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1.
Social media data have recently attracted considerable attention as an emerging voice of the customer as it has rapidly become a channel for exchanging and storing customer-generated, large-scale, and unregulated voices about products. Although product planning studies using social media data have used systematic methods for product planning, their methods have limitations, such as the difficulty of identifying latent product features due to the use of only term-level analysis and insufficient consideration of opportunity potential analysis of the identified features. Therefore, an opportunity mining approach is proposed in this study to identify product opportunities based on topic modeling and sentiment analysis of social media data. For a multifunctional product, this approach can identify latent product topics discussed by product customers in social media using topic modeling, thereby quantifying the importance of each product topic. Next, the satisfaction level of each product topic is evaluated using sentiment analysis. Finally, the opportunity value and improvement direction of each product topic from a customer-centered view are identified by an opportunity algorithm based on product topics’ importance and satisfaction. We expect that our approach for product planning will contribute to the systematic identification of product opportunities from large-scale customer-generated social media data and will be used as a real-time monitoring tool for changing customer needs analysis in rapidly evolving product environments. 相似文献
2.
Veera Kangaspunta 《The Information Society》2013,29(5):275-288
AbstractThe research on online news comments has been dominated by a normative approach and has centered on media engagement. Normativity and media dominance have also featured big in the theoretical discussions on the public sphere. This article presents a case study of online news comments, combining a novel methodological testing of social network hypotheses to examine user–user interactions in online comments with a conceptual discussion of the potential connections between social network research and theories of the public. The social network analysis in this study indicated that users (online commentators) do not constitute highly dense networks, although their relations can be studied as social networks. However, this analysis can only explore limited features of this online phenomenon and requires complementary methods. From a conceptual perspective, this article confirms the role of shared issue for a potential public and also emphasizes the importance of context, actors, and meanings for understanding the public. 相似文献
3.
[目的/意义]对社交媒体中热门新闻的及时识别,有助于加速正面资讯的投送或抑制负面资讯的扩散。当前,基于自然语言处理的传统识别方法正面临社交媒体新生态的挑战：大量新闻内容以图片、音视频形式存在,缺乏用于语义及情感分析的文本。[方法/过程]对此,本文首先将社交网络划分为众多社群,并按其层次结构组织为贝叶斯网络。接着,面向社群构建基于卷积神经网络的热门新闻识别模型,模型综合考虑新闻传播的宏观统计规律及微观传播过程,以提取社群内热门新闻传播的特征。最后,利用贝叶斯推理并结合局部性的模型识别结果进行全局性热度预测。[结果/结论]实验表明,本方法在语义缺失场景下可有效识别热门新闻,其准确度强于基于语义信息的机器学习方法,模型具有良好的时效性、可扩展性和适用性。该研究有助于社交媒体的监管机构及时识别出各类不含语义信息且迅速扩散的热点内容。 相似文献
4.
[目的/意义] 本研究对国内外政务社交媒体相关研究进行梳理,分析现有研究的特点及不足,以期为政务社交媒体研究提供参考和借鉴。[方法/过程] 通过文献调研,对2014-2019年的国内外政务社交媒体研究进行系统归纳,梳理现有研究的特点及不足,以期为政务社交媒体研究提供参考和借鉴。[结果/总结] 分析发现近年的政务社交媒体研究多为量化研究,分析方法包括内容分析法、统计分析法、社会网络分析法、机器学习方法,研究主题集中于政务社交媒体的运营管理、内容挖掘、应急管理、功能与作用。现有研究存在着数据来源较为单一、数据分析方法有待优化、缺乏系统规范的研究范式等方面的不足,并从政府和公众两个视角为政务社交媒体的未来研究提供思路。 相似文献
5.
大数据环境下信息与通讯技术的发展, 使越来越多的用户进入了社交媒介建构的虚拟网络空间之中, 社交媒体的影响力也在不断增强。本文从信息内容和网络结构两个层面综述了国外计算机科学等相关领域的社交媒体研究, 指出了基于内容的主题提取, 信息传播的流行度分析, 社交媒体中的网络结构分析以及社区发掘等重要的研究领域;具体阐释了一部分具有基础性或典型性的模型、算法、以及相应的研究成果比较, 同时也提出了未来的研究领域和研究方法的可能发展方向。最后, 本文讨论了国外社交媒体影响力研究对基于国内语境之研究的启示。 相似文献
6.
