Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims ...Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.展开更多
Background Social media listening is a new approach for gathering insights from social media platforms about users experiences.This approach has not been applied to analyse discussions about Alzheimer's disease(AD...Background Social media listening is a new approach for gathering insights from social media platforms about users experiences.This approach has not been applied to analyse discussions about Alzheimer's disease(AD)in China.Aims We aimed to leverage multisource Chinese data to gain deeper insights into the current state of the daily management of Chinese patients with AD and the burdens faced by their caregivers.Methods We searched ninemainstreampublic onlineplatforms in China fromSeptember2010 to March 2024.Natural language processing tools were used to identify patients and caregivers,and categorise patients by disease stage forfurther analysis.We analysed the current state of patient daily management,including diagnosis and treatment,choice oftreatment scenarios,patient safetyand caregiverconcerns.Results Atotal of 1211patientswithAD(66% female,82% aged 60-90)and 756caregiversfor patients with AD were identified from 107556 online sources.Most patients were derived from online consultation platforms(43%),followed by bulletin board system platforms(24%).Among the patients categorised into specific disease stages(n=382),42% were in the moderate stage.The most frequent diagnostic tools included medical history(97%)and symptoms(84%).Treatment options for patients with AD primarily included cholinesterase inhibitors,N-methyl-D-aspartate receptor antagonists and antipsychotics.Both quantitative and qualitative analysis of patients whoexperiencedwandering(n=92)indicated a higher incidence of wandering during the moderate stage of the disease.Most caregivers were family members,with their primary concerns focusing on disease management and treatment(90%),followed by daily life care(37%)and psychosocial support(25%).Conclusions Online platform data provide a broad spectrum of real-world insights into individuals affected byAD in China.This study enhances our understanding of the experiences of patients with AD and their caregivers,providing guidance for developing personalised interventions,providing advicefor caregivers and improving care for patients with AD.展开更多
Organizations often use sentiment analysis-based systems or even resort to simple manual analysis to try to extract useful meaning from their customers’general digital“chatter”.Driven by the need for a more accurat...Organizations often use sentiment analysis-based systems or even resort to simple manual analysis to try to extract useful meaning from their customers’general digital“chatter”.Driven by the need for a more accurate way to qualitatively extract valuable product and brand-oriented consumer-generated texts,this paper experimentally tests the ability of an NLP-based analytics approach to extract information from highly unstructured texts.The results show that natural language processing outperforms sentiment analysis for detecting issues from social media data.Surprisingly,the experiment shows that sentiment analysis is not only better than manual analysis of social media data for the goal of supporting organizational decision-making,but may also be disadvantageous for such efforts.展开更多
Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling a...Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling among patients with IBD from July 2022 to July 2023.Patients were assessed using the Generalized Social Alienation Scale(GSAS),the Brief Illness Perception Questionnaire(B-IPQ),the Hospital Anxiety and Depression Scale(HADS),and the Medical Coping Modes Questionnaire(MCMQ).Demographic and disease-related characteristics were also collected.Latent profileanalysis(LPA)was used to identify potential subgroups of social alienation.Univariate analysis and multicollinearity analysis were conducted to explore the influencing factors,followed by multiple regression analysis to evaluate the effect of influencingfactors on social alienation.Results:Three distinct profilesof social alienation were identified:integrated-low alienation group(n=61,20.20%),accommodative-moderate alienation group(n=195,64.57%),and maladaptive-high alienation group(n=46,15.23%).Seven characteristics were associated with the profile’smembership:self-perceived financialstress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode.Conclusions:Patients with IBD were categorized into three subgroups based on social alienation levels.Financial stress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode were key predictors of the subgroup membership.Targeted interventions should be developed to mitigate the negative effects of social alienation,with a focus on improving illness perception,alleviating anxiety and depression,and promoting effective coping strategies.展开更多
The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlatio...The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.展开更多
The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related in...The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.展开更多
Objective:To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance,and to examine the demogra...Objective:To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance,and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call.Methods:Surveillance data for 393 suspected Ebola cases(192 males,201 females) were collected from October 23,2014 to June 28,2015 using cellphone technology.UCINET and Net Draw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases.Poisson and logistic regression analyses were used to do multivariable analysis.Results:The entire social network was comprised of 393 ties and 745 nodes.Women(AOR=0.33,95% CI [0.14,0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men.Women(IR= 0.63,95% CI [0.49,0.82]) were also associated with making fewer Ebola surveillance calls compared to men.Conclusion:Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.展开更多
Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory a...Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.展开更多
This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. W...This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.展开更多
