An improved mix framework for opinion leader identification in online learning communities

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.