Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Sport Participation and Community Capacity Building in Disadvantaged Urban Communities

The aim of this study was to investigate the relationships between sport and community by examining the role played by sports participation in community engagement and community capacity building in disadvantaged urban communities.From a conceptual standpoint,the study is based on the sociological theory of the community and Community Capacity Building(CCB)theory,which provide overviews of the concept of community and community capacity and its characteristics.Four disadvantaged urban communities were investigated.Two of these communities had sports programs,while the other two did not.The online questionnaire methodology was adopted to assess the socio-demographic characteristics and community engagement of the participants(knowledge of programs and participation in community activities),and subsequently,an analysis of the online social media narratives of the community members themselves was developed to measure the characteristics of their community capacity building.We found that community engagement and community capacity values were higher in the communities with sports programs.These findings are promising and suggest that approaches based on sports participation development could be fruitful,particularly in disadvantaged communities.

VisQAC: Visual Analytics for Online Q&A Communities

Online question and answer(Q&A)communities,which allow users to exchange knowledge by asking and answering questions,have become increasingly popular.As a result of user active participation,these communities store overwhelming volumes of information.However,existing related methods are unable to meet community operators’needs for analyzing multi-dimensional Q&A sequences and understanding user behavior.In this paper,collaborating with domain experts in online community,we present a system,VisQAC,which explores the patterns of Q&A sequence and user behavior.In the system,a novel visual design is proposed,which is combined with flexible mapping measures for analyzing critical characteristics of sequence data.Moreover,a timeline visualization method is designed to visualize data with categorical attributes and its correlation can be displayed flexibly by choosing time mode and time granularity.The usefulness and effectiveness of the system are demonstrated with several case studies of VisQAC with community operators based on the Zhihu dataset.Our evaluation shows that VisQAC is beneficial to the understanding of Q&A sequence and associated user behavior.

Comparison of Online Distribution Channels of the Thailand Community Enterprise Products

This research aims to study an online distribution of community enterprise products,to design the management system for an e-commerce distribution channel,and to compare the efficiency of distribution channels through a developed e-commerce system and ready web.A personal interview survey method was used to collect data on the production,marketing,and online selling issues.In total 150 community enterprise entrepreneurs in five product categories(food,appliance/home decoration,beverage,garment/ornament and non-food herb)and 50 consumers were equally selected by judgment sampling method.The e-commerce channel that was developed is our website and social network website that was selected to compare the efficiency of distribution channels is Facebook.The research found that only 14.67%of community enterprise entrepreneurs sell products through online channel.The product which was sold through the developed website and maximized gross profit is food(35.53%of total revenue).The others,are beverage,non-food herb,appliance/home decoration,and garment/ornament,earn gross profit 31.50%,22.39%,9.78%and 0.80%of total revenue,respectively.The result of distribution channels comparison between our website and webpages on Facebook found that Facebook has the ability to reach customers 2,811 times more than the website 64.75%,which has only 991 times.Regarding to sales,it is found that revenue from Facebook is higher than the website in all product categories.

Factors That Influence Healthcare Professionals’ Online Interaction in a Virtual Community of Practice

Online technologies have facilitated the development of Virtual Communities of Practice (virtual CoPs) to support health professionals collaborate online to share knowledge, improve performance and support the spread of innovation and best practices. Research, however,shows that many virtual CoPs do not achieve their expected potential because online interaction among healthcare professionals is generally low. Focusing on health visitors, who are UK qualified midwives or nurses who have undertaken additional qualifications as specialist public health workers in the community, the paper examines the factors that influence online interaction among health visitors collaborating to share knowledge and experience in a virtual CoP. The paper makes suggestions for how to improve online interaction among health professionals in virtual CoPs by increasing the size of membership in order to take advantage of both posting and viewing contributions, facilitating moderation to improve networking among geographically dispersed members groups and im-proving the topic relevance in order to stimulate contributions.

Self-Directed Creativity in Web 2.0-Based Online Communities： Research Model and Exploratory Study

Web 2.0-based online communities and social networking platforms in particular enable users to create their own content, share this content with anyone they invite and organize connections with existing or new online contacts. The underlying processes are self-directed and represent a valuable source for creativity and innovation---especially outside firms＇ boundaries. The basis for the authors＇ research in progress is a framework which focuses on the relations between intrinsic motivation, creativity and Web 2.0-based online communities or social networking platforms. First results of the authors＇ exploratory empirical investigation of a specific social networking platform suggest that the authors＇ two propositions are valid.

