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基于内容相似性的论坛用户社会网络挖掘 总被引：1，自引：0，他引：1
现有研究通常基于论坛用户之间的回复引用关系建立用户社会网络,提出一种基于用户发帖内容相似度值的论坛用户社会网络的构建与挖掘方法,该方法能够透过用户的交流内容发现潜在的用户交流关系,为网络舆情监控提供决策依据.以人民网强国社区为研究对象,利用提出的方法构建用户社会网络,并对该网络进行了网络特性分析与挖掘,实验结果得出强国社区用户网络内聚性较高,用户之间交流关系紧密等结论. 相似文献
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基于精细加工可能性模型(ELM)理论,依据微博数、粉丝数和关注数,在将微博用户划分为游离型、信息获取型、活跃型和名人型4种群体的基础上,构建了微博用户公共情绪偏好分析模型,并进行实证研究,最后针对微博用户的分类引导提出了可操作性建议. 相似文献
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以新浪微博为研究平台,以麦当劳3·15事件为例,通过社会网络分析法,研究负面口碑的传播效果,得到中间中心度排名与粉丝数、微博数显著正相关的结论,即粉丝数越大、发布微博数越多的用户,中间中心性排名就越靠前,在传播路径图中的控制能力愈强、权力愈大,对负面口碑传播的中介作用也愈大。 相似文献
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【目的/意义】为探究学术型社区用户协同交互行为生命周期,旨在通过基于学术型社区主题帖的协同交
互行为的调研分析,指导学术型社区用户协同交互行为的持续开展,促进知识资源的有效利用。【方法/过程】分别
选取丁香园社区的热门版块和冷门版块共计 4个版块的 84日内的病例帖作为样本,进行时序性调研分析,并计算
各版块的生命周期分布。【结果/结论】丁香园社区协同交互行为整体生命周期均值为 40.5日,热门版块相对冷门版
块而言其协同交互行为活跃期和衰退期均更长,学术型社区用户在工作日的协同交互频率要明显高于非工作日。 相似文献
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[目的/意义]非替代性时间分配行为是指个体对计划的时间不愿挪作他用的现象，引入心理账户理论分析在线健康社区用户该行为的规律，有助于预测用户行为与优化信息服务。[方法/过程]爬取丁香园论坛慢病区用户行为以及统计学特征信息，依据时间的非替代性从“来源—用途”构建在线健康社区用户发回帖时间分配框架，分别从发帖行为、回帖及其单次持续使用行为数据识别时间心理账户，进而分析其非替代性时间分配行为规律。[结果/结论]在线健康社区用户回帖行为具有非替代性时间分配行为规律，源于工作用于政策了解与就业寻求以及源于休息用于求医问诊与药物使用的时间不会挪作他用，但发帖及单次持续使用行为的时间分配没有非替代性。 相似文献
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为向用户提供具有较高质量的网络原生数字资源检索服务,促进网络原生信息资源利用的优化和良性循环,针对网络原生数字资源劣多优少的特点,采用定量评价法,建立了包括文章总数、浏览次数、回帖数、下载次数、存在时间等指标在内的具有可操作性的评价指标体系,然后采用网络和实地两种调查方法求得各指标权重,并进行实证研究。 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
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以2004-2008年披露的R&D支出的602个中国上市公司为样本,实证研究了上市公司CEO任期与R&D强度的关系.研究结果表明,CEO的任期与R&D强度呈倒U型关系,且这一转折点发生在CEO任期约为7年的时候.不同年龄层次和教育层次的CEO,其任期与R&D强度的关系存在显著差异:年龄小于45岁的CEO,其任期与R&D强度显著正相关,年龄大于等于45岁的CEO,其任期与R&D强度显著负相关;学历低于本科的CEO,其任期与R&D强度呈倒U型关系,学历高于本科的CEO,其任期与R&D强度显著正相关,而学历为本科的CEO,其任期与R&D强度无显著关系. 相似文献
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《Information processing & management》2005,41(5):1243-1262
The analysis of contextual information in search engine query logs enhances the understanding of Web users’ search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic changes within a search session is an important branch of search engine user behavior analysis. The purpose of this study is to investigate the properties of a specific topic identification methodology in detail, and to test its validity. The topic identification algorithm’s performance becomes doubtful in various cases. These cases are explored and the reasons underlying the inconsistent performance of automatic topic identification are investigated with statistical analysis and experimental design techniques. 相似文献
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《Information processing & management》2019,56(4):1494-1505
This study aims to explore the relationships between Instagram user characteristics and color features of their photos. Based on the assumption that individuals who are similar in characteristics would exhibit a similar style in their social media photos, this study pays attention to color as one of the key elements of style. An online survey to 179 university students measured their Big Five personality traits, narcissism, life satisfaction, loneliness, attitude to Instagram, and gender, and their Instagram photos were analyzed in terms of colorfulness, color diversity, and color harmony. Total 25,394 photos were analyzed. Results suggest that (1) agreeableness was the most relevant user variable associated with all color features; (2) the color features of Instagram photos were different by gender; (3) neuroticism of Instagram users was negatively associated with the color harmony of their photos; (4) color diversity was negatively correlated with loneliness, and romantic loneliness in particular; (5) users’ attitude to Instagram and the color harmony of their photos were positively correlated; and (6) extraversion of Instagram users was positively correlated with the color diversity while openness was negatively correlated with the color diversity and color harmony of their photos. They reveal that the color features of Instagram photos are linked to the characteristics of their uploader. The present study contributes to the body of research on color preference and online self-presentation. It not only exemplifies how the stylometric approach can be adopted to SNS research but also presents the link between user characteristics and the features of their posts at pixel-level, including color. 相似文献
