Search Results (26)

In this paper, we present a stance detection model grounded in multi-task learning, specifically designed to address the intricate challenge of text stance analysis within social media comments. This model is structured with an embedding network, an encoder module, a sophisticated multi-task attention mechanism, an ensemble module, and a classification output layer. To augment the performance of stance detection, we employed sentiment analysis and toxicity language detection as auxiliary tasks. The sentiment analysis plays a pivotal role in enabling the model to capture the public opinion inclinations of both individual and collective users. By delving into these inclinations, our model can extract fine-grained stance elements, offering a more nuanced understanding of users’ positions. On the other hand, toxicity language detection aids in modeling the extreme tendencies of social media users towards specific events. It identifies manifestations of hatred, offensiveness, discrimination, and insult, thereby allowing the model to reconstruct users’ genuine stance information from these extreme expressions. Through the synergy of multi-task joint learning, the accuracy and reliability of the stance detection were significantly improved. To validate the efficacy of our proposed model, we selected two hot events as representative cases, one from the Chinese Weibo platform and the other from the English Twitter platform. A series of comprehensive tasks, including developing crawler programs, collecting data, performing data preprocessing, and conducting data annotation, were systematically executed. Subsequently, we applied our model to detect the stances within the comments related to these two events, categorizing them into three classes: support, opposition, and ambiguity. The experimental results demonstrate that our stance detection model, which integrates sentiment analysis and toxicity language detection, substantially improves the detection accuracy, outperforming traditional methods. Full article

Extreme weather events and rising temperatures pose significant risks, not only in urban areas but also in metropolitan forests, that affect the well-being of the people who visit them. City forests are considered one of the best bets for mitigating high temperatures within civic areas. Such areas modulate microclimates in contemporary cities, offering environmental, social, and economic advantages. Therefore, comprehending the intricate relationships between municipal forests and the climatic changes of various destinations is crucial for attaining healthier and more sustainable city environments for people. In this research, the thermal comfort index (Modified Temperature–Humidity Index (MTHI)) has been analysed using Landsat images of six urban forests in London during July 2022, when the area first experienced record-breaking temperatures of over 40 °C. Our results show a significant growth in the MTHI that goes from 2.5 (slightly hot) under normal conditions to 3.4 (hot) during the heat wave period. This situation intensifies the environmental discomfort for visitors and highlights the necessity to enhance their adaptability to future temperature increases. In turn, it was found that the places most affected by heat waves are those that have grass cover or that have small associated buildings. Conversely, forested regions or those with lakes and/or ponds exhibit lower temperatures, which results in enhanced resilience. These findings are noteworthy in their concentration on one of the UK’s most severe heat waves and illustrate the efficacy of integrating spectral measurements with statistical analyses to formulate customized regional initiatives. Therefore, the results reported will allow the implementation of new planning and adaptation policies such as incorporating thermal comfort into planning processes, improving green and blue amenities, increasing tree densities that are resilient to rising temperatures, and increasing environmental comfort conditions in metropolitan forests. Finally, the applicability of this approach in similar urban contexts is highlighted. Full article

Climate change is a global phenomenon having wide-ranging social, economic, ecological, and environmental sustainability implications. This study assesses climate change awareness, understanding, causes, mitigation measures, and practices among residents of the Kathmandu Valley through a mixed-method approach. Quantitative surveys with 433 respondents and four Focus Group Discussions (FGDs) are conducted with diverse demographics. Descriptive statistics is used to summarize quantitative data, and the chi-square (χ²) test is used to measure the associations between awareness, understanding, causes, mitigation measures, and practices among various demographics. The analysis shows that respondents frequently link climate change to extreme weather events, particularly flooding, severe hot and cold waves, and changes in rain precipitation patterns. Furthermore, the respondents identify deforestation, industrialization, and fossil fuels as the primary causes, with mitigation strategies such as afforestation, recycling waste, and use of renewable energies for long-term environmental sustainability. Similarly, the survey analysis also revealed that greenhouse gases like carbon dioxide and methane are major drivers of climate change; individuals, industries, and governments are held accountable for climate change with industries as key polluters. Furthermore, individuals are self-aware to adopt sustainable practices, and the government can play a vital role through policies promoting renewable energy, afforestation, and waste management, alongside raising awareness. Other highlights of the analysis have been raising voices of collective action at all levels, which is crucial to mitigate the impact of climate change. The study also addresses the gaps in comprehensive climate literacy and underscores the need for targeted educational initiatives to foster informed climate actions within the community. Likewise, the study brings the findings that policymakers should prioritize inclusive engagement strategies, ensuring that climate policies and adaptation programs are accessible, particularly to those who are less represented in environmental discourse, such as older adults and unschooled individuals. Full article

