An improved mix framework for opinion leader identification in online learning communities
An improved mix framework for opinion leader identification in online learning communities
复制标题
在线学习社区中意见领袖识别的改进组合框架
DOI：
10.1016/j.knosys.2013.01.005
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发表时间：
2013-05-01
影响因子：
8.8
通讯作者：
Kinshuk
中科院分区：
文献类型：
--
作者：
Li, Yanyan;Ma, Shaoqian;Kinshuk
随着社交媒体的广泛采用，在线学习社区被视为由具有不同角色的相互关联的个人组成的知识网络。意见领袖在社交网络中很重要，因为他们有能力通过自己的上级地位、教育和社会声望影响他人的态度和行为。许多理论被提出来解释社交网络的形成、特征和持久性，但很少有人解决意见领袖识别问题。本文提出了一种改进的在线学习社区意见领袖识别的混合框架。实验研究验证了该框架的有效性。通过分析文本内容，用户行为和时间，该研究根据四个显著特征对意见领袖进行排名：专业知识，新奇，影响力和活动。此外，意见领袖的表现，进一步调查的长寿和中心性。在真实的数据集上的实验研究表明，该框架能够有效地识别在线学习社区中的意见领袖。(C)2013爱思唯尔有限公司版权所有。
With the widespread adoption of social media, online learning communities are perceived as a network of knowledge comprised of interconnected individuals with varying roles. Opinion leaders are important in social networks because of their ability to influence the attitudes and behaviours of others via their superior status, education, and social prestige. Many theories have been put forward to explain the formation, characteristics, and durability of social networks, but few address the issue of opinion leader identification. This paper proposes an improved mix framework for opinion leader identification in online learning communities. The framework is validated by an experimental study. By analysing textual content, user behaviour and time, this study ranked opinion leaders based on four distinguishing features: expertise, novelty, influence, and activity. Furthermore, the performances of opinion leaders were further investigated in terms of longevity and centrality. Experimental study on real datasets has shown that our framework effectively identifies opinion leaders in online learning communities. (C) 2013 Elsevier B.V. All rights reserved.