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When public events occur, users often generate a huge number of microblog entries and their online interactions with one another. Forwarding and commenting on posts contribute to the huge networks of topic and sentiment communication. This study constructs the topic and sentiment propagation maps of microblogging in the context of public events to visually explore the patterns of topic and sentiment propagation among stakeholders across different phases. To quantify the influence of topic and sentiment propagation, four indicators of “topic out-degree,” “topic variation degree,” “sentiment out-degree,” and “sentiment deviation degree” are proposed. We chose the child abuse case in the Beijing Red-Yellow-Blue (RYB) Kindergarten for our study. The positions of various stakeholders in the propagation paths and the relationship among stakeholders were revealed. Results indicate that the government and mainstream media have the greatest influence in terms of topic and sentiment propagation. Moreover, topic propagation was the most influential in the recession phase and the same can be said with sentiment propagation in the spreading phase. The findings can help the emergency management departments gain a better understanding of the propagation patterns of topics and emotions and the role of stakeholders in such phenomena to improve their emergency response ability. 相似文献
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《Information processing & management》2020,57(6):102313
Social media is widely used for sharing disaster-related information following natural disasters. Drawing on negativity bias theory, integrated crisis mapping model, and arousal theory, this study characterized the emotional responses of the public and tested the way emotional factors and influential users (with high numbers of followers and activeness) affect the number of reposts. Results indicated that after unpredictable earthquakes, the public showed negative responses, and negativity bias theory manifested especially when the posts came from influential users. During a typhoon or earthquake, the number of reposts grew as the number of anger-related words in posts increased. Anxiety- and typhoon-related posts from users with high numbers of followers negatively affected the number of reposts, whereas sadness-related posts had contrasting effects. These findings can help emergency managers formulate proper emotional response strategies after various natural calamities and help researchers test the abovementioned theories or models using real-word data from social media. 相似文献
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在社会化媒体时代,如何在创新社区的海量数据环境中识别出领先用户,是企业从创新社区中获取价值的关键问题。本文从语言风格的视角出发,首先通过内容分析法探索了创新社区中领先用户表达的典型语言风格特征,即成就需求、未来导向、积极情绪和集体主义。其次,收集了355名创新社区用户所发表的47310篇帖子,利用自动文本分析方法对其中包含的积极情绪、集体主义和未来导向语言风格进行了测量,并验证了这四种语言风格与用户领先性的关系。研究结果表明,创新社区用户生成内容中所表现的语言风格起到了“信号”的作用：除了集体主义之外,成就需求、未来导向和积极情绪的语言风格都与用户领先性有显著的正向关系,可以作为识别领先用户的有效指标。最后,讨论了基于语言风格的领先用户识别机制的理论意义和实践价值。 相似文献
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Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
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《Information processing & management》2023,60(2):103144