Bullet screen, a form of online video commentary in emerging social media, is widely used on video websites frequented by young people. It has become a novel means of expressing emotions towards videos. The characteristics, such as varying text lengths and the presence of numerous new words, lead to ambiguous emotional information. To address these characteristics, this paper proposes a Robustly Optimized BERT Pretraining Approach (RoBERTa) + Convolutional Neural Network (CNN) sentiment classification algorithm integrated with a sentiment lexicon. RoBERTa encodes the input text to enhance semantic feature representation, and CNN extracts local features using multiple convolutional kernels of different sizes. Sentiment classification is then performed by a softmax classifier. Meanwhile, we use the sentiment lexicon to calculate the emotion score of the input text and normalize the emotion score. Finally, the classification results of the sentiment lexicon and RoBERTa+CNN are weighted and calculated. The bullet screens are grouped according to their length, and different weights are assigned to the sentiment lexicon based on their length to enhance the features of the model’s sentiment classification. The method combines the sentiment lexicon can be customized for the domain vocabulary and the pre-trained model can deal with the polysemy. Experimental results demonstrate that the proposed method achieves improvements in precision, recall, and F1 score. The experiments in this paper take the Russia–Ukraine war as the research topic, and the experimental methods can be extended to other events. The experiment demonstrates the effectiveness of the model in the sentiment analysis of bullet screen texts and has a positive effect on grasping the current public opinion status of hot events and guiding the direction of public opinion in a timely manner. Full article

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. Full article

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas. Full article

Many aspects of intangible cultural heritage have associated objects of material culture that augment or enable aspects of intangible heritage to be exercised or emphasized. Christmas markets have been publicized as the quintessential event in Germany leading up to Christmas, with the over 2000 locations attracting large numbers of local, domestic, and international visitors. From their origins as mercantile venues during the medieval period, Christmas markets have evolved into multisensory social and experiential events, where the acquisition of Christmas decorations or gifts has been supplanted by the consumption of mulled wine in a social setting. Christmas markets represent intangible cultural heritage staged in ephemeral surroundings. While the abundance of material culture in Christmas markets is widely understood, this focuses on the objects offered for sale at the markets, rather than the objects that characterize a Christmas market and enable its functioning. This paper provides the first comprehensive assessment of the portable material culture associated with the German Christmas markets, covering objects as diverse as payment tokens, lapel pins, special postmarks, beer mats, and commemorative cups issued for the consumption of mulled wine. These objects, as well as numerous other manifestations of material culture, are discussed in the wider framing of the materiality of the markets, examining their ontological qualities within the multiple spheres in which these objects attain meaning (i.e., personal, event, social, and public spheres). It demonstrates that the wide range of alienable material culture associated with German Christmas markets has different manifestations of materiality, depending on the viewpoint of the user (i.e., participant, vendor, organizer), and these manifestations have different expressions of representativeness. On this foundation, this paper examines the various groups of portable and alienable material culture and discusses them in terms of their authenticity and to what extent these are representative of German Christmas markets. While all items have a connection with Christmas markets and function as symbolic shorthand souvenirs, commemorative cups issued for the consumption of hot drinks as well as the deposit tokens associated with these are both genuine and authentic and are also representative of the conceptual, social, and experiential dimensions of the event. Full article

Floods and landslides cause continuous damage to ecosystems, infrastructures, and populations. Particularly, the occurrence and the existence of different natural hazards in the same territory highlight the need to improve risk mitigation strategies for local authorities and community resilience solutions for inhabitants. Analyzing and mapping social vulnerability provides information about the main features of a specific community to deal with natural events. Specifically, the interaction between multi-hazards and the socio-economic environment suggests multidisciplinary assessments that merge the physical and the socio-economic features of the affected territories, providing a useful approach to support multi-risk reduction planning. In this context, the article focuses on integrating landslide and flood hazard scenarios with social vulnerability in the Basilicata Region (southern Italy) at the census tract level. Thirteen municipalities were chosen as multi-hazard hot spots, while open-source platforms were selected for hazard and social vulnerability data collection and analyses. A geographic information system (GIS)-based approach was applied to combine different hazard scenarios with social vulnerability distribution among 1331 census tracts to detect the most vulnerable sub-municipality areas that need special attention in multi-risk reduction strategies. The results are presented in the form of maps, which provide a relevant suitable tool in local emergency planning. Full article

A rare compound hot and drought (CHD) event occurred in the Yangtze River Basin (YRB) in the summer of 2022, which brought serious social crisis and ecological disaster. The analysis of the causes, spatiotemporal characteristics and impacts of this event is of great significance and value for future drought warning and mitigation. We used the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, meteorological data, hydrological data and satellite remote sensing data to discuss the spatiotemporal evolution, formation mechanism and the influence of the CHD event. The results show that the drought severity caused by the CHD event was the most severe during 2003 and 2022. The CHD event lasted a total of five months (from July to November), and there were variations in the damage in different sub-basins. The Wu River Basin (WRB) is the region where the CHD event lasted the longest, at six months (from July to December), while it also lasted four or five months in all the other basins. Among them, the WRB, Dongting Lake Rivers Basin (DLRB) and Mainstream of the YRB (MSY) are the three most affected basins, whose hot and drought severity values are 7.750 and −8.520 (WRB), 7.105 and −9.915 (DLRB) and 6.232 and −9.143 (MSY), respectively. High temperature and low precipitation are the direct causes of the CHD event, and the underlying causes behind this event are the triple La Niña and negative Indian Ocean Dipole event. The two extreme climate events made the Western Pacific Subtropical High (WPSH) unusually strong, and then the WPSH covered a more northerly and westerly region than in previous years and remained entrenched for a long period of time over the YRB and its adjacent regions. Moreover, this CHD event had a devastating impact on local agricultural production and seriously disrupted daily life and production. Our results have implications for the study of extreme disaster events. Full article

