Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies' social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers' work and family, and (e) users' action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week', ‘safety first', and ‘construction team', (b) the largest clusters were ‘safety training', ‘occupational health and safety administration', and ‘health and safety environment', (c) the most active users were ‘Parallel Consultancy Ltd.', ‘Pike Consulting Group', and ‘Global Training Canada', and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.

Weibo users and Academia's foci on tourism safety: Implications from institutional differences and digital divide.

Tourism safety is essential for tourists and tourism practitioners. This study conducted a bibliometric analysis using VOSviewer and CiteSpace for 2018 articles indexed on the Web of Science (WoS). It also analysed 7293 Weibo posts between 1977 and 2022 using Python, MYSQL, AI sentiment, and Tableau. The first tourism safety publication on WoS appeared in 1977, while the first Weibo microblog dated was dated back to 2011. Compared to the information posted on Weibo, the annual publications about tourism safety on WoS recorded a stable increment. On Web of Science (WoS), the academic staff and universities produced the largest number of tourism safety posts. On the flip side, the most productive organisations on Weibo are government agencies in popular tourism destinations. "Accident", "medical tourism", "environment", "mediating role", and "hospitality" were important burst nodes in tourism safety on WoS. "Quality", "accident", and health-related words were the foci on both Weibo and WoS. On Web of Science, the top 10 most popular keywords of tourism safety-related articles could be classified into two groups: health ("Covid-19", "restoration", "pandemics", "Sars-Cov-2", "Sars", "mental health") and IT terminologies ("big data", "artificial intelligence"). It has been concluded that "artificial intelligence (AI)" is more likely to be included in the keywords on tourism researched by academia. In contrast, the public may not know about or use AI in the tourism industry. Besides, the top 10 most popular keywords on Weibo related to tourism risks and hazards were drowning and traffic risks and hazards, such as drowning and traffic risks. The digital divide may explain such a difference: the academic circle benefits more from the digital age than laypersons. It may also be the result of institutional differences and information asymmetry.

Can a chatbot enhance hazard awareness in the construction industry?

Safety training enhances hazard awareness in the construction industry. Its effectiveness is a component of occupational safety and health. While face-to-face safety training has dominated in the past, the frequent lockdowns during COVID-19 have led us to rethink new solutions. A chatbot is messaging software that allows people to interact, obtain answers, and handle sales and inquiries through a computer algorithm. While chatbots have been used for language education, no study has investigated their usefulness for hazard awareness enhancement after chatbot training. In this regard, we developed four Telegram chatbots for construction safety training and designed the experiment as the treatment factor. Previous researchers utilized eye-tracking in the laboratory for construction safety research; most have adopted it for qualitative analyses such as heat maps or gaze plots to study visual paths or search strategies via eye-trackers, which only studied the impact of one factor. Our research has utilized an artificial intelligence-based eye-tracking tool. As hazard awareness can be affected by several factors, we filled this research void using 2-way interaction terms using the design of experiment (DOE) model. We designed an eye-tracking experiment to study the impact of site experience, Telegram chatbot safety training, and task complexity on hazard awareness, which is the first of its kind. The results showed that Telegram chatbot training enhanced the hazard awareness of participants with less onsite experience and in less complex scenarios. Low-cost chatbot safety training could improve site workers' danger awareness, but the design needs to be adjusted according to participants' experience. Our results offer insights to construction safety managers in safety knowledge sharing and safety training.

Housing safety and health academic and public opinion mining from 1945 to 2021: PRISMA, cluster analysis, and natural language processing approaches.

Housing safety and health problems threaten owners' and occupiers' safety and health. Nevertheless, there is no systematic review on this topic to the best of our knowledge. This study compared the academic and public opinions on housing safety and health and reviewed 982 research articles and 3,173 author works on housing safety and health published in the Web of Science Core Collection. PRISMA was used to filter the data, and natural language processing (NLP) was used to analyze emotions of the abstracts. Only 16 housing safety and health articles existed worldwide before 1998 but increased afterward. U.S. scholars published most research articles (30.76%). All top 10 most productive countries were developed countries, except China, which ranked second (16.01%). Only 25.9% of institutions have inter-institutional cooperation, and collaborators from the same institution produce most work. This study found that most abstracts were positive (n = 521), but abstracts with negative emotions attracted more citations. Despite many industries moving toward AI, housing safety and health research are exceptions as per articles published and Tweets. On the other hand, this study reviewed 8,257 Tweets to compare the focus of the public to academia. There were substantially more housing/residential safety (n = 8198) Tweets than housing health Tweets (n = 59), which is the opposite of academic research. Most Tweets about housing/residential safety were from the United Kingdom or Canada, while housing health hazards were from India. The main concern about housing safety per Twitter includes finance, people, and threats to housing safety. By contrast, people mainly concerned about costs of housing health issues, COVID, and air quality. In addition, most housing safety Tweets were neutral but positive dominated residential safety and health Tweets.

Housing safety and health academic and public opinion mining from 1945 to 2021: PRISMA, cluster analysis, and natural language processing approaches

Housing safety and health problems threaten owners' and occupiers' safety and health. Nevertheless, there is no systematic review on this topic to the best of our knowledge. This study compared the academic and public opinions on housing safety and health and reviewed 982 research articles and 3,173 author works on housing safety and health published in the Web of Science Core Collection. PRISMA was used to filter the data, and natural language processing (NLP) was used to analyze emotions of the s. Only 16 housing safety and health articles existed worldwide before 1998 but increased afterward. U.S. scholars published most research articles (30.76%). All top 10 most productive countries were developed countries, except China, which ranked second (16.01%). Only 25.9% of institutions have inter-institutional cooperation, and collaborators from the same institution produce most work. This study found that most s were positive (n = 521), but s with negative emotions attracted more citations. Despite many industries moving toward AI, housing safety and health research are exceptions as per articles published and Tweets. On the other hand, this study reviewed 8,257 Tweets to compare the focus of the public to academia. There were substantially more housing/residential safety (n = 8198) Tweets than housing health Tweets (n = 59), which is the opposite of academic research. Most Tweets about housing/residential safety were from the United Kingdom or Canada, while housing health hazards were from India. The main concern about housing safety per Twitter includes finance, people, and threats to housing safety. By contrast, people mainly concerned about costs of housing health issues, COVID, and air quality. In addition, most housing safety Tweets were neutral but positive dominated residential safety and health Tweets.

Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia;(b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist;(c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories;(d) timesaving, innovation, and digitalization are the most common AI prospects;and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

Housing Real Estate Economics and Finance

Housing research is one of the hot topics in many countries. This paper provides a quick review of the housing economics research in the US, Sweden, Latvia, China, Corsica, and Italy published in this special issue. Bao and Shah studied the effects of home-sharing platforms in general and the effects of the US' Airbnb on neighbourhood rent. Wilhelmsson's results showed that interest rates directly affected house prices and indirectly affected bank loans in Sweden. Caudill and Mixon threw light on the relative negotiating power of the buyer and seller as a key element of real estate price models. Čirjevskis presented a real application of 'step-by-step';valuation options for real estate development projects as a managerial risk management tool for similar real estate development projects in the EU to make investment decisions during COVID-19 and in the post-COVID-19 era. Pelizza and Schenk-Hoppéused an exponential Ornstein–Uhlenbeck process to model price dynamics provincially and regionally to estimate the liquidation value.

Economic Development and Mountain Tourism Research from 2010 to 2020: Bibliometric Analysis and Science Mapping Approach

Due to hectic city lives and the growing health concerns in light of the global pandemic, mountain tourism has become increasingly popular worldwide, which has increased the related research. Based on traditional bibliometric laws, such as those authored by Price, Bradford, Lotka, and Zipf, this study acquired 1413 mountain tourism journal articles via bibliometric analysis and identified the most influential journal articles, researchers, and countries in mountain tourism research as indexed in the Web of Science (WoS) database during 2010–2020. The keyword analysis revealed mountain tourism’s emerging research topics, including climate change, sustainable development, sustainability, sustainable tourism, protected areas, rural tourism, and conservation. The most influential journal was Sustainability in the mountain tourism. The research results showed that China, the U.S., and Romania produced the most significant mountain tourism articles indexed in the WoS. Most developed countries in Europe had the highest average and average normalized citations, which indicated that they may have more influence in this field as compared to other countries. Some developing countries, such as India, Nepal, and China, had higher citations, average citations, and/or average normalized citations than other countries. The main research trend was the sustainable development aspect of mountain-based tourism during the COVID-19 pandemic. We identified the research gap in WoS;although there is some research shedding light on tourism via bibliometrics, mountain tourism bibliometric analysis and science mapping via VOSViewer is scarce. The paper summarizes the critical aspects of the current discussion of sustainable mountain tourism, such as transport and coopetition (i.e., combing with cooperation and competition) in mountain tourism areas. The results indicated that government agencies and destination managers need to strike a balance between sustainable mountain tourism development and environment and natural landscape conservation after COVID-19.

Tourist Satisfaction, Willingness to Revisit and Recommend, and Mountain Kangyang Tourism Spots Sustainability: A Structural Equation Modelling Approach

The rapid development of society and economy has imposed insurmountable pressure on the urban population, and many people suffer from sub-health conditions. Kangyang tourism (KT), which combines the concepts of health preservation, ecological resources, and tourism activities, has developed rapidly in China since the concept was first introduced. Although previous studies have examined the relationship among experience, satisfaction, and intention, there is a lack of study of experience value’s impact on Mountain Kangyang Tourim Spots’ Sustainability, that is, willingness to revisit and recommend to other potential tourists. Consequently, an “experience value (functional value, contextual value, emotional value, cognitive value, economic value), satisfaction and post-trip willingness to revisit and recommend” framework is suggested to examine mountain Kangyang tourism (MKT). Data were collected from 500 tourists after visiting five well-known MKT destinations in Panzhihua city. Using the structural equation modeling (SEM) technique, the results suggest that tourist satisfaction plays an important role in experience value as well as willingness to revisit and recommend the MKT spots after their revisits. Our research offers some practical suggestions for MKT destination operators when they design and provide MKT destinations. The results would be useful for governments and non-profit organizations which attempt to promote MKT.

Does formal or informal institution prevail regarding the perception of wearing masks during the COVID-19 pandemic? Evidence from French Tweets

With the spread of COVID-19, many countries have issued different policies and regulations on wearing masks to prevent the pandemic spread. While wearing surgical masks is a major way to prevent COVID-19 in Asian countries, it is controversial in the western world. French nations were sceptical on wearing masks as it is associated with religion freedom suppression, gender inequality or even the infamous “the veil in school” (le voile à l’école) incident. As the COVID-19 problem escalated, wearing masks proved to be effective in reducing the spread of virus and many French-speaking nations had to wear masks according to regulations and policies. This raised the questions, ‘What is the French nations’ viewpoint?’ and ‘Does formal or informal institution prevail in wearing masks during COVID-19?’ Through data mining from Twitter, this study attempted to investigate how wearing mask (‘port du masque’) policies and regulations were perceived by French-speaking countries during COVID-19. In total, 1288 French Tweets were collected and analysed. While it may be expected that French Tweets regarding wearing masks is primarily linked to health and culture, French Tweets on wearing masks were mainly linked to laws and policies.