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1.
《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
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【目的/意义】通过构建网络舆情传播分析模型,探究新冠肺炎疫情网络舆情传播过程和演化规律,提出新
冠肺炎疫情常态化背景下相关网络舆情引导和舆情治理建议。【方法/过程】基于信息生态学理论,从信息、信息人
和信息环境三要素分析舆情事件,构建信息生态学视角下的网络舆情传播分析模型。以新冠肺炎疫情中的方舱医
院事件为例,运用主题分析、社会网络分析和情感分析等方法进行实证研究,分析舆情内容演进和情感演化规律,
总结新冠肺炎疫情网络舆情传播特征。【结果/结论】结果表明,本文所构建的舆情传播分析模型能够较为全面地刻
画公众对于舆情事件的反应,分析舆情传播规律与演化趋势,挖掘不同分析维度的内在关联。【创新/局限】从信息
生态学视角出发,基于内容、用户和情感等维度构建舆情传播分析模型。下一步将结合二模网络、知识图谱等研究
方法探索新冠肺炎疫情中舆情事件之间的关联性。 相似文献
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The use of social media and Web 2.0 platforms is proliferating and affecting different formal and highly structured organisations including public safety agencies. Much of the research in the area has focussed on public use of social media during an emergency as well as how emergency agencies benefit from the data and information generated by this process. However, there is little understanding of “what are the operational implications of this public use on emergency management agencies and how does social media either positively or negatively impact these operations”? In order to progress research into this topic, we chose an engaged scholarship framework to shape a research agenda with the active participation of stakeholders. Hence, we conducted a series of workshops primarily involving over 100 public safety practitioners working in the area of disasters and emergency management who work in public safety agencies, humanitarian organisations, volunteering online platforms and volunteer groups in addition to 20 academics working on this area of enquiry. The findings highlight six different challenges that emergency responding organisations currently face in relation to social media use. We conceptualise these challenges as creating six operational tension zones for organisations. We discuss these tensions and their implications for future research and practice. 相似文献
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本文针对微博网络舆情的控制和引导问题,提出一种基于复杂网络的图谱分析方法。本文以微博用户间转发和评论某一话题下用户关系数据作为基础研究数据,生成用户节点网络关系图谱,通过对微博网络模块化图谱、路径图谱和中心性图谱分析,定性和定量评估出对舆情活跃度高、传播范围广、传播速度快的微博用户节点,作为控制微博舆情的传播、引导舆情舆论导向的关键用户节点。本文以某一微博社区为样本数据,采用复杂网络分析工具Gephi,验证了基于复杂网络的图谱分析对识别舆情控制中关键用户节点的正确性和有效性。 相似文献
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[目的/意义]网民情感变化是影响政府舆情应对进程、政策、策略的关键。因此，构建科学高效的情感词典，对网民情感分析研究及应对策略的选择具有重要的实际意义。[方法/过程]结合扎根理论的质性研究特点，在情感词典的构造中融入突发事件演化规律影响因素，采用点互信息算法，TF-IDF，统计量等方法对微博表情符号词典及突发事件专属情感词典进行构建，编制了突发事件情感词典，随后选取"6.22"杭州保姆纵火案微博语料进行情感分析。[结论/结果]实验发现，与不加入影响因素的情感词典相比，本文构造的词典在准确率召回率等指标的对比中均得到了一定程度的提高。同时，结合扎根理论与主题分析的结果，对舆情发展的不同阶段所选择的舆情应对策略提供了参考。 相似文献
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随着互联网的高速发展,作为事件信息传播平台的网络舆情开始对非常规突发事件的发展产生越来越大的影响。进入21世纪,非常规突发事件的频繁发生使人民的生命财产遭受了巨大的损失。政府通过正确的渠道和方式,把握和引导非常规突发事件的网络舆情,对于事件的处理显得尤为重要。根据非常规突发事件的一般定义分析其特征,详细研究网络舆情的形成机理及影响其演化的要素,描述非常规突发事件网络舆情产生的潜在影响,结合政府现阶段处理非常规突发事件网络舆情的方法,找出其中存在的主要问题,并提出几点建议为政府日后处理非常规突发事件的网络舆情提供参考。 相似文献