In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emoti...In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.展开更多
This paper provides a comprehensive overview of evolution and innovation in social network analysis to the paradigm of social networking. It explains how the development of sociological theory and the structural prope...This paper provides a comprehensive overview of evolution and innovation in social network analysis to the paradigm of social networking. It explains how the development of sociological theory and the structural properties of social groups matter to computer science and communications. Authors such as Moreno, John Barnes and Harrison C. White provide evidence of a growing body of literature addressing the networking of people, organizations and communities to explain the structure of society. This perspective has passed from sociology to other fields, changing understandings of social phenomena. Social networks remain a potent concept for analyzing computer science and communications. This paper shows how and why this has occurred and examines substantive areas in which social network analysis has been applied—mainly how the advantages of graphic visualization and computer software packages have influenced SNA in different audiences and publics leading to the unfolding of social networking to different audiences and publics.展开更多
The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discove...The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.展开更多
Objective:Network analysis was used to explore the complex inter-relationships between social participation activities and depressive symptoms among the Chinese older population,and the differences in network structur...Objective:Network analysis was used to explore the complex inter-relationships between social participation activities and depressive symptoms among the Chinese older population,and the differences in network structures among different genders,age groups,and urban-rural residency would be compared.Methods:Based on the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey(CLHLS),12,043 people aged 65 to 105 were included.The 10-item Center for Epidemiologic Studies Depression(CESD)Scale was used to assess depressive symptoms and 10 types of social participation activities were collected,including housework,tai-chi,square dancing,visiting and interacting with friends,garden work,reading newspapers or books,raising domestic animals,playing cards or mahjong,watching TV or listening to radio,and organized social activities.R 4.2.1 software was used to estimate the network model and calculate strength and bridge strength.Results:21.60%(2,601/12,043)of the participants had depressive symptoms.The total social participation score was negatively associated with depressive symptoms after adjusting for sociodemographic factors.The network of social participation and depressive symptoms showed that“D9(Inability to get going)”and“S9(Watching TV and/or listening to the radio)”had the highest strength within depressive symptoms and social participation communities,respectively,and“S1(Housework)”,“S9(Watching TV and/or listening to the radio)”,and“D5(Hopelessness)”were the most prominent bridging nodes between the two communities.Most edges linking the two communities were negative.“S5(Graden work)-D5(Hopelessness)”and“S6(Reading newspapers/books)-D4(Everything was an effort)”were the top 2 strongest negative edges.Older females had significantly denser network structures than older males.Compared to older people aged 65e80,the age group 81e105 showed higher network global strength.Conclusions:This study provides novel insights into the complex relationships between social participation and depressive symptoms.Except for doing housework,other social participation activities were found to be protective for depression levels.Different nursing strategies should be taken to prevent and alleviate depressive symptoms for different genders and older people of different ages.展开更多
The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for...The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-fier trained with this new dataset was more than 80%.展开更多
In order to explore the relationship between the corporate social responsibility (CSR) and financial performance in different CSR cultures, this paper respectively takes four representative smartphone companies in Chi...In order to explore the relationship between the corporate social responsibility (CSR) and financial performance in different CSR cultures, this paper respectively takes four representative smartphone companies in China, the United States, Japan and Korea as research objects, that is Huawei, Apple, Sony and Samsung smartphone companies. Then, dividing the CSR into five dimensions i.e. shareholder, customer, supplier, technology innovation and government and establishing the grey correlation model to empirically analyze the relationship between CSR and financial performance. The results indicate that for HUAWEI, the CSR performance in shareholder, customer and technology innovation have greater impact on its financial performance;for Apple, the CSR performance in shareholder, technology innovation and customer has greater impact on its financial performance;for SONY, the CSR performance in technology innovation, customer and shareholder has greater impact on its financial performance;for Samsung, the CSR performance in technology innovation, government and shareholder has greater impact on its financial performance.展开更多
In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these meas...In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.展开更多
Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-att...Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.展开更多
Social influence analysis （SIA） is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and eva...Social influence analysis （SIA） is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and eval- uation aspects related to SIA. We divide SIA models into two types： microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.展开更多
The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on pr...The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.展开更多
Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods...Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.72364006).
