Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China

Jiang, Hanchen; Qiang, Maoshan; Lin, Peng

doi:10.1016/j.eiar.2016.06.004

Cited by 79 publications

(54 citation statements)

References 95 publications

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“…Extracting sentiments during disaster events can help develop stronger situational awareness of the disaster zone. Jiang et al [ 13 ] used sentiment analysis to systematically assess the online public onions on an ex-post evaluation of a large infrastructure project in China. Yu and Wang [ 14 ] performed sentiment analysis to examine U.S. soccer fan’s emotional reactions towards the World Cup 2014 games from their tweets.…”

Section: Introductionmentioning

confidence: 99%

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Cao

MacNaughton

Deng

et al. 2018

IJERPH

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Twitter provides a rich database of spatiotemporal information about users who broadcast their real-time opinions, sentiment, and activities. In this paper, we sought to investigate the holistic influence of land use and time period on public sentiment. A total of 880,937 tweets posted by 26,060 active users were collected across Massachusetts (MA), USA, through 31 November 2012 to 3 June 2013. The IBM Watson Alchemy API (application program interface) was employed to quantify the sentiment scores conveyed by tweets on a large scale. Then we statistically analyzed the sentiment scores across different spaces and times. A multivariate linear mixed-effects model was used to quantify the fixed effects of land use and the time period on the variations in sentiment scores, considering the clustering effect of users. The results exposed clear spatiotemporal patterns of users’ sentiment. Higher sentiment scores were mainly observed in the commercial and public areas, during the noon/evening and on weekends. Our findings suggest that social media outputs can be used to better understand the spatial and temporal patterns of public happiness and well-being in cities and regions.

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Section: Introductionmentioning

confidence: 99%

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Cao

MacNaughton

Deng

et al. 2018

IJERPH

View full text Add to dashboard Cite

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“…(1) decline of stable value I * with the increase of t d and contrast between the model with and without risk mitigation prove the rationality and feasibility of the novel model; (2) regardless of change of parameters in the model, RPDP has three phases: (1) risk first slightly propagates, (2) risk then breaks out, and (3) in last phase value of I will converge to a stable value I * . The impact of a stakeholder's risk-resisting ability on ultimate stable I * is more sensitive than risk mitigation ability when probabilities of risk occurrence and mitigation vary from 0 to 1; (3) the stakeholders with strong risk-mitigating ability have more partners than others.…”

Section: Discussionmentioning

confidence: 70%

“…Large hydropower project requiring substantial investment and tremendous public resources belongs to the critical infrastructure engineering and has more profound impact on local communities and even a nation than general infrastructure engineering [1,2]. Because of the varied essence of construction business activity, various types of construction process, complex construction environment, and longtime construction [3], in large hydropower project construction (LHPC), stakeholders are widely exposed to a high degree of economic risk, including construction cost/duration overrun, delayed payment, changes in government regulation, poor performance of suppliers or manufacturers, and other situations [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Risk Propagation of Delayed Payment in Stakeholder Network of Large Hydropower Project Construction considering Risk Resistance and Mitigation

Chen

Liu

et al. 2018

Mathematical Problems in Engineering

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Large hydropower project involves numerous stakeholders in the construction phase. The risk of delayed payment may escalate into an event of catastrophic economic loss through complex stakeholders’ relationships. This paper builds a complex stakeholder network by collecting all contract prices in large hydropower project construction (LHPC) and proposes a novel mathematical model based on Cellular Automata Susceptible-Infected-Susceptible (CA-SIS) framework to analyze the risk propagation of delayed payment (RPDP). In the model, to approximate actual loss diffusion, risk-resisting and risk-mitigating abilities of stakeholders are combined, and relevance between risk mitigation and time is taken into account. The rationality and feasibility of the novel model are illustrated through its application to an actual LHPC. The simulation results include the following: RPDP presents three phases: risk first slightly propagates, risk then exponentially breaks out, and at last propagation consequence will reach a stable state related to stakeholders’ risk-resisting ability closely; in respect of stakeholders’ risk-mitigating ability and property of the network, RPDP range decreases with the increase of number of partners and homogeneity of contract price distribution; in respect of stakeholders’ risk-resisting ability, RPDP range decreases with the decrease of number of partners. This research can serve as a powerful complement to analyze and control the delayed payment in LHPC.

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“…Public opinion analytics is essential for a better understanding of the social environment and the dynamics of social changes. Among various sources of public opinion data, social media data is attracting great attention from researchers, as it provides highly valuable data about the public attitudes and opinions on controversial social events [6] and has been widely used to monitor and analyze public responses to natural or social phenomena [7]. For social science research, the nature of social media data is significantly different from the data collected by traditional questionnaire surveys [8].…”

Section: Introductionmentioning

confidence: 99%

Climate Change Communication in an Online Q&A Community: A Case Study of Quora

et al. 2018

Self Cite

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An emerging research trend in climate change studies is to use user-generated-data collected from social media to investigate the public opinion and science communication of climate change issues. This study collected data from the social Q&A website Quora to explore the key factors influencing the public preferences in climate change knowledge and opinions. Using web crawler, topic modeling, and count data regression modeling, this study quantitatively analyzed the effects of an answer's textual and auxiliary features on the number of up-votes received by the answer. Compared with previous studies based on open-ended surveys of citizens, the topic modeling result indicates that Quora users are more likely to talk about the energy, human and societal issues, and scientific research rather than the natural phenomena of climate change. The regression modeling results show that: (i) answers with more emphasis on specific subjects, but not popular knowledge, about climate change can get significantly more up-votes; (ii) answers with more terms of daily dialogue will get significantly fewer up-votes; and (iii) answers written by an author with more followers, with a longer text, with more images, or belonging to a question with more followers, can get significantly more up-votes.

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Assessment of online public opinions on large infrastructure projects: A case study of the Three Gorges Project in China

Cited by 79 publications

References 95 publications

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Risk Propagation of Delayed Payment in Stakeholder Network of Large Hydropower Project Construction considering Risk Resistance and Mitigation

Climate Change Communication in an Online Q&A Community: A Case Study of Quora

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