Abstract
In this chapter, we discuss main remarks and findings raised from the experimental evaluations of lurker rank methods conducted over several real-world OSNs, such as Twitter, FriendFeed, Flickr, Instagram, and Google+. We discuss how the ranking results produced by LurkerRank are effective in identifying and characterizing users at different grades of lurking. We also point out that LurkerRank solutions are correlated with data-driven rankings based on empirical influence. Then, we provide an in-depth analysis of aspects related to the time dimension, which aims to unveil the behavior of lurkers and their relations with other users. More specifically, we address a number of important research questions, including comparison of lurkers with other types of users (inactive users, newcomers, active users), lurkers’ responsiveness, evolution of lurking trends, and evolution of topical interests of lurkers.
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Tagarelli, A., Interdonato, R. (2018). Lurking Behavior Analysis. In: Mining Lurkers in Online Social Networks. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-00229-9_4
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DOI: https://doi.org/10.1007/978-3-030-00229-9_4
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