《Information processing & management》2019,56(3):584-608
“We the Media” networks are real time and open, and such networks lack a gatekeeper system. As netizens’ comments on emergency events are disseminated, negative public opinion topics and confrontations concerning those events also spread widely on “We the Media” networks. Gradually, this phenomenon has attracted scholarly attention, and all social circles attach importance to the phenomenon as well. In existing topic detection studies, a topic is mainly defined as an "event" from the perspective of news-media information flow, but in the “We the Media” era, there are often many different views or topics surrounding a specific public opinion event. In this paper, a study on the detection of public opinion topics in “We the Media” networks is presented, starting with the characteristics of the elements found in public opinions on “We the Media” networks; such public opinions are multidimensional, multilayered and possess multiple attributes. By categorizing the elements’ attributes using social psychology and system science categories as references, we build a multidimensional network model oriented toward the topology of public opinions on “We the Media” networks. Based on the real process by which multiple topics concerning the same event are generated and disseminated, we designed a topic detection algorithm that works on these multidimensional public opinion networks. As a case study, the “Explosion in Tianjin Port on August 12, 2015″ accident was selected to conduct empirical analyses on the algorithm's effectiveness. The theoretical and empirical research findings of this paper are summarized along the following three aspects. 1. The multidimensional network model can be used to effectively characterize the communication characteristics of multiple topics on “We the Media” networks, and it provided the modeling ideas for the present paper and for other related studies on “We the Media” public opinion networks. 2. Using the multidimensional topic detection algorithm, 70% of the public opinion topics concerning the case study event were effectively detected, which shows that the algorithm is effective at detecting topics from the information flow on “We the Media” networks. 3. By defining the psychological scores of single and paired Chinese keywords in public opinion information, the topic detection algorithm can also be used to judge the sentiment tendencies of each topic, which can facilitate a timely understanding of public opinion and reveal negative topics under discussion on “We the Media” networks. 相似文献
7.
Ronald E. Rice 《The Information Society》2005,21(4):285-299
This study summarizes prior reviews of new media and Internet research, and the growth of the term Internet in academic publications and online newsgroups. It then uses semantic network analysis to summarize the interests and concepts of an interdisciplinary group of Internet researchers, as represented by session titles and paper titles and abstracts from the 2003 and 2004 Association of Internet Researchers conferences. In both years, the most frequent words appearing in the paper abstracts included Internet, online, community, social, technology, and research. The 2003 papers emphasized topics such as the social analysis/research of online/Internet communication, community, and information, with particular coverage of access, individuals, groups, digital media, culture; role and process in e-organizations; and world development. The 2004 papers emphasized topics such as access; news and social issues; the role of individuals in communities; user-based studies; usage data; and blogs, women, and search policy, among others. 相似文献
8.
This paper examines how alternative food networks (AFNs) cultivate engagement on a social media platform. Using the method proposed in Kar and Dwivedi (2020) and Berente et al. (2019), we contribute to theory through combining exploratory text analysis with model testing. Using the theoretical lens of relationship cultivation and social media engagement, we collected 55,358 original Weibo posts by 90 farms and other AFN participants in China and used Latent Dirichlet Allocation (LDA) modeling for topic analysis. We then used the literature to map the topics with constructs and developed a theoretical model. To validate the theoretical model, a panel dataset was constructed on Weibo account and year level, with Chinese city-level yearly economic data included as control variables. A fixed effects panel data regression analysis was performed. The empirical results revealed that posts centered on openness/disclosure, sharing of tasks, and knowledge sharing result in positive levels of social media engagement. Posting about irrelevant information and advertising that uses repetitive wording in multiple posts had negative effects on engagement. Our findings suggest that cultivating engagement requires different relationship strategies, and social media platforms should be leveraged according to the context and the purpose of the social cause. Our research is also among the early studies that use both big data analysis of large quantities of textual data and model validation for theoretical insights. 相似文献
9.