Currently, many software companies are looking to assemble a team of experts who can collaboratively carry out an assigned project in an agile manner. The most ideal members for an agile team are T-shaped experts, who not only have expertise in one skill-area but also have general knowledge in a number of related skill-areas. Existing related methods have only used some heuristic non-machine learning models to form an agile team from candidates, while machine learning has been successful in similar tasks. In addition, they have only used the number of candidates’ documents in various skill-areas as a resource to estimate the candidates’ T-shaped knowledge to work in an agile team, while the content of their documents is also very important. To this end, we propose a multi-step method that rectifies the drawbacks mentioned. In this method, we first pick out the best possible candidates using a state-of-the-art model, then we re-estimate their relevant knowledge for working in the team with the help of a deep learning model, which uses the content of the candidates’ posts on StackOverflow. Finally, we select the best possible members for the given agile team from among these candidates using an integer linear programming model. We perform our experiments on two large datasets C# and Java, which comprise 2,217,366 and 2,320,883 posts from StackOverflow, respectively. On datasets C# and Java, our method selects, respectively, 68.6% and 55.2% of the agile team members from among T-shaped experts, while the best baseline method only selects, respectively, 49.1% and 40.2% of the agile team members from among T-shaped experts. In addition, the results show that our method outperforms the best baseline method by 8.1% and 11.4% in terms of F-measure on datasets C# and Java, respectively. 相似文献
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领先用户在开放式创新社区中发挥着重要作用,成为产品创新的有力推动者。但网络环境下领先用户识别的研究刚刚兴起,相关研究非常缺乏。本文首先对开放式创新社区中领先用户特征进行理论研究,提出需求领先力、活跃表现力、社区影响力三大特征。然后基于改进的网络志方法,将定性的网络志和定量的数据分析结合,构建了识别领先用户的理论步骤。遵循社区选择原则,以知乎社区扫地机器人版块103位用户为研究对象,对用户的提问数、回答数等7个指标进行因子分析和聚类分析,识别出2位领先用户;并采用扎根理论,比较发帖内容和未来市场上扫地机器人新产品功能,发现领先用户提出的大部分建议会体现于未来新产品功能上,证明了领先用户识别方法的有效性。研究有助于完善领先用户识别方法,并为后续领先用户行为研究奠定坚实基础。 相似文献
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Prior literature suggests that social media users are increasingly experiencing social media fatigue. Only recently have scholars undertaken empirical studies to investigate its antecedents and outcomes to better understand the impact of fatigue on social media users. To further this understanding, the present study has conducted a cross-sectional survey with 1552 users. The Stress-Strain-Outcome (SSO) theoretical framework is applied to examine if privacy concerns, self-disclosure, parental mediation strategies, and decrement in academic performance due to social media use correlate with social media fatigue. Two forms of fatigue are considered, namely, fatigue due to social networking site (SNS) and mobile instant messaging (MIM) use. The study results suggest that privacy concerns, self-disclosure, parental encouragement and worry significantly and positively correlate with SNS and MIM fatigue. Parental permission and parental monitoring are either not or lowly associated with fatigue. In addition to this, SNS and MIM fatigue positively correlated with the tendency to experience academic decrement due to social media use. The antecedents and consequences of social media fatigue were similar for SNS and MIM users. Moreover, students perceived their parents to be more open to their MIM use, and they had higher self-disclosure in MIM than in SNS. The study concludes with significant implications for practitioners, policy makers as well as service designers. 相似文献
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【目的/意义】研究在线健康社区中用户参与行为和互动模式,了解用户从潜水者向贡献者转化的影响因
素,从而为在线健康社区可持续发展提供参考建议。【方法/过程】本文通过构建激励机制下用户之间、在线健康社
区与用户之间两类有限理性的博弈模型,计算复制动态方程并求解均衡稳定策略,探究用户参与行为转化的影响
因素。【结果/结论】研究表明,在线健康社区中用户参与行为转化与发帖成本、隐私成本以及读帖收益系数成负相
关,与发帖收益系数、其他用户贡献的知识量以及情感支持成正相关,激励机制的引入对用户参与行为转化有积极
促进作用。在对策建议方面,在线健康社区可以从平台设计、氛围营造、用户生成内容、激励机制引入和隐私保障
机制构建五个层面进行优化管理。【创新/局限】本研究将引入激励机制作为在线健康社区的选择策略,未探讨其他
策略因素对于用户参与行为的影响,后续研究可以加以改进。 相似文献