共查询到10条相似文献，搜索用时 234 毫秒
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【目的/意义】构建用户特征指标体系,对恐怖事件情境下微博信息与评论用户进行画像并比较有助于掌握参与恐怖事件讨论用户的特点,加强反恐舆论引导。【方法/过程】以"#巴塞罗那恐怖袭击#"话题下的所有微博及评论数据为例,使用基于相关性的LDA主题模型提取微博主题,从用户特征和文本特征两个角度构建指标体系,并采用两步聚类刻画微博用户特征,分析发布微博用户和评论用户的异同。【结果/结论】以往活跃度、影响力较高的用户在该事件中不一定拥有较高的影响力;原始微博用户的平均等级略低于评论用户,但其在该事件中的影响力高于评论用户;原始微博用户类型多于评论微博用户类型。 相似文献
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Socially similar social media users can be defined as users whose frequently visited locations in their social media histories are similar. Discovering socially similar social media users is important for several applications, such as, community detection, friendship analysis, location recommendation, urban planning, and anomaly user and behavior detection. Discovering socially similar users is challenging due to dataset size and dimensions, spam behaviors of social media users, spatial and temporal aspects of social media datasets, and location sparseness in social media datasets. In the literature, several studies are conducted to discover similar social media users out of social media datasets using spatial and temporal information. However, most of these studies rely on trajectory pattern mining methods or take into account semantic information of social media datasets. Limited number of studies focus on discovering similar users based on their social media location histories. In this study, to discover socially similar users, frequently visited or socially important locations of social media users are taken into account instead of all locations that users visited. A new interest measure, which is based on Levenshtein distance, was proposed to quantify user similarity based on their socially important locations and two algorithms were developed using the proposed method and interest measure. The algorithms were experimentally evaluated on a real-life Twitter dataset. The results show that the proposed algorithms could successfully discover similar social media users based on their socially important locations. 相似文献
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经济的不断发展,使人们对道路交通的需求不断增加,但有些因素却阻止了行人和非机动车驾驶员参与交通活动的范围,使他们参与交通活动的权力受到一定限制。通过分析我国非机动交通的交通环境,行人和非机动车驾驶员的交通需求及影响他们交通安全的风险因素,并提出了保障行人和非机动车驾驶员交通安全的措施。 相似文献
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用户教育与用户满意度 总被引：15，自引：0，他引：15
文章从调整用户期望、提高用户信息获取与处理能力和规范用户信息行为等三方面论述用户教育在提高用户满意度的重要作用，并由此归纳出现代用户教育具有行为理性化、成果适应性和手段对象化等特点。 相似文献
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在新产品开发过程中,用户作为价值创造者的作用已经受到学者和企业家的重视,领先用户区别于普通用户是更具有创新性的个人或者组织,对于企业的新产品开发具有更大的价值.分析了领先用户的内涵及形成条件、识别路径和吸纳领先用户参与新产品开发的方式,为企业有效利用领先用户资源提供了参考. 相似文献
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[目的/意义]旨在为用户提供个性化知识创新服务的特色数据库可持续发展新模式。[方法/过程]综合运用文献调研法、网络调查法,系统分析当前图书馆特色数据库建设发展中存在的问题,结合成功案例探讨用户导向下的特色数据库建设新模式。[结果/结论]图书馆必须创新建库理念,在政策的大力支持下,通过馆员的主动作为,积极构建用户导向下的找准用户、驱动用户、知识创新的特色数据库可持续发展模式。 相似文献
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研究男性与女性用户在社交媒体中造谣（传谣）和举报谣言行为上的差异性,有助于制定针对性的谣言消解措施。以新浪微博中已被证实为谣言的微博内容为数据源。采用列联卡方检验（Chi-squared test）检验了男性和女性用户在造谣（传谣）和举报谣言行为上的差异,同时通过隐含狄利克雷分布（Latent Dirichlet Allocation）对男女用户生成和举报的谣言内容进行了主题分类。结合列联卡方检验结果和主题分类结果,对社交媒体男女用户在造谣传谣和举报谣言行为上的差异进行了讨论。主要研究发现如下：造谣（传谣）行为方面,女性用户中造谣（传谣）的比率显著高于男性用户;举报谣言行为方面,男性用户中举报谣言的比率显著高于女性用户（x2=169.426,P<0.001）。两性用户均关注国际关系话题,但也存在差异性：男性用户造谣传谣的内容重点聚焦于国际关系方面,而女性造谣传谣的内容还体现在社会安全、食药安全和突发事件方面。在研究结果基础上,为社交媒体运营商提出了谣言消解措施。 相似文献
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针对创新社区日益增长的海量信息阻碍了用户对知识进行有效获取和创造的现状,将模糊形式概念分析（FFCA）理论应用于创新社区领先用户的个性化知识推荐研究。首先识别出创新社区领先用户并对其发帖内容进行文本挖掘得到用户——知识模糊形式背景,然后构建带有相似度的模糊概念格对用户偏好进行建模,最后基于模糊概念格和协同过滤的推荐算法为领先用户提供个性化知识推荐有序列表。以手机用户创新社区为例,验证了基于FFCA的领先用户个性化知识推荐方法的可行性,有助于满足用户个性化知识需求,促进用户更好地参与社区知识创新。 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