Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2018 | Book

Introduction Read chapter Read first chapter

Mining Lurkers in Online Social Networks

Principles, Models, and Computational Methods

Authors: Prof. Andrea Tagarelli, Dr. Roberto Interdonato

Publisher: Springer International Publishing

Book Series : SpringerBriefs in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This SpringerBrief brings order to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs) lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs.

All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate.

Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining.

While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields. Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and human-computer interaction will also find this brief useful research material .

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This chapter opens the brief by introducing the readers to its research subject. The chapter provides main motivations and implications for studying a number of problems related to the theme of this brief, which will be elaborated in the subsequent eight chapters. The chapter also clarifies the target audience of scope of this brief, and finally provides acknowledgements.
Andrea Tagarelli, Roberto Interdonato
Chapter 2. Background
Abstract
This chapter summarizes main literature and relating findings from social science and human-computer interaction research, focusing on: the different interpretations of lurking and related implications, the motivational factors underlying this kind of user behavior, and the main criteria to promote delurking of lurkers.
Andrea Tagarelli, Roberto Interdonato
Chapter 3. Characterization and Ranking of Lurkers
Abstract
In this chapter, we discuss computational approaches to identify and rank lurkers in online social networks. We begin with a formal definition of topology-driven lurking and a detailed description of a family of centrality methods specifically conceived for ranking lurkers solely based on network topology, namely LurkerRank. To better model dynamics of user behaviors, the Time-Aware LurkerRank models are also described. The chapter ends with the description of a learning-to-rank framework for lurker prediction and classification.
Andrea Tagarelli, Roberto Interdonato
Chapter 4. Lurking Behavior Analysis
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.
Andrea Tagarelli, Roberto Interdonato
Chapter 5. Pervasiveness of the Notion of Lurking in OSNs
Abstract
Identifying and mining lurkers finds application in a variety of OSNs other than social media platforms. In this chapter, we put evidence on the pervasiveness of the notion of lurking, utilizing collaboration networks and trust networks as two cases in point. As regards collaboration networks, we focus on a parallel between lurkers and vicarious learners, i.e., users who take “non-expert” roles such as apprentices or advisees. We illustrate how to model a vicarious-learning-oriented collaboration network and we describe a method to identify and rank vicarious learners on it, namely VLRank. The second part of the chapter is devoted to the study of relations between lurkers and trustworthy/untrustworthy users. Through an analysis on who-trusts-whom networks and social media networks, we clarify to what extent the general perception of lurkers as untrustworthy users is appropriate or not.
Andrea Tagarelli, Roberto Interdonato
Chapter 6. Delurking
Abstract
Encouraging lurkers to more actively participate in the OSN life, a.k.a. delurking, is desirable in order to make lurkers’ social capital available to other users. In this chapter, we discuss in detail the delurking problem and computational approaches to solve it. We first provide an overview of works focusing on user engagement methodologies to understand how users can be motivated to participate and contribute to the community living in a social environment. Then we concentrate on the presentation of algorithmic solutions to support the task of persuading lurkers to become active participants in their OSN.
Andrea Tagarelli, Roberto Interdonato
Chapter 7. Boundary Spanning Lurking
Abstract
The social boundary spanning theory explains how OSN users share and transfer their knowledge through the network. In this chapter, we consider two aspects related to the role of lurkers in boundary spanning contexts. In the first part, we concentrate on the relation between lurkers and OSN communities, discussing how the user’s capability of across-community boundary spanning can relate with the role s/he may take in the community, and to what extent lurkers match community-based bridge users. In the second part, we introduce the problem of alternate lurker-contributor behaviors, under a framework of multilayer network modeling cross-platform user behaviors, and describe solutions based on ranking methods.
Andrea Tagarelli, Roberto Interdonato
Chapter 8. Bringing Lurking in Game Theory
Abstract
In this chapter, we describe the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. A study carried out on different complex network models shows how the Lurker Game is suitable to model lurking dynamics, and how the adoption of rewarding mechanisms combined with the modeling of hypothetical heterogeneity of users’ interests may lead users in an online community towards a cooperative behavior.
Andrea Tagarelli, Roberto Interdonato
Chapter 9. Concluding Remarks and Challenges
Abstract
This chapter ends the brief offering a summary of the main topics discussed and providing suggestions for future research.
Andrea Tagarelli, Roberto Interdonato
Metadata
Title
Mining Lurkers in Online Social Networks
Authors
Prof. Andrea Tagarelli
Dr. Roberto Interdonato
Copyright Year
2018
Electronic ISBN
978-3-030-00229-9
Print ISBN
978-3-030-00228-2
DOI
https://doi.org/10.1007/978-3-030-00229-9

Premium Partner

    Image Credits