《Information processing & management》2020,57(6):102348
Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved framework for pre-processing of social media feeds for better performance. To do this, the use of an integrated knowledge base (ikb) which comprises a local knowledge source (Naijalingo), urban dictionary and internet slang was combined with the adapted Lesk algorithm to facilitate semantic analysis of social media feeds. Experimental results showed that the proposed approach performed better than existing methods when it was tested on three machine learning models, which are support vector machines, multilayer perceptron, and convolutional neural networks. The framework had an accuracy of 94.07% on a standardized dataset, and 99.78% on localised dataset when used to extract sentiments from tweets. The improved performance on the localised dataset reveals the advantage of integrating the use of local knowledge sources into the process of analysing social media feeds particularly in interpreting slangs/acronyms/abbreviations that have contextually rooted meanings. 相似文献
10.
《Information processing & management》2020,57(2):102018
Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1. 相似文献
11.
日益兴起的社会网络负功能研究,修正了以往过度重视积极效应的认识误区,逐渐成为社会网络研究的核心话题。本文基于企业层面,对相关领域的文献进行总结梳理后发现,企业社会网络的负功能可作用于微观、中观和宏观三个层面,分别表现为限制组织成员的创造力、导致企业面临"负债"困境、促使市场分割与地方保护主义的形成。在影响因素的研究中,现有的分析框架可归纳为网络结构主义和组织环境两种理论视角,网络排他性、过度嵌入、市场需求的不确定性、技术动荡、企业生命周期与战略目标均对社会网络负功能的发生产生重要作用。在此基础上,本文从研究视角、内容、方法和理论建构四个方面提出未来研究的发展方向。 相似文献
12.
本文以CNKI数据库中1 003篇国内"微博"研究文献为研究对象,运用共词分析方法和社会网络分析方法,以SPSS 17.0、Ucinet 6.2和NetDraw软件为分析工具,通过聚类分析、相关分析和K-核分析等分析方法,确定了我国"微博"研究的6个重要主题:微博传播特征、微博传播机制、微博用户特征、微博舆情传播、微博应用领域和微博与传统媒体的比较研究,为探析我国微博研究领域的研究热点和发展趋势提供了参考。 相似文献
13.
从社会网探讨团队知识整合是一个重要方向,但现有研究忽略了因媒介不同对社会网联结产生的差异,同时关注点更多放在直接效应的检验。结合媒介丰富度和个体社会网两种视角,推进了对个体社会网多模式形态(面对面、电话、电子和纸质)的认识。同时整合了社会网的能力观和资源观,提出直接效应和间接效应两种理论模型。通过对知识型团队的问卷调查,采取结构方程模型方法对数据进行分析。研究结果发现:个体网不仅对知识整合产生直接影响,而且还通过知识定位和合作满意度,对知识整合产生间接影响。其中,面对面的个体网联结模式影响最为显著。 相似文献
14.
Ronald E. Rice 《The Information Society》2013,29(4):285-299
This study summarizes prior reviews of new media and Internet research, and the growth of the term Internet in academic publications and online newsgroups. It then uses semantic network analysis to summarize the interests and concepts of an interdisciplinary group of Internet researchers, as represented by session titles and paper titles and abstracts from the 2003 and 2004 Association of Internet Researchers conferences. In both years, the most frequent words appearing in the paper abstracts included Internet, online, community, social, technology, and research. The 2003 papers emphasized topics such as the social analysis/research of online/Internet communication, community, and information, with particular coverage of access, individuals, groups, digital media, culture; role and process in e-organizations; and world development. The 2004 papers emphasized topics such as access; news and social issues; the role of individuals in communities; user-based studies; usage data; and blogs, women, and search policy, among others. 相似文献
15.
《Information processing & management》2022,59(3):102935
The rapid dissemination of misinformation in social media during the COVID-19 pandemic triggers panic and threatens the pandemic preparedness and control. Correction is a crucial countermeasure to debunk misperceptions. However, the effective mechanism of correction on social media is not fully verified. Previous works focus on psychological theories and experimental studies, while the applicability of conclusions to the actual social media is unclear. This study explores determinants governing the effectiveness of misinformation corrections on social media with a combination of a data-driven approach and related theories on psychology and communication. Specifically, referring to the Backfire Effect, Source Credibility, and Audience’s role in dissemination theories, we propose five hypotheses containing seven potential factors (regarding correction content and publishers’ influence), e.g., the proportion of original misinformation and warnings of misinformation. Then, we obtain 1487 significant COVID-19 related corrections on Microblog between January 1st, 2020 and April 30th, 2020, and conduct annotations, which characterize each piece of correction based on the aforementioned factors. We demonstrate several promising conclusions through a comprehensive analysis of the dataset. For example, mentioning excessive original misinformation in corrections would not undermine people’s believability within a short period after reading; warnings of misinformation in a demanding tone make correction worse; determinants of correction effectiveness vary among different topics of misinformation. Finally, we build a regression model to predict correction effectiveness. These results provide practical suggestions on misinformation correction on social media, and a tool to guide practitioners to revise corrections before publishing, leading to ideal efficacies. 相似文献
16.
