Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder h...Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.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 study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the ...This study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the platform trade more frequently under the influence of the comments posted by their leaders(the owners of portfolios they have followed).Moreover,portfolio owners are more sensitive to the quantity than to the tone of leaders’comments.Finally,both trading frequency and leaders’comments negatively impact portfolio owners’future performance.Our find-ings support the notion that social interaction promotes active investment strategies.展开更多
The cyber development of government service platform is just to integrate all the superiorities of the government to strengthen its roles such as service,performance and function to drive the social development active...The cyber development of government service platform is just to integrate all the superiorities of the government to strengthen its roles such as service,performance and function to drive the social development actively.Hence,the social public administration will take this opportunity to optimize the cyber information service platform to make the civic administration oriented to the social development to maximize its functions of organization,coordination and service to protrude the ascendancy of the web era so as to lay a solid foundation for the leap development of the public administration.展开更多
Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physica...Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.展开更多
社交商务作为数字经济的重要组成,拥有巨大的市场潜力,但也面临着平台发展模式同质化等问题,导致形成固化用户的自我认识和网络信息行为的信息茧房。由于社交媒体的多样性、群体关系复杂性,茧房效应与社交商务结合的研究有待进一步挖掘...社交商务作为数字经济的重要组成,拥有巨大的市场潜力,但也面临着平台发展模式同质化等问题,导致形成固化用户的自我认识和网络信息行为的信息茧房。由于社交媒体的多样性、群体关系复杂性,茧房效应与社交商务结合的研究有待进一步挖掘。通过中国知网和Web of Science核心集,截至2023年3月31日,检索并筛选获取606篇相关文献,运用社会网络分析方法进行分析。其中300篇中文文献重点探讨个性化推荐算法对信息茧房形成的影响,306篇英文文献更多关注信息茧房与平台和用户行为之间的关系。基于信息茧房相关概念的研究明确其内涵和特征,根据平台、算法和用户3个维度的作用机制分析构建信息同质化视角下信息茧房形成机制框架,即社交平台内外部信息趋同导致了用户选择同质化、平台内容同质化以及用户群体同质化问题,并以正反馈的形式不断加强,最终形成信息茧房。由此,基于同质化视角,分别从政府、平台和用户3个主体层面提出社交商务平台应对信息茧房的策略。展开更多
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant coder NU/RC/SERC/11/5.
文摘Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.
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.
基金National Natural Science Foundation of China(Grant No.7167030951).
文摘This study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the platform trade more frequently under the influence of the comments posted by their leaders(the owners of portfolios they have followed).Moreover,portfolio owners are more sensitive to the quantity than to the tone of leaders’comments.Finally,both trading frequency and leaders’comments negatively impact portfolio owners’future performance.Our find-ings support the notion that social interaction promotes active investment strategies.
文摘The cyber development of government service platform is just to integrate all the superiorities of the government to strengthen its roles such as service,performance and function to drive the social development actively.Hence,the social public administration will take this opportunity to optimize the cyber information service platform to make the civic administration oriented to the social development to maximize its functions of organization,coordination and service to protrude the ascendancy of the web era so as to lay a solid foundation for the leap development of the public administration.
文摘Two artificial agents(a humanoid robot and a virtual human) are enriched with various similar intelligence,autonomy, functionalities and interaction modalities. The agents are integrated in the form of a cyber-physical-social system(CPSS) through a shared communication platform to create a social ecology. In the ecology, the agents collaborate(assist each other) to perform a real-world task(search for a hidden object)for the benefits of humans. A robot-virtual human bilateral trust model is derived and a real-time trust measurement method is developed. The role of taking initiative in the collaboration is switched between the agents following a finite state machine model triggered by bilateral trust, which results in a mixedinitiative collaboration. A scheme is developed to evaluate the performance of the agents in the ecology through the CPSS.The results show that the robot and the virtual human perform satisfactorily in the collaboration through the CPSS. The results thus prove the effectiveness of the real-world ecology between artificial agents of heterogeneous realities through a shared platform based on trust-triggered mixed-initiatives. The results can help develop adaptive social ecology comprising intelligent agents of heterogeneous realities to assist humans in various tasks through collaboration between the agents in the form of a CPSS.
文摘社交商务作为数字经济的重要组成,拥有巨大的市场潜力,但也面临着平台发展模式同质化等问题,导致形成固化用户的自我认识和网络信息行为的信息茧房。由于社交媒体的多样性、群体关系复杂性,茧房效应与社交商务结合的研究有待进一步挖掘。通过中国知网和Web of Science核心集,截至2023年3月31日,检索并筛选获取606篇相关文献,运用社会网络分析方法进行分析。其中300篇中文文献重点探讨个性化推荐算法对信息茧房形成的影响,306篇英文文献更多关注信息茧房与平台和用户行为之间的关系。基于信息茧房相关概念的研究明确其内涵和特征,根据平台、算法和用户3个维度的作用机制分析构建信息同质化视角下信息茧房形成机制框架,即社交平台内外部信息趋同导致了用户选择同质化、平台内容同质化以及用户群体同质化问题,并以正反馈的形式不断加强,最终形成信息茧房。由此,基于同质化视角,分别从政府、平台和用户3个主体层面提出社交商务平台应对信息茧房的策略。