In recent years, social media has emerged as an important channel for the dissemination of destination branding. Despite the fact that the dissemination of information through social media enables a broader audience to become acquainted with destinations, the dissemination process of trending events exhibits variances. Consequently, the precise impact of the underlying mechanisms that govern the spread of information on the efficacy of disseminating destination brand trending events remains ambiguous. In an endeavor to bridge this gap, an improved SEIR model was developed in this research to investigate the dynamic dissemination mechanisms and influencing factors of destination trending events within social media. The model was applied to simulate the diffusion mechanism of destinations’ trending events. The results show that during the dissemination process of destination trending events on social media, the proportion of users affected at different stages influences the ultimate effectiveness of information propagation. In light of these insights, this research proposes a social media trending event dissemination strategy to aid in enhancing the propagation efficiency of destination brands through existing resources. Full article

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors. Full article

Collective resilience is the ability of human beings to adapt and collectively cope with crises in adversity. Emotional expression is the core element with which to characterize the psychological dimension of collective resilience. This research proposed a stage model of collective resilience based on the temporal evolution of the public opinions of COVID-19 in China’s first anti-pandemic cycle; using data from hot searches and commentaries on Sina Weibo, the changes in the emotional patterns of social groups are revealed through analyses of the sentiments expressed in texts. A grounded theory approach is used to elucidate the factors influencing collective resilience. The research results show that collective resilience during the pandemic exhibited an evolutionary process that could be termed, “preparation–process–recovery”. Analyses of expressed sentiments reveal an evolutionary pattern of “positive emotion prevailing–negative emotion appearing–positive emotion recovering Collective resilience from a psycho-emotional perspective is the result of “basic cognition-intermediary condition-consequence” positive feedback, in which the basic cognition is expressed as will embeddedness and the intermediary conditions include the subject behavior and any associated derived behavioral characteristics and spiritual connotation. These results are significant both theoretically and practically with regard to the reconstruction of collective resilience when s‘ force majeure’ event occur. Full article

(1) Background: Hot social events contain a large amount of public opinion information, and a more detailed analysis of this information will help the relevant parts to formulate more targeted supervision strategies at different stages and for the public opinion publishers involved in the event discussions, so as to achieve efficient management of public opinion; (2) Methods:Based on stakeholder theory and life cycle theory, this study constructs stakeholder classification system by using keyword identification method; adopts LDA model to complete topic clustering; analyzes and summarizes topic evolution pattern by calculating topic similarity; (3) Results: The study divided the stakeholders involved in the Jiang Ge case into 10 categories, and the results of topic clustering were divided into two categories according to the content of the topics, which were based on the case itself and on the parties involved in the case; it was found that each stakeholder focused on a different topic with different emphasis, no matter the topic of public opinion or the different life cycle stages of public opinion. Based on the differences in topic similarity between adjacent stages, the topic evolution patterns of different stakeholders were categorized into three types; (4) Conclusions: Example verification shows that the method presented in this paper can dig out the topic focus and evolution path of stakeholders in the field of public opinion, and provide a horizontal and vertical comparative analysis between stakeholders and different life cycle stages. Full article

Intangible cultural heritage (ICH) can be a valuable tourism resource for both government and local communities. However, the complex definition and the massive and fragmented nature of ICH data make it hard to review and conclude research trends and future directions of ICH tourism. In this study, 85 keywords extracted from ICH definitions are input in the Web of Science database before collecting papers indexed in the Social Sciences Citation Index, the Arts and Humanities Citation Index, and the Conference Proceedings Citation Index-Social Science and Humanities. Later, a systematic literature review of 418 ICH tourism studies from 76 countries published between 2000 and 2021 were conducted based on three groups of questions. The findings mainly illustrated that: (1) Currently research in ICH tourism is mainly composed of three themes: resource planning and sustainability, the impact of tourism development, and tourist behavior and destination marketing; (2) topics related to food tourism, sacred knowledge, traditional management systems, traditional management systems, legends, and myths can achieve high impact; (3) in the last five years, scholars have reduced using the official full name of ICH in tourism studies, while the category of “social practices, rituals and festive events” has become a hot topic since 2010; (4) ecotourism, culinary tourism, festival tourism, and religious tourism are the most discussed in ICH tourism research, and they will still be intensive topics in near future; (5) future directions in ICH tourism research are resultant of three vectors: place making, technology, and environment. The results present a comprehensive picture of current popular ICH topics and predict future directions in the field of ICH tourism. The systematic review of literature can help contribute to both theoretical construction, heritage preservation, and tourism practices. Full article

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods. Full article