文摘Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.
基金funded by the Ministry of Science and Technology of the People's Republic of China(2021ZD0201804,GW).
文摘Background Social media listening is a new approach for gathering insights from social media platforms about users experiences.This approach has not been applied to analyse discussions about Alzheimer's disease(AD)in China.Aims We aimed to leverage multisource Chinese data to gain deeper insights into the current state of the daily management of Chinese patients with AD and the burdens faced by their caregivers.Methods We searched ninemainstreampublic onlineplatforms in China fromSeptember2010 to March 2024.Natural language processing tools were used to identify patients and caregivers,and categorise patients by disease stage forfurther analysis.We analysed the current state of patient daily management,including diagnosis and treatment,choice oftreatment scenarios,patient safetyand caregiverconcerns.Results Atotal of 1211patientswithAD(66% female,82% aged 60-90)and 756caregiversfor patients with AD were identified from 107556 online sources.Most patients were derived from online consultation platforms(43%),followed by bulletin board system platforms(24%).Among the patients categorised into specific disease stages(n=382),42% were in the moderate stage.The most frequent diagnostic tools included medical history(97%)and symptoms(84%).Treatment options for patients with AD primarily included cholinesterase inhibitors,N-methyl-D-aspartate receptor antagonists and antipsychotics.Both quantitative and qualitative analysis of patients whoexperiencedwandering(n=92)indicated a higher incidence of wandering during the moderate stage of the disease.Most caregivers were family members,with their primary concerns focusing on disease management and treatment(90%),followed by daily life care(37%)and psychosocial support(25%).Conclusions Online platform data provide a broad spectrum of real-world insights into individuals affected byAD in China.This study enhances our understanding of the experiences of patients with AD and their caregivers,providing guidance for developing personalised interventions,providing advicefor caregivers and improving care for patients with AD.
文摘Organizations often use sentiment analysis-based systems or even resort to simple manual analysis to try to extract useful meaning from their customers’general digital“chatter”.Driven by the need for a more accurate way to qualitatively extract valuable product and brand-oriented consumer-generated texts,this paper experimentally tests the ability of an NLP-based analytics approach to extract information from highly unstructured texts.The results show that natural language processing outperforms sentiment analysis for detecting issues from social media data.Surprisingly,the experiment shows that sentiment analysis is not only better than manual analysis of social media data for the goal of supporting organizational decision-making,but may also be disadvantageous for such efforts.
基金supported by the“333 High-Level Talents Training Project”of Jiangsu province(No.BRA2020069)。
文摘Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling among patients with IBD from July 2022 to July 2023.Patients were assessed using the Generalized Social Alienation Scale(GSAS),the Brief Illness Perception Questionnaire(B-IPQ),the Hospital Anxiety and Depression Scale(HADS),and the Medical Coping Modes Questionnaire(MCMQ).Demographic and disease-related characteristics were also collected.Latent profileanalysis(LPA)was used to identify potential subgroups of social alienation.Univariate analysis and multicollinearity analysis were conducted to explore the influencing factors,followed by multiple regression analysis to evaluate the effect of influencingfactors on social alienation.Results:Three distinct profilesof social alienation were identified:integrated-low alienation group(n=61,20.20%),accommodative-moderate alienation group(n=195,64.57%),and maladaptive-high alienation group(n=46,15.23%).Seven characteristics were associated with the profile’smembership:self-perceived financialstress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode.Conclusions:Patients with IBD were categorized into three subgroups based on social alienation levels.Financial stress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode were key predictors of the subgroup membership.Targeted interventions should be developed to mitigate the negative effects of social alienation,with a focus on improving illness perception,alleviating anxiety and depression,and promoting effective coping strategies.
