is considered
acceptable65,66. In this study, the coding of the data was examined through the Holsti reliability test, and the
overall reliability coecients of the two coders were calculated to be between 0.90 and 1.0, which shows that the
results of the reliability between the 2 coders are acceptable.
Measurement of public risk-coping behaviors
Based on the literature review, representative risk-coping behaviors in the relocation process include information-
seeking behaviors and violent criminal behaviors. is paper assesses public risk-coping behaviors in the urban
relocation process through an examination of these two dimensions.
Fig. 3. Online Negative Emotions data.
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Information searching behaviors
is paper employed the Baidu Index to gauge public information searching behaviors. e time series data
capturing the public’s search volume related to the “Beijing government relocation” policy were obtained through
the Baidu search index (http://index.baidu.com, accessed on June 1, 2022).
Violent crime behaviors
In previous studies, violent crimes have typically been derived from ocial statistics. e violent crimes
investigated in this paper require time ser ies statistical analysis in conjunction with online public opinion. Online
data can be captured at specic intervals, even down to the minute or second, while oine ocial statistics are
typically reported annually or quarterly, posing challenges for correlation studies. erefore, this paper utilizes
big data mining to acquire daily data on violent crimes. Davenport et al67. employed news reports to study public
behaviors. Traditional newspaper media, unlike new media, tends to track crisis events, oering insights into
the essence of these events. Hence, this paper initially identies violent crime behaviors in the relocation area
using the “newspaper database” from the China National Knowledge Infrastructure (CNKI). Keywords such as
“Beijing Tongzhou,” “violence incident,” “mass incident,” “petition,” and “crime” were used in the search, and the
data on behaviors were manually assessed and ltered.
Moreover, ocial microblog accounts serve as extensions of government functions, and each piece of
information released through them can be considered credible. us, government microblogs are selected
as sources of data on violent crime behaviors. Given that the “Beijing Municipal Government Relocation”
policy primarily impacted Beijing and Hebei, the ocial microblog accounts of the Beijing Municipal Public
Security Department, “@ Ping An Beijing,” and the Hebei Provincial Public Security Department, “@ Hebei
Factors Variables Manual coding
Types A1:Negative comments If the content of the text is expressions of dierent views about the public policy, and the opposing attitude is more moderate, then
code 1. If not, code 0
A2:Negative feelings If the content of the text is expressions of strong dissatisfaction and strong opposition, then code 1. If not, code 0
Issues
B1:Policy formulation If the content of the text is to express opinions of policy making (such as agenda setting, policy plan, etc.), then code 1. If not, code 0
B2:Policy implementation If the content of the text is to express opinions of policy implementation (e.g. group incidents, rumors, etc.), then code 1. If not,
code 0
B3:Policy eectiveness If the content of the text is to express opinions of the policy eect (for example the policy caused the rise of housing prices, the
policy caused the problems of education, medical care, etc.), then code 1. If not, code 0
Participants C1: Institutions If the publisher is media or government, the code is 1, if not 0
C2: Individuals If the publisher is an ordinary person, the code is 1, if not 0
Stakeholders
D1: Key Stakeholder If the negative information is published in Beijing, the code is 1. If not, the code is 0
D2:Non-Key stakeholder If the negative information is published in Tianjin or Hebei, the code is 1. If not, the code is 0
D3:Marginal stakeholders If the negative information is not published in Beijing, Tianjin or Hebei, the code is 1. If not, the code is 0
Tab le 1. Variables coding list.
Fig. 4. Risk-coping Behaviors data(information searching and violent crime behaviors).
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Public Security Network Spokesman,” were chosen to gather crime data. Keywords such as “violent events,”
“mass events,” “petitions,” and “crimes” were used in the search, and manual judgment was applied to determine
whether the behavior constituted a violent crime. By combining newspaper data and government microblog
data, a time series dataset of violent crime behaviors was compiled.
Granger causality analysis
Granger causality analysis, pioneered by Clive W. J. Granger, the 2003 Nobel Prize winner in economics, has been
widely employed by scholars to examine the predictive relationship between variables68,69. is test was initially
developed to analyze Granger causality in economic variables. Granger dened Granger causality as dependence
on the variance of the best least squares prediction using all the information at some point in the past.
e original hypothesis of the Granger causality test is as follows:
Hypothesis 0 Variable x cannot Granger cause variable y;
Hypothesis 1 Variable x can Granger cause variable y.
e calculation formula is presented in Formula 1. If the p-value is less than 0.05, it indicates that H1 is
established; if the p-value is greater than 0.05, it means H0 is established.
