Weibo信息发布影响力与公司绩效实证研究——基于我国传媒行业上市公司的经验证据

随着互联网的不断发展,weibo等网络新媒体也开始飞速发展,在媒体传播信息中的地位越来越重要。weibo等网络媒体的特点不仅在于其发布的信息更大众化,更亲民化,得到的传播也更广泛,也在于打破了常规的单向传播,变为了更具有互动性的多项传播。传媒企业在网络新媒体上所取得的成绩,也是其业务能力的一种体现,而这种业务能力也会相应的影响到公司绩效。

Evolution mechanism of Weibo top news competition

In a certain period,some news will compete for the top news to gain the most attention and influence,and more news will be submerged in the ocean of news and become mediocre.This article deeply studies the evolution process and competition mechanism of the dissemination of Weibo news.In this paper,we innovatively propose a pre-processing scheme for traditional small-world networks and scale-free networks and divide nodes into three roles:fans,passersby,and anti-fans.The competition mechanism of Weibo top news is defined from the aspects of node role and node aggregation degree.A network evolution model is established based on the competition mechanism.The propagation characteristics of the network evolution model are deeply analyzed,and simulation experiments are performed on the small-world network and the scale-free network.Finally,the validity and rationality of the new model are verified through comparative experiments,and a feasible scheme for the propagation of top news on Weibo is given.

Estimating the Number of Posts in Sina Weibo

Sina Weibo,an online social network site,has gained popularity but lost it in recent years.Now we are still curious on the number of posts in Sina Weibo in its golden age.Besides checking this number in Sina’s operating results,we aim to estimate and verify this number through measurement by using statistical techniques.Existing approaches on measurement always rely on the supported streaming application programming interface(API)which provides proportional sampling.However no such API is available for Sina Weibo.Instead,Sina provides a public timeline API which provides non-proportional sampling but always returns a(nearly)fixed number of s amples.In this paper,we present a novel method utilizing this API and estimate the number of posts in Sina Weibo in its golden age.

A Multi-Agent Based Model for User Interest Mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations,advertising,marketing promotions,and other tasks.Although great progress has been made in this area,previous studies have ignored the differences among users:the varied behaviors and habits that lead to unique user data characteristics.It is unreasonable to use a single strategy to mine interests from such varied user data.Therefore,this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data,microblogs and correlations.This method has the ability to select the appropriate strategy based on each user’s data characteristics.The experimental results show that the proposed method performs better than the baselines.

Application of Association Rule Mining Theory in Sina Weibo

A user profile contains information about a user. A substantial effort has been made so as to understand users’ behavior through analyzing their profile data. Online social networks provide an enormous amount of such information for researchers. Sina Weibo, a Twitter-like microblogging platform, has achieved a great success in China although studies on it are still in an initial state. This paper aims to explore the relationships among different profile attributes in Sina Weibo. We use the techniques of association rule mining to identify the dependency among the attributes and we found that if a user’s posts are welcomed, he or she is more likely to have a large number of followers. Our results demonstrate how the relationships among the profile attributes are affected by a user’s verified type. We also put some efforts on data transformation and analyze the influence of the statistical properties of the data distribution on data discretization.

Using Weibo to Extend University Ideological and Political Education

With the diversified development of conmiunication platforms and information,internet resources are becoming more and more abundant,and the challenges faced by all walks of life are becoming more and more difficult.It is necessaiy to progress with the times and introduce new generation resources,and to prevent various iiifonnation leakage and fraud risks caused by the internet,preventing too much bad information oil the internet.University is the crucial stage for students to establish a good understanding,and good ideological and political education can promote the establishment of correct‘Three Views’in students.This paper mainly uses Weibo as the earner to explain the relevant fimetions of Weibo,using Weibo to assist the challenges faced by the universities in ideological and political education,and using Weibo to extend the chamiels of ideological and political education in universities for the reference of relevant personnel.

