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International Journal of Web Based Communities最新文献

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Exploring the impact of COVID-19 Pandemic and Vaccine Dissemination on Airbnb's Popularity and Sentiment on Twitter 探索COVID-19大流行和疫苗传播对Airbnb在Twitter上的人气和情绪的影响
Q2 Social Sciences Pub Date : 2024-01-01 DOI: 10.1504/ijwbc.2024.10056991
C. Evert, Weiming Liang, Vahid Ghomi, Amirsalar Jafari Gorizi, Sina Shokoohyar
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引用次数: 0
An evolution trend evaluation of social media network public opinion based on unsupervised learning 基于无监督学习的社交媒体网络舆情演变趋势评价
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10051913
Yanhua Shen
{"title":"An evolution trend evaluation of social media network public opinion based on unsupervised learning","authors":"Yanhua Shen","doi":"10.1504/ijwbc.2023.10051913","DOIUrl":"https://doi.org/10.1504/ijwbc.2023.10051913","url":null,"abstract":"","PeriodicalId":39041,"journal":{"name":"International Journal of Web Based Communities","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66704114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
YouTube and the Production of Online Video Cultures in Rural South India 南印度农村的YouTube和在线视频文化的制作
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10060125
Prabha Shankar Dwivedi, Vamshi Krishna Reddy Vemireddy, Srikanth Nayaka
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引用次数: 0
Prediction method of consumer repeat purchase behavior in e-commerce environment 电子商务环境下消费者重复购买行为预测方法
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10055931
Miao Sun
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引用次数: 0
Decision making method of e-consumption behavior and attitude based on social network trust model 基于社会网络信任模型的电子消费行为与态度决策方法
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10048830
L. Qian
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引用次数: 0
Study on Network marketing service resource allocation based on social media platform 基于社会化媒体平台的网络营销服务资源配置研究
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10048831
Miao Wei
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引用次数: 0
An online social network image retrieval using deep belief network 基于深度信念网络的在线社交网络图像检索
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.10049535
Hongzheng Dong, Chao Guo
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引用次数: 0
A rough set-based consumer buying behaviour prediction method in online marketing system 基于粗糙集的网络营销系统中消费者购买行为预测方法
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.128406
Dian Jia
Aiming at the problems of large prediction deviation and low acquisition accuracy of consumer purchase behaviour in traditional online marketing systems, a rough set-based consumer purchase behaviour prediction method in online marketing system is proposed. By improving the accuracy and recall rate of online consumer buying behaviour prediction methods, the deviation of prediction results is reduced. The data of consumer purchase behaviour in the region related to rough set are reduced to improve the accuracy and recall rate, and the forecast bias is reduced by removing redundant features in the e-marketing system. With the rough set theory, the dimension of consumer behaviour vector is reduced, and a predictive model framework is built. The simulation results show that the accuracy and recall rate of this proposed method are higher than 95%, and the minimum deviation of the prediction result is only 8.12%, which proves that the prediction result is more reliable.
针对传统网络营销系统对消费者购买行为预测偏差大、获取精度低的问题,提出了一种基于粗糙集的网络营销系统消费者购买行为预测方法。通过提高在线消费者购买行为预测方法的准确率和召回率,减少预测结果的偏差。通过对粗糙集相关区域的消费者购买行为数据进行简化,提高预测准确率和召回率,并通过去除网络营销系统中的冗余特征来减少预测偏差。利用粗糙集理论对消费者行为向量进行降维,构建预测模型框架。仿真结果表明,该方法的准确率和召回率均高于95%,预测结果的最小偏差仅为8.12%,证明了该方法的预测结果更加可靠。
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引用次数: 1
Foundations of consumer engagement with social media influencers 消费者与社交媒体影响者互动的基础
Q2 Social Sciences Pub Date : 2023-01-01 DOI: 10.1504/ijwbc.2023.131410
Tatyana Bastrygina, Weng Marc Lim
This article is a systematic review of recent and relevant journal articles on consumer engagement with social media influencers using the PRISMA protocol. The review in this article reveals several noteworthy findings pertaining to the factors (antecedents) influencing consumer engagement with social media influencers who subsequently affect consumer behaviour toward the brands promoted and used by social media influencers (outcomes). In terms of antecedents, this article finds that consumers may engage with social media influencers due to: 1) brand-related reasons such as brand placement; 2) influencer-related reasons such as attractiveness, authenticity, content, credibility, endorsement, expertise, opinion leadership, originality, relatability, and trustworthiness of the influencer; 3) social-related reasons such as social influence and word-of-mouth. In terms of consumer engagement, this article finds that consumers engage with social media influencers in four ways, namely clicking, commenting, liking, and sharing the posts of social media influencers. In terms of outcomes, this article finds that consumers' engagement with social media influencers can influence their attitude, awareness, credibility perception, loyalty, purchase intention, relationship, responses and trust toward brands. Implications and future research directions conclude this article.
这篇文章是对最近的相关期刊文章的系统回顾,这些文章是关于消费者使用PRISMA协议与社交媒体有影响力的人进行互动的。本文的回顾揭示了几个值得注意的发现,这些发现与影响消费者与社交媒体影响者互动的因素(前因)有关,这些因素随后影响消费者对社交媒体影响者所推广和使用的品牌的行为(结果)。在前因方面,本文发现消费者与社交媒体网红互动的原因有:1)品牌相关的原因,如品牌植入;2)与网红相关的原因,如网红的吸引力、真实性、内容、可信度、背书、专业知识、意见领导、原创性、相关性和可信度;3)社会相关原因,如社会影响、口碑。在消费者参与方面,本文发现消费者与社交媒体网红的互动方式有四种,即点击、评论、点赞和分享社交媒体网红的帖子。在结果方面,本文发现消费者与社交媒体网红的接触会影响他们对品牌的态度、意识、信誉感知、忠诚度、购买意愿、关系、反应和信任。本文的结论与未来的研究方向。
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引用次数: 3
Abnormal data classification of social media based on support vector machine 基于支持向量机的社交媒体异常数据分类
Q2 Social Sciences Pub Date : 2022-01-01 DOI: 10.1504/ijwbc.2022.10047485
Kangyi Wang N.A.
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引用次数: 0
期刊
International Journal of Web Based Communities
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