Formula 1. Granger causality analysis.
is paper employs the Granger causality analysis method to examine the predictive impact of online
negative emotions on public risk perception, through three steps: unit root test, VAR model construction, and
establishment of the optimal lag date.
Results
e analysis was conducted using EViews 10.0 soware. Data cleaning by using the Excel soware. e negative
emotions data section includes the variables A1-D3 in Table1, and the data in the Public Risk-coping behaviors
includes two parts, respectively, the public information searching behaviors and the number of violent incidents.
e data of public information searching behaviors are derived from Baidu search index, the data of violent
events is the sum of dierent violence reports in microblog news, newspaper news and website news in the same
day.
Verication of the prediction of public risk perception by negative emotions
Unit root test
Only stationary series can be used for the Granger test. e Unit Root Test was employed to assess whether the
data represent stationary series. e time series data for negative emotions and risk perception underwent the
Augmented Dickey-Fuller (ADF) test in EViews. Table2 shows that at a 1% signicance level, the variables of
negative emotions, information searching behaviors (ISB), and violent crime behaviors (VCB) are stationary and
meet the conditions for modeling. Consequently, an unconstrained VAR model was established.
VAR model construction
e VAR model was estimated using the AR root estimation method to test the stationarity of the results. AR
root estimation is based on the principle that if the reciprocal of all root modules of the VAR model is less than 1,
indicating that all root modules are within the unit circle, then the model is stable. Conversely, if the reciprocal
of all root modules of the VAR model is greater than 1, meaning that all root modules are outside the unit
circle, then the model is unstable. is paper constructed two VAR models: negative emotion and information
search behavior (Model 1), negative emotion and violent criminal behavior (Model 2). Figures5 and 6 display
t-Statistic Prob.*
Test critic al values
1% level 5% level 10% level
Negative emotions –24.92146 0.0000 –3.964525 –3.412980 –3.128488
ISB –8.230661 0.0000 –3.964534 –3.412985 –3.128490
VCB –3.599641 0.0059 –3.434779 –2.863383 –2.567800
Tab le 2. Augmented Dickey-Fuller test of negative emotions and risk-coping behaviors.
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the Inverse roots results of the two VAR models. As shown in the Figures, no root is outside the unit circle,
conrming that the estimated VAR models satisfy the stability condition.
Granger causality test and optimal lag date establishment
As depicted in Figure 3, all test results, with the exception of the Final Prediction Error (FPE), converge upon a
conclusion that the optimal lag order for both Model 1 and Model 2 is 3.First of all, AIC (Akaike Information
Criterion) and SC (Schwarz Criterion) are usually used as the core observation indicators of the optimal lag order
in the VAR model70,71, which should be considered mainly. Secondly, if the lag order above order 3 is selected for
model 1 according to the selection method of HPE, the model will need to estimate more parameters, and then
lose more sample size. In conclusion, according to the principle of the most concise lag order, the third-order lag
was selected to be included in the construction of VAR model.
Fig. 6. Inverse roots of AR Characteristic Polynomial in model 2 (negative emotion and violent criminal
behavior).
Fig. 5. Inverse roots of AR Characteristic Polynomial in model 1(negative emotion and information search
behavior).
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e optimal lag time, determined by both the Akaike Information Criterion (AIC) and Schwarz Information
Criterion (SC) in models 1 and 2, was found to be 3. erefore, VAR (Lag = 3) was selected for the test, and the
results are presented in Table4.
e probability that negative emotion does not have Granger Causality of information search behavior is
only 0.0000. Hence, negative emotions can predict information search behaviors in advance. Additionally, the
probability that negative emotion does not have Granger Causality of violent crime behavior is only 0.0189
(< 0.05). erefore, negative emotions can predict violent crime behaviors in advance. e research hypothesis
1 was conrmed in both models.
Verication of the factors inuencing the prediction eect
Table 5 Indicates that at the 1% signicance level, all variables related to types, issues, participants, and
stakeholders are stationary and meet the conditions for modeling. Unconstrained VAR models were constructed
for each variable and risk perception, and the AR root estimation method was employed to test the stationarity
of the estimated results of the VAR model. Aer the test, no root was found outside the unit circle, conrming
that the estimated VAR model satises the stability condition.
To assess Hypotheses 2 to 5, Granger causality analysis was conducted to evaluate the overall predictive
impact of the variables representing ten inuencing factors on public risk-coping behavior.
