Spatial-temporal Patterns of Urban Parks’Effects on the Sentiments and Their Associated Factors Based on Social Media Data——a Case Study in Beijing

As the pivotal green space,urban parks play an important role in urban residents’daily activities.Thy can not only bring people physical health,but also can be more likely to elicit positive sentiment to those who visit them.Recently,social media big data has provided new data sources for sentiment analysis.However,there was limited researches that explored the connection between urban parks and individual’s sentiments.Therefore,this study firstly employed a pre-trained language model(BERT,Bidirectional Encoder Representations from Transformers)to calculate sentiment scores based on social media data.Secondly,this study analysed the relationship between urban parks and individual’s sentiment from both spatial and temporal perspectives.Finally,by utilizing structural equation model(SEM),we identified 13 factors and analyzed its degree of the influence.The research findings are listed as below:①It confirmed that individuals generally experienced positive sentiment with high sentiment scores in the majority of urban parks;②The urban park type showed an influence on sentiment scores.In this study,higher sentiment scores observed in Eco-parks,comprehensive parks,and historical parks;③The urban parks level showed low impact on sentiment scores.With distinctions observed mainly at level-3 and level-4;④Compared to internal factors in parks,the external infrastructure surround them exerted more significant impact on sentiment scores.For instance,number of bus and subway stations around urban parks led to higher sentiment scores,while scenic spots and restaurants had inverse result.This study provided a novel method to quantify the services of various urban parks,which can be served as inspiration for similar studies in other cities and countries,enhancing their park planning and management strategies.

GatherTweet: A Python Package for Collecting Social Media Data on Online Events

Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Research and Enlightenment of Text Mining Applications in ADR from Social Media

Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Urban sprawl and its impact on sustainable urban development:a combination of remote sensing and social media data

Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.

Security Threat and Data Consumption as Mojor Nuisance of Social Media on Wi-Fi Network

This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.

More than Just a Game: The Power of Social Media on Super Bowl XLVI

The evolution of social media in the recent years promoted the appearance of a new category: social media based on check-in. It enables the user to define their identity through information sharing. This paper aims to show the evolution of these media highlighting the effects and changes they cause in society through Super Bowl XLVI scenario, besides indicating the important role they have for the companies and marketing.

基于社交媒体数据的应急决策大群体群智知识挖掘及其价值测度方法研究

本文针对突发事件下的社交媒体数据体量庞大,但能够辅助应急决策的大群体群智知识分布无序且价值难以测度的问题,提出了一种应急决策大群体群智知识挖掘及其价值测度方法。首先,基于社交媒体博文数据,依据特定应急决策知识框架,采用TF-IDF(term frequency-inverse document trequency)法和改进后的词汇链算法构建了应急决策大群体群智知识挖掘方法;然后,提出了博文热度计算方法,在此基础上根据知识的语义重要性计算博文包含的群智知识总价值,并使用Shapley函数对群智知识价值进行分配;最后,以“新冠疫情”事件为案例对方法进行应用,并通过对比分析说明了方法的有效性和优越性。

新质生产力赋能传统武术现代转型的实践向度

文章运用构建理论框架并分析实际案例,探讨了新质生产力在促进传统武术现代转型中的作用,展示了科技创新如何赋能传统武术文化的传承与发展。在数字化时代背景下,新质生产力,尤其是虚拟现实、大数据、社交媒体等媒介,成为推动传统武术跨越发展的重要力量。研究发现,数字化技术显著提高了教学效率与学习体验,拓宽了文化传播渠道,但也面临着维持传统文化精髓、技术接受度及文化适应性等挑战。为了解决这些问题,文章提出了相应策略,如,深化技术与武术教学的融合,提升人员技术能力,维护文化核心,制定行业标准及加强国际交流。指出,恰当借用新质生产力,结合文化传承与技术创新,可有效促进传统武术的现代化转型与全球化发展。

长江文化的景观形象感知研究——以长江中下游若干滨江历史公园为考察对象

长江滨江岸线景观及历史文化资源丰富,在长江文化保护和传承中占有重要地位。文章以长江中下游4个城市的8个滨江历史公园为研究对象,结合风景资源类型对社交媒体数据中长江文化形象感知情况进行分析。结果显示:游客对长江自然景观形象感知充分,但多停留在认知形象层面,缺少情感形象层面感受;游客对长江文化的多样性感知不足,对其深层次的感知较弱;公园内除了被大家广泛接受的文化,还有许多被忽视的文化价值有待激活。