文摘The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.
基金sponsored by the National Natural Science Foundation of China under grant number No.61100008,61201084the China Postdoctoral Science Foundation under Grant No.2013M541346+3 种基金Heilongiiang Postdoctoral Special Fund(Postdoctoral Youth Talent Program)under Grant No.LBH-TZ0504Heilongjiang Postdoctoral Fund under Grant No.LBH-Z13058the Natural Science Foundation of Heilongjiang Province of China under Grant No.QC2015076The Fundamental Research Funds for the Central Universities of China under grant number HEUCF100602
文摘The e-mail network is a type of social network. This study analyzes user behavior in e-mail subject participation in organizations by using social network analysis. First, the Enron dataset and the position-related information of an employee are introduced, and methods for deletion of false data are presented. Next, the three-layer model(User, Subject, Keyword) is proposed for analysis of user behavior. Then, the proposed keyword selection algorithm based on a greedy approach, and the influence and propagation of an e-mail subject are defined. Finally, the e-mail user behavior is analyzed for the Enron organization. This study has considerable significance in subject recommendation and character recognition.
文摘Objective:To explore and visualize the connectivity of suspected Ebola cases and surveillance callers who used cellphone technology in Moyamba District in Sierra Leone for Ebola surveillance,and to examine the demographic differences and characteristics of Ebola surveillance callers who make more calls as well as those callers who are more likely to make at least one positive Ebola call.Methods:Surveillance data for 393 suspected Ebola cases(192 males,201 females) were collected from October 23,2014 to June 28,2015 using cellphone technology.UCINET and Net Draw software were used to explore and visualize the social connectivity between callers and suspected Ebola cases.Poisson and logistic regression analyses were used to do multivariable analysis.Results:The entire social network was comprised of 393 ties and 745 nodes.Women(AOR=0.33,95% CI [0.14,0.81]) were associated with decreased odds of making at least one positive Ebola surveillance call compared to men.Women(IR= 0.63,95% CI [0.49,0.82]) were also associated with making fewer Ebola surveillance calls compared to men.Conclusion:Social network visualization can analyze syndromic surveillance data for Ebola collected by cellphone technology with unique insights.
基金This paper is supported by the National Natural Science Foundation of China under contract No.71774084,72274096the National Social Science Fund of China under contract No.16ZDA224,17ZDA291.
文摘Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.
文摘This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.
文摘In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.
文摘This paper provides a comprehensive overview of evolution and innovation in social network analysis to the paradigm of social networking. It explains how the development of sociological theory and the structural properties of social groups matter to computer science and communications. Authors such as Moreno, John Barnes and Harrison C. White provide evidence of a growing body of literature addressing the networking of people, organizations and communities to explain the structure of society. This perspective has passed from sociology to other fields, changing understandings of social phenomena. Social networks remain a potent concept for analyzing computer science and communications. This paper shows how and why this has occurred and examines substantive areas in which social network analysis has been applied—mainly how the advantages of graphic visualization and computer software packages have influenced SNA in different audiences and publics leading to the unfolding of social networking to different audiences and publics.
文摘The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.
基金supported by the National Key Research and Development Plan Project(grant number:2022YFC3600904)The funding organization had no role in the survey’s design,implementation,and analysis.