Table 6 presents the analysis results for public risk-coping behavior, as indicated by information search
behavior. e ndings revealed that among the two variables representing negative emotion types, negative
comments (A1, 8.30***) and negative feelings (A2, 4.11**), the probability that negative comments were not the
Factors Variables t-Statistic Prob.*
Test critic al values
1% level 5% level 10% level
Types Negative comments –17.24982 0.0000 –3.434624 –2.863315 –2.567763
Negative feelings –36.97040 0.0000 –3.434618 –2.863312 –2.567762
Issues
policy formulation –17.24982 0.0000 –3.434624 –2.863315 –2.567763
policy implementation –36.74461 0.0000 –3.434618 –2.863312 –2.567762
policy eectiveness –18.88733 0.0000 –3.434624 –2.863315 –2.567763
Participants Institution emotions –17.61006 0.0000 –3.434624 –2.863315 –2.567763
Individual emotions –26.63745 0.0000 –3.434618 –2.863312 –2.567762
Stakeholders
Key stakeholders –25.65244 0.0000 –3.434618 –2.863312 –2.567762
Non-key stakeholders –6.016446 0.0000 –3.434664 –2.863333 –2.567773
Marginal stakeholders –18.71895 0.0000 –3.434624 –2.863315 –2.567763
Tab le 5. Augmented Dickey-Fuller test of the factors.
Null Hypothesis F-Statistic Prob. Lag
Model1: Negative emotions does not Granger Cause Information Searching Behaviors 33.6063*** 0.0000 3
Model 2: Negative emotions does not Granger Cause Violent Crime Behaviors 3.33127* 0.0189 3
Tab le 4. Granger Causality Test. Model1: Max lag(= 3) selected according to Akaike information criterion
(AIC) by building VAR model. AIC(Lag = 1),17.29692; AIC(Lag = 2),17.28203 AIC(Lag = 3),17.23450;
SC(Lag = 1), 17.31872; SC(Lag = 2),17.31836; SC(Lag = 3),17.28536. Model 2: Max lag(= 3) selected according
to Akaike information criterion (AIC) by building VAR model. AIC(Lag = 1), 6.938467; AIC(Lag = 2), 6.818072;
AIC(Lag = 3), 6.774964; SC(Lag = 1), 6.960723; SC(Lag = 2), 6.855167; SC(Lag = 3), 6.826896. *signicant at 0.05;
**signicant at 0.01; ***signicant at 0.001.
Lag LogL LR FPE AIC SC HQ
Model 1
0 –12905.96 NA 327279.8 18.37432 18.38180 18.37712
1 –12058.90 1690.512 68565.69 17.29692 17.31872 17.18261
2 –12041.07 35.53914 96643.81 17.28203 17.31836 17.16850
3 –11994.31 93.04316 90937.43 17.23450* 17.28536* 17.11323*
Model 2
0 –5229.338 NA 5.517510 7.383681 7.391100 7.386453
1 –4909.904 637.5160 3.534997 6.938467 6.960723 6.946782
2 –4820.604 177.9689 3.134025 6.818072 6.855167 6.831930
3 –4786.062 68.74312* 3.001793* 6.774964* 6.826896* 6.794366*
Tab le 3. Result of optimal lag order test. *Indicates lag order selected by criterion.
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Granger cause of risk-coping behavior was less than 0.001. In contrast, the probability that negative feelings were
not the Granger cause of risk-coping behavior was less than 0.01. us, negative comments can better predict
public risk-coping behavior, conrming Hypothesis 2.
Regarding the analysis of issues, policy formulation (B1, 10.36***) was found to predict risk-coping behavior,
while policy implementation and policy eectiveness were not identied as Granger causes of risk-coping
behavior. e probabilities were 0.446 (B1) and 0.503 (B2). erefore, public risk-coping behavior cannot be
predicted by negative emotions related to these two issues, supporting Hypothesis 3.
Analysis of participants indicated that negative emotions from institutions (C1, 0.33) could not predict public
risk-coping behavior, while negative emotions from individuals (C2, 16.38***) might be the cause of public risk-
coping behavior. us, Hypothesis 4 was validated.
In the variables related to stakeholders, the probability that negative emotions from key stakeholders (D1,
14.01 ***) were not the Granger cause of public risk-coping behavior was less than 0.001. is suggests that
negative emotions from key stakeholders can predict risk-coping behavior, while negative emotions from
non-key stakeholders (D2, 0.048) and marginal stakeholders (D3, 0.17) cannot predict risk-coping behavior.
erefore, Hypothesis 5 was conrmed.
Table7 presents the analysis results for public risk-coping behavior represented by violent crime behavior.
e outcomes indicated that among the two variables representing negative emotion types, negative comments
(A1, 9.09***) and negative feelings (A2, 1.90), the probability that negative comments were not the Granger
cause of risk-coping behaviors was less than 0.001. In contrast, the probability that negative feelings were not the
Granger cause of risk-coping behavior was greater than 0.05. erefore, negative comments can better predict
public risk perception, thus conrming Hypothesis 2.