基于CNN-LSTM的社交媒体大数据评论文本情感元自动识别方法

为了准确识别社交媒体评论文本情感,助力公众负面情绪引导,提出了基于CNN-LSTM的社交媒体大数据评论文本情感元自动识别方法。通过社交媒体大数据分类,并通过具有字典功能的Token将评论文本转换成数字列表。结合词嵌入技术得到向量列表,完成社交媒体大数据向量转换的预处理。将预处理获取的向量列表输入CNN网络,得到评论文本情感元最终局部特征值。将该值传至LSTM,通过遗忘门、输入门、输出门调节,获取评论文本情感元特征表征结果,经Softmax分类器分类后,实现情感元自动识别。实验结果表明,该方法能有效完成实验数据预处理,用文字和标签的形式标记正面、负面情感元,并准确识别情感元,间接反映社会问题,应用性较强。

数据赋能县级融媒体参与基层治理的实践逻辑与优化路径

县级融媒体参与基层治理源于媒体自身的发展需求,也与基层治理实践的现实需要及信息技术发展的时代驱动密切相关。数据对于提升参与治理的效能具有重要价值,通过数据标识汇集媒体资源,通过数据智能实现关系匹配,通过数据预测驱动科学决策,通过数据评估完成全面连接,塑造了数据驱动县级融媒体参与基层治理的现代图景。文章认为,通过提升大数据的全面性与准确性、创设多元主体参与的良性体系、激发大数据活力深度融入治理、推动媒介技术与人类智慧融合共创,以此提升作为县域治理平台的县级融媒体参与基层治理的综合效能。

基于社交媒体数据的多维游憩情绪评价框架研究——以南京中心城区城市公园为例

利用大量带有情绪标签的社交媒体数据,了解和把握居民在城市公园绿地中的游憩情绪感知体验,为城市公园的改造提升提供策略依据。文章基于城市公园使用者发布的新浪微博数据,在Rost Content Mining软件和BERT模型的支持下对微博文本进行分类、分析,运用IPA分析法构建了游憩区位、环境、设施和感知4个维度的情绪评价框架模型,并以位于南京市中心城区的9个城市公园为例进行实证研究。研究发现:(1)居民在城市公园的游憩体验整体上积极正向,游客对于南京中心城区城市公园的总体建设较为满意,其中白鹭洲公园表现最好。(2)从IPA分析来看,重要性高—满意度低象限内的,急需改善的因素分别是玄武湖公园和鼓楼公园的游憩区位,莫愁湖公园和古林公园的游憩环境,绣球公园、鼓楼公园和小桃园的游憩感知。使用文章的评价框架,能有效捕捉游憩人群在城市公园中的情绪状态及其影响因素,这对城市公园的更新与改进具有重要的指导意义。

基于大语言模型和社交媒体数据的城市公园公众活动丰富度测度——以上海为例

【目的】基于社交媒体数据的公园研究已成为热点。然而,既有研究依赖单模态数据和自然语言处理(natural language processing,NLP)技术,研究结果的精确度有待提升。随着大语言模型(large language models,LLM)的发展,分析社交媒体数据可实现更精确的城市公园公众活动丰富度解析。【方法】先利用LLM解析包含文本、图像和视频的多模态社交媒体数据,再运用聚类算法探究用户的情感倾向和活动丰富度,生成活动热力图,构建公园公众活动丰富度的量化方法。【结果】以传统问卷方法为参照标准,对比分析发现基于多模态数据的LLM分析法的准确性远优于单模态数据分析法,证实了研究方法的有效性。并将LLM分析法应用于上海外环内的20个城市公园,构建出大规模、高精度的公园公众活动丰富度的全景测度方法。【结论】创新性地利用LLM和多模态社交媒体数据分析城市公园公众活动丰富度,有利于推动人工智能在城市研究领域的学术发展和应用。