新浪“weibo”商标 告赢商评委

北京微梦创科网络技术有限公司（下称北京微梦公司）作为新浪微博的实际运营者,欲在计算机终端通讯、电视广播等服务上申请注册＂Weibo及图＂商标,但被商标局以该商标与新浪网技术（中国）有限公司（下称新浪公司）在类似服务上已注册的第12919579号图形商标构成近似为由驳回。

Online Ideological and Political Education in Colleges and Universities Under the Weibo Network Environment

In recent years,the in-depth development of quality education has brought opportunities for the innovation and reform of ideological and political education in colleges and universities in China.Online ideological and political education,as a modern embodiment of ideological and political education,is planned and carried out purposefully under the Weibo network environment,which is more conducive to breaking the distance between time and space and presenting high-quality educational content to students.In view of this,this article will focus on the development of online ideological and political education under the Weibo network environment,consider the existing problems,and propose specific countermeasures to improve the level of online ideological and political education in colleges and universities.

缤纷WEIBO

协和医院近视矫正医生-李莹【近视度数增长眼镜不背'锅'】很多家长认为孩子近视增长是因为戴眼镜的缘故。这样看来,眼镜确实有点儿冤。近视度数加深与戴眼镜没有关系,其原因是由于孩子生理发育眼轴不断增长,再加上用眼负担的加重,以及不正确的用眼等因素。戴镜后视觉质量提高,用眼疲劳改善,在一定程度上延缓了近视的进展。

情感分歧对社交媒体中信息再传播的影响——以微博为例

[研究目的]社交媒体中信息的再传播是目前主流传播方式之一,研究情感特征对社交媒体中信息再传播的作用,揭示情感信息影响社交媒体中信息再传播的机制,对于科学引导网络舆论、维持健康网络环境具有重要意义。[研究方法]基于认知失调理论,引入情感分歧特征进行情感分析,以社交媒体中帖子的转发数与评论数作为信息再传播效果的衡量指标,构建了情感分歧与情感倾向影响信息再传播的理论模型,并使用微博平台上的数据,对模型进行验证。[研究结论]通过对回归模型结果分析,情感分歧能够显著正向影响社交媒体信息再传播,促进帖子的转发与评论;正面情感倾向性能够显著促进帖子的转发效果,对帖子的评论数影响效果并不显著;帖子内容的情感倾向性能够调节帖子内容中的情感分歧对帖子转发的影响,而评论情感倾向能够调节评论内容中的情感分歧对帖子评论数的影响。

社交媒体中电子竞技的社会形象

本研究基于微博与抖音两大社交媒体中的电子竞技相关文本,探索电子竞技社会形象。从“印象”“认知”“评价”3个层面,分别利用词频统计、主题模型与情感倾向分析考察社会公众对电子竞技的认识,探讨电子竞技社会形象的主要特征及其建构过程中的深层影响因素。研究认为,社交媒体场域中电子竞技社会形象被普遍看作是积极正面的,社会公众以电竞游戏和电竞赛事为核心,建构了以职业化为目标、以情感体验为诉求、以产业多元化为方向的电子竞技社会形象。这一建构过程同时受到经济发展、政府态度、文化环境、认知途径和社会话语权等因素的影响,未来电子竞技社会形象可以从提升电子竞技相关人才的职业素养、正确引导粉丝及圈层、积极发掘电子竞技经济价值、借鉴传统体育项目的可持续发展路径、重视电子竞技的情感体验和文化传播作用等方面进行优化。

考虑用户能动性和流动性的舆情传播模型

针对现有信息传播模型忽略了用户主观能动性和社交网络动态性的问题,提出异构网络中考虑用户能动性和流动性的SCBRD(Susceptible-Commented-Believed-Recovered-Defensed)舆情传播模型。首先,利用下一代矩阵方法计算基本再生数,并运用Lyapunov稳定性定理和庞特里亚金原理分析了系统的动力学和最优控制问题。然后,基于BA(Barabási-Albert)无标度网络进行仿真分析以确定影响舆情传播的显著因素,结果表明,用户的好奇心理、转发行为和进入率在信息扩散中起着主导作用,并且系统存在最优控制解。最后,依据实际数据验证模型的合理性。与SCIR(Susceptible-inCubation-Infective-Refractory)模型相比,SCBRD模型的拟合优度提高了27.40%,预测的均方根误差(RMSE)减小了39.02%。因此,该模型能够适应信息传播复杂的变化形势,为官方的舆情监管提供较好的指导。