Concerning the three variables related to relocation issues, the predictive eect of policy formulation (B1,
8.54***, p < 0.001) was superior to policy implementation (B2, 3.28**, p < 0.01) and policy eectiveness (B3,
2.85*, p < 0.05). e probability that negative emotions on the policy formulation issue were not the Granger
cause of risk-coping behavior was lower, validating Hypothesis 3.
Among the two variables representing negative emotions from dierent participants, emotions from
institutions (C1, 3.66**) and emotions from individuals (C2, 10.36***), the probability that negative emotions
from individuals were not the Granger cause of risk-coping behavior was less than 0.001. In contrast, the
probability that negative emotions from institutions were not the Granger cause of risk-coping behavior was less
than 0.01. us, negative emotions from individuals can better predict public risk-coping behavior, conrming
Hypothesis 4.
In the variables related to stakeholders, the probability that negative emotions from key stakeholders (D1,
7.54***, P = 6.E-08) were not the Granger cause of public risk perception was the least. is suggests that negative
emotions from key stakeholders can better predict risk-coping behavior than negative emotions from non-key
stakeholders (D2, 1.92*, P < 0.05) and marginal stakeholders (D3, 5.33***, P = 7.E-05). erefore, Hypothesis 5
was veried.
Discussions and conclusions
Summary of Major ndings and contributions
e present research veried the impact of negative emotions on public risk-coping behaviors1–4, but fewer
studies focused on the prediction of negative emotions on public risk-coping behaviors. is study veried that
negative emotions can eectively predict public information searching behaviors and violent crime behaviors
using the case of relocation policy of Beijing Municipal Government. Our ndings further explored the factors
inuencing the prediction eect. e results indicated that: (1) e publics’ negative emotions expressed online
towards the relocation policy can predic t the public risk-coping behaviors in advance; (2) e negative comments
A1 A2 B1 B2 B3 C1 C2 D1 D2 D3
Lag 5 5 5 5 5 5 5 6 16 5
SC 6.67 3.097 6.04 3.47 4.83 5.46 5.92 6.16 3.08 5.15
F 9.09*** 1.90 8.54*** 3.28** 2.85* 3.66** 10.36*** 7.54*** 1.92* 5.33***
P 2.E-08 0.0909 6.E-08 0.0060 0.0144 0.0027 9.E-10 6.E-08 0.0156 7.E-05
Tab le 7. Granger Causality Tests of dierent emotion variables and violent crime behaviors. *signicant at
0.05; **signicant at 0.01; ***signicant at 0.001.
A1 A2 B1 B2 B3 C1 C2 D1 D2 D3
Lag 3 3 3 3 3 3 3 3 3 3
SC 17.19 13.64 16.61 14.03 15.38 16.02 16.41 16.63 13.80 15.75
F 8.30*** 4.11** 10.36*** 0.89 0.78 0.33 16.38*** 14.01*** 0.048 0.17
P 0.000 0.007 0.000 0.446 0.503 0.803 0.000 0.000 0.986 0.914
Tab le 6. Granger Causality Tests of dierent emotion variables and information search behaviors. *signicant
at 0.05; **signicant at 0.01; ***signicant at 0.001.
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can better predict the public risk-coping behaviors than the negative feelings; (3) e negative emotions on issue
of relocation policy formulation can better predict the public risk-coping behaviors than on issues of relocation
policy eectiveness and relocation policy implementation. (4) e negative emotions from individuals can better
predict public risk-coping behaviors than from institutions. (5) e negative emotions from key stakeholder can
better predict the public risk-coping behaviors than from non-key stakeholder or marginal stakeholder.
e theoretical contributions of this study encompass the following points. 1)Validation of the predictive
relationship between negative emotions and public risk-coping behaviors. is nding is consistent with existing
literature on the impact of emotions on decision-making and behavior, as exemplied by Lerner & Keltner, who
argued that emotional states can signicantly inuence an individual’s information processing and decision-
making processes72. 2) Revealing dierences in predictive eectiveness among dierent types of emotions and
emotional issues. is aligns with Schwarz & Clore’s “Emotion as Information” hypothesis, which posits that
dierent types of emotions carry distinct information and subsequently aect an individual’s cognition and
behavior73. 3) Emphasizing the role of information sources (individuals vs. institutions) in prediction. is echoes
Granovetter’s theory of the strength of weak ties, suggesting that weak connections may have greater inuence
in the dissemination of information, albeit with a focus on the emotional inuence aspect74. 4) Highlighting the
importance of key stakeholders in predicting risk-coping behaviors. is discovery oers a novel perspective
on stakeholder theory, underscoring the signicance of identifying and prioritizing key stakeholders in public
policy and risk management, which is consistent with Mitchell, Agle & Wood’s discussions on stakeholder
classication and inuence75.
