Reviewing the Definitions of “Lurkers” and Some Implications for Online Research

Edelmann, Noella

doi:10.1089/cyber.2012.0362

Cited by 116 publications

(100 citation statements)

References 29 publications

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“…Silent users are also referred to as lurkers, since they gain benefit from information produced by others, by observing the user-generated communications at all stages (e.g., reading posts, watching videos, etc. ), but without significantly giving back to the community [15], [40].…”

Section: Cial Networkmentioning

confidence: 99%

Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks

Caliò

Interdonato

Pulice

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Research on influence maximization has often to cope with marketing needs relating to the propagation of information towards specific users. However, little attention has been paid to the fact that the success of an information diffusion campaign might depend not only on the number of the initial influencers to be detected but also on their diversity w.r.t. the target of the campaign. Our main hypothesis is that if we learn seeds that are not only capable of influencing but also are linked to more diverse (groups of) users, then the influence triggers will be diversified as well, and hence the target users will get higher chance of being engaged. Upon this intuition, we define a novel problem, named Diversity-sensitive Targeted Influence Maximization (DTIM), which assumes to model user diversity by exploiting only topological information within a social graph. To the best of our knowledge, we are the first to bring the concept of topology-driven diversity into targeted IM problems, for which we define two alternative definitions. Accordingly, we propose approximate solutions of DTIM, which detect a size-k set of users that maximizes the diversity-sensitive capital objective function, for a given selection of target users. We evaluate our DTIM methods on a special case of user engagement in online social networks, which concerns users who are not actively involved in the community life. Experimental evaluation on real networks has demonstrated the meaningfulness of our approach, also highlighting the opportunity of further development of solutions for DTIM applications.Index Terms-diversity-sensitive influence propagation, linear threshold diffusion model, social capital, lurking behavior analysis.Leveraging diversity for enhanced IM. While maximizing the advertising of a product, an organization also needs to minimize the incentives offered to those users who will reach out the target ones. This obviously raises the necessity of choosing a proper number k of seed users (i.e., initial influencers) to be detected, which corresponds to the budget constraint. Surprisingly, an important aspect that is often overlooked is that the success of a viral marketing process might depend not only on the size of the seed set but also on the diversity that is reflected within, or in relation to, the seed set. Intuitively, individuals that differ from each other in terms of kind (e.g., age, gender), socio-cultural aspects (e.g., nationality, race) or other characteristics, bring unique opinions, experiences, and perspectives to bear on the task at hand; moreover, in an OSN context, members naturally have different knowledge, community experience, participation motivation and shared information [7], [8], [9]. It is worth noticing that diversity has been generally recognized as a key-enabling dimension in data analysis, which is essential to enhance productivity, develop wiser crowdsourcing processes, improve user satisfaction in content recommendation based on novelty and serendipity, avoid information bubble effects, and ultimate...

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Section: Cial Networkmentioning

confidence: 99%

Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks

Caliò

Interdonato

Pulice

et al. 2018

IEEE Trans. Knowl. Data Eng.

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“…They need to be considered as an important factor in online research, starting with a clearer and more positive definition of lurking. It is also necessary to understanding why users decide to lurk, what activities lurkers engage in, and whether the online environment is more valuable by turning lurkers into posters, Edelmann (2013).…”

Section: Literature Reviewmentioning

confidence: 99%

The Relation between Popularity and Engagement on Facebook

Ruzal¹

2018

Preprint

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IntroductionIn this paper, we will attempt to find factors that encourage content contribution and crowdsourcing on Facebook groups. This platform for information for various topics is very much based on the factor of group members helping and contributing their own knowledge, information and experiences with others, usually strangers in the real life.We know there is the 99:1 rule that many suggest applies for online content contribution, and we can see this in the large majority of Facebook groups as well as any other platform, when a small portion of a given sample of people is the main contributor to the community. However, in some instances we see lurkers start to participate on content contribution. It can be a one-time participation or a gradual move to be a regular contributor. We will try to examine just that and see what are the factors that influence this behavior online.This understanding can lead to the interpretation of future behavior and prediction of it, leading to more productive conversations for users who seek information in groups. We can also hypothesis that those factors can be used on a larger scale by brand pages for user engagement on marketing campaigns. In all these studies we did not find a connection between the amount of friends or connection on SNS to the level of activity and participation. In our own study, we will attempt and analyze patterns in our selected online community and see if we identify motivators for sharing content as well as trying to map the users who contribute in the community as well as the lurkers. Leading to our research questions: what makes a group post attract engagement from other users? And, what is the distribution of lurkers and engaged users in the selected group? We also want to examine whether there is a relationship between amount of friends one has on the network and his likelihood of sharing.6 Research Question and HypothesisAs mentioned, in all the previous studies we have looked into, we did not find a study that looks into the link between the amount of friends or connections on SNS, and specifically Facebook, to the level of activity and participation. In our own study, we will attempt and analyze patterns in our selected online community and see if we identify motivators for sharing content as well as trying to map the users who contribute in the community as well as the lurkers. Leading to our research questions: what makes a group post attract engagement from other users? and, what is the distribution of lurkers and engaged users in the selected group? We also want to examine our main question, whether there is a relationship between amount of friends one has on the network and his likelihood of sharing content or commenting on other posts.The argument of this paper suggests that the engagement of a Facebook user on a certain group is directly correlated to his number of friends on the social network.The assumption is that the higher is the engagement of a user on a group, the higher will be his number of friends. This assumption is base...

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“…As well known in OSN community, a lurker is a silent user, who takes benefit from others' contributions without producing anything on their own, 9 that is the perfect identikit of a person/bot that could acquire the more possible knowledge for adversarial attack against users in a OSN.…”

Section: Motivating Examplementioning

confidence: 99%

Centrality in heterogeneous social networks for lurkers detection: An approach based on hypergraphs

Amato

Moscato

Picariello

et al. 2017

Concurrency and Computation

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Nowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure-based on the concept of "neighborhood" among users-that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in "interest-based" social networks. Some experiments using the Yelp dataset are discussed.

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Reviewing the Definitions of “Lurkers” and Some Implications for Online Research

Cited by 116 publications

References 29 publications

Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks

Topology-Driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks

The Relation between Popularity and Engagement on Facebook

Centrality in heterogeneous social networks for lurkers detection: An approach based on hypergraphs

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