A Comparative Study on the Effects of Online Synchronous Instruction in the Framework of Community of Inquiry

The objective of this case study is to compare the satisfaction of online educational experience with traditional one from the perspective of Community of Inquiry theory.It compares the teaching effects of this online instruction with traditional class through cognitive presence,social presence and teaching presence which are 3 influencers in deciding the quality of distance learning and teaching.A survey to 40 undergraduates and 45 graduates and 28 teachers were conducted for this purpose.The key finding of the research is that the online teaching model is comparatively functional and efficient for both students and teachers comparing with traditional classes.Cognitive presence is more prominent among 2 student cohort and social presence varies among undergraduates and graduates.The teacher’s response in the three presences are a bit more pessimistic than that of two groups of students.This result would bring some implications for future online synchronous learning and teaching and blended instruction.

Innovation in Adult Language Learning Programs: A Case Study of "Livemocha" Online Language Learning Community

Due to the increasing broadband Internet access, VOIP adoption as well as the Web 2.0 concepts, online language learning communities have gained great popularity among adult language learners. Based upon Malcolm Knowles' adult learning theory, this paper analyzes the innovation that online language learning communities have brought to adult language learning programs. At the same time, the limitations of such programs have been pointed out.

A comparative analysis of major Chinese and English online question-answering communities

This paper compares 12 representative Chinese and English online questionanswering communities(Q&A communities) based on their basic functions, interactive modes, and customized services. An empirical experiment from a comparative perspective was also conducted on them by using 12 questions representing for four types of questions,which are assigned evenly to three different subject fields so as to examine the task performance of these 12 selected online Q&A communities. Our goal was to evaluate those online Q&A communities in terms of their quality and efficiency for answering questions posed to them. It was hoped that our empirical research would yield greater understanding and insights to the working intricacy of these online Q&A communities and hence their possible further improvement.

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

Identification of ChatGPT answers and physician answers in the online medical community

ChatGPT changes the way of knowledge production and information space structure of human society.In the healthcare industry,ChatGPT's powerful question-and-answer capability will drive its application in automated question answering in online healthcare communities.However,because ChatGPT answers are limited by factors such as the quality of data sets,their authority and accuracy cannot be guaranteed,and they are prone to misdiagnosis and damage to life and health.Therefore,the identification of ChatGPT answers in online medical communities with physician answers is crucial.In this paper,we collected medical question-answering data generated by the Haodafu platform and ChatGPT,respectively,constructed feature vectors from semantic features,syntactic features,and the fusion of both,and combined different feature vectors with XGBoost models to construct BERT-XGBoost,POS-XGBoost and Merge-XGBoost models for identifying ChatGPT answers and physician answers in online medical communities.The three models achieved accuracy rates of 0.960,0.968,and 0.986,respectively.The difference in performance between the three models reflects the degrees of variation in different features of ChatGPT answers versus physician answers.The results indicate that the differences between ChatGPT and physicians in syntactic features,i.e.,linguistic expression habits,are greater than their differences in semantic features,i.e.,specific content suggestions.

Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.

ComRank： Joint Weight Technique for the Identification of Influential Communities

Recently, the community analysis has seen enormous research advancements in the field of social networks. A large amount of the current studies put forward different models and algorithms about most influential people. However, there is little work to shed light on how to rank communities while considering their levels that are determined by the quality of their published contents. In this paper, we propose solution for measuring the influence of communities and ranking them by considering joint weight composed of internal and external influence of communities. To address this issue, we design a novel algorithm called Com Rank: a modification of Page Rank, which considers the joint weight in order to identify impact of each community and ranking them. We use real-world data trace in citation network and perform extensive experiments to evaluate our proposed algorithm. The comparative results depict significant improvements by our algorithm in community ranking due to the inclusion of proposed weighting feature.

Antecedents of Microblogging Users’Purchase Intention Toward Celebrities’Merchandise:Perspectives of Virtual Community and Fan Economy

Over the last few decades,cross-fertilization between marketing and fandom studies in a mediated world has been rare,hindering the knowledge development for marketing practitioners in the Chinese fan economy context.The purpose of this study is to find and establish a conceptual framework that includes Online Interaction(OI),Parasocial Relationships(PSR)and virtual fan communities on Weibo,and how these contributory factors embedded in the interplay process of digital fandom practices in terms of enhancing fans’Purchase Intention(PI)and media consumption behaviours toward the celebrities’merchandise.Using an online survey instrument,this research collected 294 completed responses from fans who had online interactions with celebrities and engagement of virtual fan communities on Weibo.Key results and findings provided a clear framework of four antecedents based on the conceptual model and indicated that the high intensity of OI led to higher levels of the perception of PSR and the Sense of Virtual Community(SOVC).Increased PSR and the SOVC can be seen as significant positive predictors of the PI(a part of consumer identity construction as a fan)also.This study revealed the underlying mechanism of an emerging marketing genre,also provide useful implications of audiences’digital marketing practices for marketers,celebrities and policymakers.