以新浪微博为例的社交网络服务质量改进

随着社交网络的不断发展与流行,社交网络的服务质量以及经营成效成为了运营商赢得市场竞争优势的关键,研究社交网络服务质量改进有利于提升运营商的市场竞争力。在理论与现有文献研究基础上开发了社交网络服务质量量表,以中国的社交网络新浪微博为例,基于开发的量表结合KANO模型及IPA分析方法探讨新浪微博网络服务质量的特性以及服务质量提升建议,并根据企业当前的绩效和融入战略之后的改进策略进行对比分析。实证分析表明,新浪微博网络服务质量具有二维质量的属性,而且考虑企业战略有助于管理者做出更完善的质量改善决策。

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

日常语言创造性的生成与理解--以金庸小说关键词的微博使用为例

语言的创造性一般被认为是文学家的专利,但事实上,源自文学作品的创造性表达在日常语言交流中也俯拾皆是。金庸小说关键词体现了文学家的创造性,其在社交媒体中的创新使用则体现了普通大众的语言创造性,主要表现为词汇使用创新和多模态语言创新。从社会认知视角来看,金庸小说关键词在微博中的创造性使用涉及概念整合的认知建构作用。具体而言,日常语言创造性的生成与理解离不开其建构者和共同建构者的概念整合认知建构,以及文学经典化和日常生活化所提供的共享社会文化背景。

Emotional differences based on comments on doctor-patient disputes with varying levels of severity

BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.

数字空间生育议题的公众表达及性别差异:以新浪微博为例的分析

分析数字空间生育议题的公众表达,既能深化对人口动态变化微观逻辑的理解,也能为政策制定提供公众情感和态度方面的直接证据。研究基于5300条微博文本数据,采用LDA主题模型和SnowNLP情感分析工具,探讨三孩生育政策实施初期及两年后公众对生育议题的态度表达及变化。研究显示,支持和批评共同构成了生育政策公共讨论的主要内容,生育率—女性的共现频次最高,女性对生育的关注度明显更高,负面情绪也更多。同时,随着政策实施时间的推移,讨论从相对广泛议题逐渐聚焦于生育率问题。在数字空间里,公众对传统婚育观念的挑战、对个人幸福和自我实现的追求,反映出社会价值观、个人权利与政策导向之间的多维交织。

构建新媒体环境下舆论控制的新模式——基于微博舆情事件控制的案例分析

新媒体环境下,新闻传播模式由单向式的传播变成多维、立体、几何式的传播,导致媒体格局、传播模式以及舆论生态与传统媒体环境下新闻传播不同,新闻舆论工作面临新的挑战。以微博热点为例,对几个典型舆论案例进行分析。针对新媒体环境下微博舆论传播的特点及现实困境,从舆论主体、舆论话语权掌握、舆论传播渠道、舆论传播环境四个方面出发,建构“回应—引导—联动—法治”微博的舆论控制新模式。

基于微博文本和深度学习的台风灾情识别方法研究——以2010—2019年广东省过境台风为例

结合台风属性数据和多标签分类方法,以BERT-BiLSTM(Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory)为分类模型,提出基于微博文本与深度学习的台风灾情识别方法,对2010—2019年登陆广东省的强台风/超强台风灾情进行识别,在粗分类获取台风灾情相关微博文本的基础上,进一步细分类为交通影响、社会影响、电力影响、林业影响和内涝积水等5类灾情。结果表明:1)提出的台风灾情识别方法粗分类和细分类精度分别达到0.907和0.814;2)强台风/超强台风的灾情占比受台风强度、路径和受灾地区发展水平等因素影响而存在差异;3)台风登陆前,灾情主要为台风预防措施导致的交通影响和社会影响。台风登陆后,灾情表现出单峰和双峰特征,反映台风灾情的变化趋势和特点。