While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can effectively be ranked according to the link structure of an OSN graph. Although this approach has enabled researchers to overcome the lack of ground-truth data at a large scale, the complexity of the problem hints at the opportunity of learning from past lurking experiences as well as of using a variety of behavioral features, including any available, possibly platform-specific information on the activity and interaction of lurkers in an OSN. In this paper, we leverage this opportunity in a principled way, by proposing a machine-learning framework which, once trained on lurking/non-lurking examples from multiple OSNs, allows us to predict the ranking of unseen lurking behaviors, ultimately enabling the prioritization of user engagement tasks. Results obtained on 23 network datasets by state-of-the-art learning-to-rank methods, using different optimization and evaluation criteria, show the significance of the proposed approach.

Learning to lurker rank: an evaluation of learning-to-rank methods for lurking behavior analysis

Perna, Diego;Tagarelli, Andrea
2018

Abstract

While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can effectively be ranked according to the link structure of an OSN graph. Although this approach has enabled researchers to overcome the lack of ground-truth data at a large scale, the complexity of the problem hints at the opportunity of learning from past lurking experiences as well as of using a variety of behavioral features, including any available, possibly platform-specific information on the activity and interaction of lurkers in an OSN. In this paper, we leverage this opportunity in a principled way, by proposing a machine-learning framework which, once trained on lurking/non-lurking examples from multiple OSNs, allows us to predict the ranking of unseen lurking behaviors, ultimately enabling the prioritization of user engagement tasks. Results obtained on 23 network datasets by state-of-the-art learning-to-rank methods, using different optimization and evaluation criteria, show the significance of the proposed approach.
Behavior analysis; Learning-to-rank; Lurking; User engagement; Information Systems; Communication; Media Technology; Human-Computer Interaction; Computer Science Applications1707 Computer Vision and Pattern Recognition
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/290082
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