基于社交媒体数据的城市暴雨洪涝灾害风险评估——以郑州市“7·20”暴雨事件为例

近年来强降雨引发的城市洪涝灾害事件趋多,严重危害了人民的生命健康和财产安全,而客观、准确地开展城市暴雨洪涝灾害风险评估对于有效提升防灾减灾水平至关重要。但是,城市灾害点部分基础数据资料的缺失和滞后限制了城市暴雨洪涝灾害风险评估结果的准确性。随着移动互联技术的发展,民众在社交媒体上发布的相关灾害信息逐渐汇集成一种具有海量、时效性强和主题明确等特征的社交媒体数据资源,将其引入城市暴雨洪涝灾害风险评估工作对于准确刻画城市暴雨洪涝灾害的全貌无疑具有显著意义。以2021年郑州市“7·20”暴雨事件为例,首先从气象因素、基础地理信息、社会经济因素三方面选取了13个影响因子,然后基于爬虫技术获取微博数据中的内涝点信息,最后基于GBDT、XGB、RF和AdaB 4种机器学习模型对郑州市“7·20”暴雨洪涝灾害进行风险评估。结果表明:①基于上述模型得到的4组指标权重具有统计意义上的一致性,在各影响因子中,道路密度、植被覆盖指数、半小时最大降雨量和日最大降雨量在4组指标重要性排序中均位列前5,表明上述影响因子是本次暴雨洪涝灾害的主要致灾因素;②基于皮尔逊相关系数检验发现上述4种模型评估结果间的相关程度较高,所得出的极高风险区均集中在郑州市五大主城区的中心部分、中牟市东北部、新密市米村镇及城关镇、巩义市巩义站周边;③上述4种模型的AUC和ACC值均超过0.7,证实了机器学习模型在城市暴雨洪涝灾害风险评估中的有效性;相较于GBDT、XGB和RF模型,AdaB模型的评估结果精度最高,且得到的高风险与极高风险区的Rei值之和最大,表明其评估结果与实际情况相符。本研究通过将社交媒体数据引入城市暴雨洪涝灾害风险评估工作有效地提升了评估结果的准确性,可为郑州市及其他城市在类似强降水事件下的洪涝灾害风险预警和应急处置提供决策依据。

信息流行病学:医院图书馆的新机遇

目的 探讨医院图书馆开展信息流行病学研究的意义和可行性。方法 分析医院图书馆开展信息流行病学研究的背景、意义、优势和掣肘、研究方向和主题。结果 医院图书馆开展信息流行病学研究有着学科背景的优势和资源的便利,且能在网络健康信息评价、网络卫生信息监测、医疗干预措施评估、舆情分析管理研究等多个方面开展研究。结论 医院图书馆开展信息流行病学研究可促进自身转型并助力于医院决策,具有可行性。

Using multi-source data to assess livability in Hong Kong at the community-based level:A combined subjective-objective approach

With the emergence of new types of data(e.g.social media data)and cutting-edge computer technology(e.g.Natural Language Processing),the shortcomings of traditional methods(subjective and objective ways)for de-tecting urban livability can be overcome by an integrated approach.This study aims to develop a comprehensive approach to measure urban livability based on statistic data,geo-data(e.g.points of interest),questionnaires survey,and social media data(Instagram),from both objective and subjective angles.Hong Kong,as a city with a high level of urbanization and contrasting urban environments,is chosen as the study area in this research.Through this study,the question“which area of Hong Kong is more suitable for living”is answered by the visu-alization of GIS-based analysis.Also,the correlation between livability scores and individuals’sentiment scores are explored.Specifically,the results show that central areas of Hong Kong with a higher level of urbanization are relatively more livable than suburban regions.However,through sentiment analysis,individuals who post Instagram in suburban areas of Hong Kong usually express more positive content and happier emotion than those who post Instagram in central urban areas.The study could offer useful information for the policy action of authorities as well as the residential location choices of citizens.

基于社交媒体数据的洪水风险信息提取与应用研究综述

社交媒体数据作为一种时空大数据,具有实时性和位置服务的特点,近年来在国外洪水风险管理中得到广泛应用。目前国内针对该数据类型在洪水风险管理中的研究较少,相关应用受限。通过对国内外基于社交媒体数据的洪涝灾害研究进行综述,探讨洪水风险信息的提取与分析方法,指出社交媒体数据在洪水风险管理中的应用方向,包括洪涝灾害的监测预警、灾情的时空分析、情绪和响应行为分析、救灾部署及灾害损失评估等。此外,社交媒体数据具有实时性、多类型性、可视化的优势,也存在质量与精度有限、虚假信息过多、用户群体受限和文化差异等挑战。如何发挥社交媒体数据的优势,弥补传统监测手段的不足,对于我国城市应急管理部门高效应对洪水风险具有重要意义。