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【目的/意义】探索重大疫情中网络舆情的多属性演化规律,为重大疫情防控中的网络舆情治理实践提供参
考。【方法/过程】基于社会学视角构建了重大疫情中网络舆情多属性演化分析模型,选取湖北红十字会事件微博舆
情数据为研究对象,采用意见领袖影响力评价、LDA主题模型和Snownlp情感分析方法对重大疫情中网络舆情意见
领袖、主题分布和情感走势进行分析,从人群、内容和情绪三种社会属性揭示重大疫情中网络舆情多属性演化规
律。【结果/结论】结果表明,重大疫情网络舆情可以分为突发期、爆发期、降温期和失焦期四个阶段,各个阶段在意
见领袖、主题讨论内容和情感倾向上均有较为明显的区别,网民的情感阶段变化与舆情的主题属性演化规律相吻
合。【创新/局限】本研究提出的模型能够有效满足重大疫情网络舆情演化特征深度挖掘的要求,为重大疫情网络舆
情的治理实践提供参考。后续研究可选取更广泛的舆情事件和数据源验证该模型。 相似文献
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【目的/意义】知识图谱不仅是新一代人工智能的前沿技术,而且是大规模知识工程的学科方向。探究我国
知识图谱研究的分布特点和发展趋势,将对技术应用和学科研究具有参考价值和借鉴意义。【方法/过程】本文运用
文献计量学方法和数据可视化技术,针对我国知识图谱研究重要文献的来源库、期刊、基金、机构、作者、关键词等
进行计量研究,采用ECharts和VOSviewer工具可视分析。再根据共词分析方法挖掘“知识抽取”和“知识应用”两大
研究主题,使用Python软件绘制词云图,着重阐述其核心内容、关键问题和主要趋势。【结果/结论】我国知识图谱研
究具有应用跨界与文献激增的总体特点,并呈现六大具体特点。面向开放域的知识抽取技术和智能应用方法,将
成为大规模知识图谱的未来发展趋势。【创新/局限】聚焦人工智能学科范畴,依据共词分析和生命周期,综合利用
多种可视化工具,我国知识图谱研究的分布特点和重要主题得以阐述。然而,国际知识图谱的文献计量与主题挖
掘,尚待进一步分析与研究。 相似文献
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This study introduces a data-driven approach for assessing the practices and effectiveness of digital diplomacy, using the cases of South Korea and Japan. The study compared the networking power of public diplomacy organizations based on social media use, engagement with the public, interaction patterns among the public, and public perceptions and attitudes toward organizations. This was accomplished through a three-step method employing social network analysis and topic modeling. The network analysis found that the Korean public diplomacy organization generated a larger, more loosely connected, and decentralized comment network than the Japanese organization, which presented a “small-world” connectivity pattern with highly interconnected actors. The findings also suggest that, compared to the Japanese organization, the Korean organization was successful in not only enhancing its soft power through social media but also building international networks among the foreign public. 相似文献
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“厌女症”是指文学作品中存在的歪曲、贬低女性形象的表征,并把一切罪过都推到女性头上的情绪或主题。《伊库斯》是当代英国剧作家彼得·谢弗最优秀的剧作之一。在《伊库斯》中,彼得·谢弗表现出明显的厌女症倾向,具体体现为塑造的女性形象传统刻板,把责任和问题归咎于女性以及贬低女性的智慧,认为男女之间在灵魂和肉体上都无法沟通。 相似文献
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随着新能源汽车产业的发展,电动汽车概论课程已成为当前高等学校车辆工程专业的热门课程。2020年春节前后,一场突发公共卫生事件引发了线上教学的应急性改革,并持续影响着后续的教学改革。文章以电动汽车概论课程教学实践为基础,通过分析课程教学内容及实施过程,旨在为公共卫生事件发生后该课程的教学探索提供新的思路。 相似文献
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【目的/意义】构建高校师德舆情微博用户评论LDA模型,可以更精准识别舆情演化特征和分析关键主题传
播路径,帮助高校和相关部门更为有效地进行舆情监管和舆情引导。【方法/过程】本文以“天津大学一教授学术造
假”事件为例,基于 LDA模型构建高校师德舆情下微博用户主题生成模型,采用困惑度评价指标确定 LDA模型最
优主题数,采用信息熵确定每一主题在不同日期的主题强度,通过关键词共现知识图谱、词云展现舆情话题的演
变,最后基于主题相似度确定主题传播路径。【结果/结论】LDA模型和信息熵可以解析出网络用户群体关注的重要
主题热点,精准识别舆情演化特征,识别主题最优传播路径进行舆论引导,对爆发的舆情实现预测和管制优化。【创
新/局限】文章创新性地构建高校学术道德舆情的LDA主题模型,有效确定微博用户群体主题、识别舆情演化特征、
分析主题间传播路径,具有普适性;进一步扩大高校师德其他舆情分析及结合网络舆情情感分析为下一步的研究
内容。 相似文献
14.
Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor
Analyzing and extracting insights from user-generated data has become a topic of interest among businesses and research groups because such data contains valuable information, e.g., consumers’ opinions, ratings, and recommendations of products and services. However, the true value of social media data is rarely discovered due to overloaded information. Existing literature in analyzing online hotel reviews mainly focuses on a single data resource, lexicon, and analysis method and rarely provides marketing insights and decision-making information to improve business’ service and quality of products. We propose an integrated framework which includes a data crawler, data preprocessing, sentiment-sensitive tree construction, convolution tree kernel classification, aspect extraction and category detection, and visual analytics to gain insights into hotel ratings and reviews. The empirical findings show that our proposed approach outperforms baseline algorithms as well as well-known sentiment classification methods, and achieves high precision (0.95) and recall (0.96). The visual analytics results reveal that Business travelers tend to give lower ratings, while Couples tend to give higher ratings. In general, users tend to rate lowest in July and highest in December. The Business travelers more frequently use negative keywords, such as “rude,” “terrible,” “horrible,” “broken,” and “dirty,” to express their dissatisfied emotions toward their hotel stays in July. 相似文献
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[目的/意义]借鉴拉斯韦尔5W传播模型,探讨微博中预置话题与突发话题的特征。[方法/过程]基于因子分析的关键节点影响力评价方法来研究话题节点特征;利用Ward方法对话题博文量曲线进行聚类,并对曲线进行多项式拟合;对话题情感度曲线进行聚类,并对不同类型话题的峰度和整体情感加权平均高峰期进行研究。[结果/结论]话题首节点多是信息分享,其中突发话题的传播多是从事件的客观描述开始;话题微博按博文量可分为前热型、中热型、后热型、爆发型和持续型,其中突发话题比预置话题爆发得更早、持续时间更长;话题出现72小时后公众情感趋于中立,但突发话题情感曲线高峰期比预置话题更集中,整体情感加权平均高峰期更早出现,曲线趋势更复杂。 相似文献
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《Information processing & management》2019,56(6):102079
The emergence of social media and the huge amount of opinions that are posted everyday have influenced online reputation management. Reputation experts need to filter and control what is posted online and, more importantly, determine if an online post is going to have positive or negative implications towards the entity of interest. This task is challenging, considering that there are posts that have implications on an entity's reputation but do not express any sentiment. In this paper, we propose two approaches for propagating sentiment signals to estimate reputation polarity of tweets. The first approach is based on sentiment lexicons augmentation, whereas the second is based on direct propagation of sentiment signals to tweets that discuss the same topic. In addition, we present a polar fact filter that is able to differentiate between reputation-bearing and reputation-neutral tweets. Our experiments indicate that weakly supervised annotation of reputation polarity is feasible and that sentiment signals can be propagated to effectively estimate the reputation polarity of tweets. Finally, we show that learning PMI values from the training data is the most effective approach for reputation polarity analysis. 相似文献
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根据舆情传播不同阶段的特征引入演化博弈理论,分别针对成长期和蔓延、爆发期两个阶段的企业和微博用户建模并得到进化稳定策略。分析不同类型舆情事件下偏好不同企业的策略选择,用"大众DSG变速器故障"事件作为案例验证模型的合理性,为企业应对负面舆情提供建议。 相似文献
19.
《Information processing & management》2020,57(2):102177
The transmission of online emergency information has become an active means of expressing public opinion and has vitally affected societal emergency response techniques. This paper analyzes interactions between three groups in time and space using a classic SIR (susceptible, infected, and recovered) epidemic model. Through social network theory and analog simulation analysis, we utilize data from China's Sina Weibo (a popular social media platform) to conduct empirical research on 101 major incidents in China that occurred between 2010 and 2017. We divide these emergencies into four types—natural disasters, accidents, public health events, and social security events—and conduct a simulation using three examples from each group. The results show that government control of public opinion is both cheaper and more effective when it occurs at the initial stages of an incident. By cooperating with the government, the media can facilitate emergency management. Finally, if netizens trust the government and the media, they are more likely to make cooperative decisions, maintain interest, and improve the management of online public sentiment. 相似文献
20.
《Research Policy》2022,51(1):104390
Public innovation has received increasing attention in recent years. Experiments with new governance structures, such as New Public Management and New Public Governance, have challenged the traditional top-down, internally driven forms of innovation in the public sector and have entailed a search for new forms of open, collaborative and interactive innovation, implying a reframing of public innovation activities. However, introducing these new frames of innovation causes uncertainties in the public sector, necessitating better understanding of how public innovation can be changed to address societal needs. This paper uses materials from case studies of 21 public living labs across Europe to analyse the lessons that can be learned from public sector participation in living labs in terms of their contribution to reframing public innovation. The “frame” construct is used to analyse and provide an understanding of how participation in living labs helps public actors to reframe innovation and address public and societal needs. Three living lab framings for changing public innovation are identified (processual learning, restrained space and democratic engagement), and the degree of intensity of these framings with respect to involving stakeholders and addressing societal challenges is discussed. The paper contributes to knowledge of public sector innovation by extending previous accounts of how public innovation can be improved. 相似文献