This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion. 相似文献
17.
中国科技政策主体合作网络演化研究 总被引：2，自引：0，他引：2
运用社会网络分析方法,绘制不同发展阶段中国科技政策制定主体合作网络图谱,提炼网络结构演化的模式特征;通过合作“广度—强度”二维矩阵分析政策主体在网络中的角色演变,并识别网络中的核心节点;在此基础上,探讨了不同核心主体在科技政策主体合作网络生成与演化过程中的功能演变及其对网络运行的影响。结果表明,在中国科技政策主体合作网络演化过程中,整体结构优化与主体功能提升相互促进,并呈现出同步演化特征;中国科技政策主体合作网络演化具有体制改革推动和科技发展需求拉动的双重驱动特征。 相似文献
18.
《Information processing & management》2022,59(4):102990
Documenting the emergent social representations of COVID-19 in public communication is necessary for critically reflecting on pandemic responses and providing guidance for global pandemic recovery policies and practices. This study documents the dynamics of changing social representations of the COVID-19 pandemic on one of the largest Chinese social media, Weibo, from December 2019 to April 2020. We draw on the social representation theory (SRT) and conceptualize topics and topic networks as a form of social representation. We analyzed a dataset of 40 million COVID-19 related posts from 9.7 million users (including the general public, opinion leaders, and organizations) using machine learning methods. We identified 12 topics and found an expansion in social representations of COVID-19 from a clinical and epidemiological perspective to a broader perspective that integrated personal illness experiences with economic and sociopolitical discourses. Discussions about COVID-19 science did not take a prominent position in the representations, suggesting a lack of effective science and risk communication. Further, we found the strongest association of social representations existed between the public and opinion leaders and the organizations’ representations did not align much with the other two groups, suggesting a lack of organizations’ influence in public representations of COVID-19 on social media in China. 相似文献
19.
Research typically focuses on one medium. But in today's digital media environment, people use and are influenced by their experience with multiple systems. Building on media ecology research, we introduce the notion of integrated media effects. We draw on resource dependence and homophily theories to analyze the mechanisms that connect media systems. To test the integrated media effects, we examine the relationships between news media visibility and social media visibility and hyperlinking patterns among 410 nongovernmental organization (NGO) websites in China. NGOs with greater news media visibility and more social media followers receive significantly more hyperlinks. Further, NGOs with a similar number of social media followers prefer to hyperlink to each other. The results suggest that both news media and social media systems are related to the configuration of hyperlink networks, providing support for the integrated media effects described. Implications for the study of hyperlink networks, online behaviors of organizations, and public relations are drawn from the results. 相似文献
20.
在国际科技竞争日益激化、我国科技实力迅速腾飞的当下,国家科学形象日益成为国家形象立体化过程中不可忽视的重要组成部分。通过对国际社交媒体推特平台上有关中国科学相关议题讨论的分析发现,西方公众目前对于中国科学类相关议题的关注度并不高,讨论由少量的主要科学事件主导,明显受到了西方主流新闻媒体与政治话语力量的引导与掌控,且认知偏向于负面。这种负面形象的“他塑”建构在一定程度上被卷入政治话语与国际关系话语体系中,表征为对中国负面科技新闻的报道及阴谋论的关注。而正面积极的科学形象则更多表征为“去政治化”语境下,对中国突破性科学成果与获得国际科学奖项科学家的赞赏,以及对于中国科幻的格外关注。对此,提升中国国际科学形象需要在重视社交媒体平台这一舆论场域的基础上,结合研究结果,制定具有针对性的对外科技传播策略。 相似文献