Evaluating Group Formation in Virtual Communities

In this paper, we are interested in answering the following research question: "Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities?"In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community(called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.

TOWARD COLLECTIVE INTELLIGENCE OF ONLINE COMMUNITIES:A PRIMITIVE CONCEPTUAL MODEL

Inspired by the ideas of Swarm Intelligence and the ＂global brain＂, a concept of ＂community intelligence＂ is suggested in the present paper, reflecting that some ＂intelligent＂ features may emerge in a Web-mediated online community from interactions and knowledge-transmissions between the community members. This possible research field of community intelligence is then examined under the backgrounds of ＂community＂ and ＂intelligence＂ researches. Furthermore, a conceptual model of community intelligence is developed from two views. From the structural view, the community intelligent system is modeled as a knowledge supernetwork that is comprised of triple interwoven networks of the media network, the human network, and the knowledge network. Furthermore, based on a dyad of knowledge in two forms of ＂knowing＂ and ＂knoware＂, the dynamic view describes the basic mechanics of the formation and evolution of ＂community intelligence＂. A few relevant research issues are shortly discussed on the basis of the proposed conceptual model.

Online Healthcare Community Interaction Dynamics

With the increasing dilemma of the rapid global demand for healthcare services(often in the presence of limited resources),how to creatively allocate and use healthcare resources across a widespread population has become a salient issue.Online healthcare communities(OHCs)are regarded as a potential ICT-based partial solution.In contrast to traditional healthcare,online doctor-patient interaction is unlimited in terms of time and space limitations while an OHC is exposed to the whole community.These characteristics are key to achieving synergistic doctor-patient interaction and community development in the longer term.In order to explore the nature of doctor-patient interaction dynamics in an OHC,we systematically investigate doctor-patient interaction dynamics from the dual perspectives of doctor and patient.Our doctor-patient interaction dual-cycle model has been built based on six doctor-patient interaction processes(i.e.,searching,choosing,knowledge sharing,providing,receiving and balancing).According to our dual-cycle model,four key managerial issues in OHC(information asymmetry,incentive mechanisms,service delivery processes and interaction mechanisms)have been identified as examples.Discussion and directions for future research,with challenges as well as opportunities,have been elaborated.A broad view with fruitful research potential is ensured and new theories and methods ultimately provide implications for effectively and efficiently allocating scarce healthcare resources to a broader population.

How Social Network Position Relates to Knowledge Building in Online Learning Communities?

Social Network Analysis,Statistical Analysis,Content Analysis and other research methods were used to research online learning communities at Capital Normal University,Beijing.Analysis of the two online courses resulted in the following conclusions:(1)Social networks of the two online courses form typical core-periphery structures;(2)Social networks of the two online courses contain“structural holes,”where some actors position themselves to become potential opinion-leaders within their social networks;(3)Actors,variously positioned within a core-periphery structure,show quite significant differences in terms of knowledge building;(4)Taking“structural holes”into account,there exist considerable differences in knowledge building between opinion-leaders and non opinion-leaders;(5)Actors in the“core”and“structural hole”positions have very different characteristics in terms of knowledge building.These actors in particular play important roles in online learning communities,impacting on the level of the constructed knowledge.

The Supernetwork Model of Social Networking Services

The unique ways of information organization and dissemination was examined through the microblog and the real-name social network as the representatives of the new virtual social networks. In order to discuss the interrelation and interaction of the two dimensions-topic and user, a supernetwork model was established based on the supernetwork research method. Through the actual data, a supernetwork topology diagram and the changing rule of user participation were attained. And it was concluded that the key factor of dealing with emergent online public sentiment should start with affecting the opinions of key figures, whose opinions would further affect the public opinions.

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Friend recommendation plays a key role in promoting user experience in online social networks(OSNs).However,existing studies usually neglect users’fine-grained interest as well as the evolving feature of interest,which may cause unsuitable recommendation.In particular,some OSNs,such as the online learning community,even have little work on friend recommendation.To this end,we strive to improve friend recommendation with fine-grained evolving interest in this paper.We take the online learning community as an application scenario,which is a special type of OSNs for people to learn courses online.Learning partners can help improve learners’learning effect and improve the attractiveness of platforms.We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest(LPRF-E for short).We extract a sequence of learning interest tags that changes over time.Then,we explore the time feature to predict evolving learning interest.Next,we recommend learning partners by fine-grained interest similarity.We also refine the learning partner recommendation framework with users’social influence(denoted as LPRF-F for differentiation).Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy(i.e.,approximate 50%improvements on the precision and the recall)and can recommend learning partners with high quality(e.g.,more experienced and helpful).