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

Perna Diego, Interdonato Roberto, Tagarelli Andrea. 2018. Learning to lurker rank: An evaluation of learning-to-rank methods for lurking behavior analysis. Social Network Analysis and Mining, 8:39, 21 p.

Journal article ; Article de recherche ; Article de revue à comité de lecture
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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.

Mots-clés Agrovoc : Informatique, Participation communautaire, Analyse de données, Comportement

Mots-clés libres : Learning-to-rank, Behavior analysis, User engagement, Lurking behavior analysis

Classification Agris : U10 - Computer science, mathematics and statistics
C30 - Documentation and information
000 - Other themes

Champ stratégique Cirad : Hors axes (2014-2018)

Auteurs et affiliations

  • Perna Diego, University of Calabria (ITA)
  • Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA)
  • Tagarelli Andrea, University of Calabria (ITA) - auteur correspondant

Source : Cirad-Agritrop (

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