文摘Objective:Network analysis was used to explore the complex inter-relationships between social participation activities and depressive symptoms among the Chinese older population,and the differences in network structures among different genders,age groups,and urban-rural residency would be compared.Methods:Based on the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey(CLHLS),12,043 people aged 65 to 105 were included.The 10-item Center for Epidemiologic Studies Depression(CESD)Scale was used to assess depressive symptoms and 10 types of social participation activities were collected,including housework,tai-chi,square dancing,visiting and interacting with friends,garden work,reading newspapers or books,raising domestic animals,playing cards or mahjong,watching TV or listening to radio,and organized social activities.R 4.2.1 software was used to estimate the network model and calculate strength and bridge strength.Results:21.60%(2,601/12,043)of the participants had depressive symptoms.The total social participation score was negatively associated with depressive symptoms after adjusting for sociodemographic factors.The network of social participation and depressive symptoms showed that“D9(Inability to get going)”and“S9(Watching TV and/or listening to the radio)”had the highest strength within depressive symptoms and social participation communities,respectively,and“S1(Housework)”,“S9(Watching TV and/or listening to the radio)”,and“D5(Hopelessness)”were the most prominent bridging nodes between the two communities.Most edges linking the two communities were negative.“S5(Graden work)-D5(Hopelessness)”and“S6(Reading newspapers/books)-D4(Everything was an effort)”were the top 2 strongest negative edges.Older females had significantly denser network structures than older males.Compared to older people aged 65e80,the age group 81e105 showed higher network global strength.Conclusions:This study provides novel insights into the complex relationships between social participation and depressive symptoms.Except for doing housework,other social participation activities were found to be protective for depression levels.Different nursing strategies should be taken to prevent and alleviate depressive symptoms for different genders and older people of different ages.
文摘The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result,social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed proces-sing are two options for dealing with such large amounts of data.Most parallel programming frameworks,such as MPI(Message Processing Interface),are dif-ficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model,this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose;unlike prior research,this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework’s scalabil-ity,we gathered a large training corpus from Twitter.The accuracy of the classi-fier trained with this new dataset was more than 80%.
文摘In order to explore the relationship between the corporate social responsibility (CSR) and financial performance in different CSR cultures, this paper respectively takes four representative smartphone companies in China, the United States, Japan and Korea as research objects, that is Huawei, Apple, Sony and Samsung smartphone companies. Then, dividing the CSR into five dimensions i.e. shareholder, customer, supplier, technology innovation and government and establishing the grey correlation model to empirically analyze the relationship between CSR and financial performance. The results indicate that for HUAWEI, the CSR performance in shareholder, customer and technology innovation have greater impact on its financial performance;for Apple, the CSR performance in shareholder, technology innovation and customer has greater impact on its financial performance;for SONY, the CSR performance in technology innovation, customer and shareholder has greater impact on its financial performance;for Samsung, the CSR performance in technology innovation, government and shareholder has greater impact on its financial performance.
文摘In a social network analysis the output provided includes many measures and metrics. For each of these measures and metric, the output provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning disrupting or deceiving a given network. All is fine when all the measures indicate the same node as the key or influential node. What happens when the measures indicate different key nodes？ Our goal in this paper is to explore two methodologies to identify the key players or nodes in a given network. We apply TOPSIS to analyze these outputs to find the most influential nodes as a function of the decision makers＇ inputs as a process to consider both subjective and objectives inputs through pairwise comparison matrices. We illustrate our results using two common networks from the literature： the Kite network and the Information flow network from Knoke and Wood. We discuss some basic sensitivity analysis can may be applied to the methods. We find the use of TOPSIS as a flexible method to weight the criterion based upon the decision makers＇ inputs or the topology of the network.
文摘Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.
文摘Social influence analysis （SIA） is a vast research field that has attracted research interest in many areas. In this paper, we present a survey of representative and state-of-the-art work in models, methods, and eval- uation aspects related to SIA. We divide SIA models into two types： microscopic and macroscopic models. Microscopic models consider human interactions and the structure of the influence process, whereas macroscopic models consider the same transmission probability and identical influential power for all users. We analyze social influence methods including influence maximization, influence minimization, flow of influence, and individual influence. In social influence evaluation, influence evaluation metrics are introduced and social influence evaluation models are then analyzed. The objectives of this paper are to provide a comprehensive analysis, aid in understanding social behaviors, provide a theoretical basis for influencing public opinion, and unveil future research directions and potential applications.
基金We thank the anonymous reviewers and editors for their very constructive comments.the National Social Science Foundation Project of China under Grant 16BTQ085.
文摘The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.
基金supported by Sun Yat-sen University Cultivation Fund for Young Teachers(Grant No.:20000-3161102)the National Social Science Fundation of China(Grant No.:08CTQ015)
文摘Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.