ese ndings have two practical contributions: (1) is study promotes the prediction of public risk-coping
behaviors in the relocation policy. In the process of the relocation policy, in order to eectively reduce the public
risk perception, which determines the successful implementation of the policy, the relocation policy related
departments should pay attention to the negative public opinion on the network in a timely manner, because the
negative emotions can eectively predict the pubic risk-coping behaviors. If the content of the policy is adjusted
according to the negative emotions, the policy risk can be reduced. In the practice of relocation policy, we
should pay attention on the negative emotions from the key stakeholders and individuals, the negative emotions
on issue of policy formulation and the negative comments. (2) is study adds a case of the relocation policy
of Beijing Municipal Government to the research elds of urban relocation policies in China. In the process of
rapid economic development, China has to face the problems of urban relocation and it is of great signicance
that the relocation policies are successfully implemented. is study also further improves on our understanding
of relocation policy from the perspective of risk-coping behaviors. Simultaneously, it provides a case reference
for comparisons with urban relocations in other cities both in China and globally.
Based on the above ndings and contributions, three policy recommendations can be formulated as follows:
Firstly, to enhance the prediction and management of public risk-coping behaviors during relocation policies,
relevant departments should establish a real-time monitoring system for negative public opinions and emotions
expressed online. By promptly addressing these negative sentiments, especially those from key stakeholders and
individuals, as well as those related to policy formulation and negative comments, policy risks can be mitigated.
Secondly, to improve the eectiveness of relocation policies, policy-makers should incorporate a stakeholder-
centric approach, prioritizing engagement and communication with key stakeholders to gather insights and tailor
policies that better align with their concerns. Finally, policymakers should strive for transparency and education
campaigns to build trust and reduce negative emotions, thereby fostering a more positive environment for
successful relocation policies. By integrating these recommendations, China can better navigate the challenges
of urban relocation amidst rapid economic development and ensure the successful implementation of relocation
policies.
Limitations and suggestions for Future Research
e main limitations of this study include the following aspects. On the one hand, the primary limitation of this
study lies in the inherent constraints associated with manual coding. e identication of negative emotion types
and issues related to negative emotions relies on human judgment, introducing the possibility of deviations. To
address this concern, the study implemented a rigorous approach by training two independent coders. However,
the potential for subjectivity remains. Future research endeavors could explore the application of big data analysis
methods to achieve more objective identications, mitigating the limitations associated with manual coding.
On the other hand, although this paper veried the predictive relationship between online negative emotions
and risk coping behaviors through a panel Granger causality test, which concluded that the causality between
the two in a statistical sense is not equal to the actual causality, the perspective of this study is relatively novel
and still has some reference value. However, there is still an endogeneity problem to be solved in this study, and
further validation by using instrumental variables and other methods can be attempted in future studies. In
addition, whether there exists some potential commonality and interaction eect among dierent indicators is
also a topic worthy of in-depth study, and further useful additions can be made regarding indicators related to
negative public sentiment, such as economic indicators, information dissemination speed, media attention, and
public opinion bias. In future research, we will expand the sample size of social media data, increase comparisons
with other national or regional studies, and adopt both qualitative and quantitative research methods to reduce
data bias.
Data availability
Online negative emotions data are publicly available from the Boryou Technology (https://www.boryou.com/).
Public information searching behaviors data are publicly available from the Baidu search index ( h t t p : / / i n d e x . b
a i d u . c o m ) . Violent crime behaviors data obtained from Weibo accounts such as “@ Ping An Beijing,” and the
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Hebei Provincial Public Security Department, “@ Hebei Public Security Network Spokesman”. e datasets used
and/or analyzed during the current study are available from the corresponding author on reasonable request.
Received: 7 May 2024; Accepted: 12 November 2024
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Author contributions
Conceptualization, Q.X.; methodology, Q.X. and H.W.; soware, Q.X. and R.Z.; validation, Q.X., H.W. and R.Z.;
formal analysis, Q.X.; investigation, Q.X. and H.W.; resources, Q.X.; writing—original dra preparation, Q.X.;
writing—review and editing, Q.X. and R.Z.; visualization, Q.X.; supervision, Q.X.; All authors have read and
agreed to the published version of the manuscript.
Funding
is research was funded by the National Social Science Fund of China, grant number 21CGL045.
Declarations
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to Q.X.
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