Personalized incremental users' engagement

López, Claudia; Farzan, Rosta; Brusilovsky, Peter

doi:10.1145/2389176.2389206

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…By showing that volunteers in human computation for citizen science projects behave very differently from each other, this study encourages the development of a component to manage the engagement of volunteers in such projects. This component would incorporate personalised engagement strategies (Fischer, 2001;López et al, 2012;Mao et al, 2013) derived from the volunteer engagement profiles uncovered in the present work. The component could also both monitor the contribution behaviour of each volunteer and, when necessary, automatically trigger a suitable engagement strategy.…”

Section: Discussionmentioning

confidence: 99%

“…The dimensions of engagement presented in the last section are helpful to framing the previous studies in engagement. There is an extensive body of work dealing with engagement in technology-mediated social participation systems (Kraut et al, 2010) such as wiki-based systems (Butler et al, 2002;Bryant et al, 2005;Butler et al, 2008;Schroer and Hertel, 2009;Preece and Shneiderman, 2009;Niederer and Van Dijck, 2010;Liu and Ram, 2011;Welser et al, 2011;Zhu et al, 2012), open source software projects (Hertel et al, 2003;Niederer and Van Dijck, 2010), and human computation for citizen science projects (Raddick et al, 2010;Rotman et al, 2012;López et al, 2012;Mao et al, 2013;Jennett et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

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Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects

Ponciano

Brasileiro

2014

HCj

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Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects

Ponciano

Brasileiro

2014

HCj

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Background

Tagarelli¹,

Interdonato²

2018

Mining Lurkers in Online Social Networks

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Time-aware analysis and ranking of lurkers in social networks

Tagarelli

Interdonato

2015

Soc. Netw. Anal. Min.

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Mining the silent members of an online community, also called lurkers, has been recognized as an important problem that accompanies the extensive use of online social networks (OSNs). Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in an OSN. However, they are limited to use only structural properties of the static network graph, thus ignoring any relevant information concerning the time dimension. Our goal in this work is to push forward research in lurker mining in a twofold manner: (i) to provide an in-depth analysis of temporal aspects that aims to unveil the behavior of lurkers and their relations with other users, and (ii) to enhance existing methods for ranking lurkers by integrating different time-aware properties concerning informationproduction and information-consumption actions. Network analysis and ranking evaluation performed on Flickr, FriendFeed and Instagram networks allowed us to draw interesting remarks on both the understanding of lurking dynamics and on transient and cumulative scenarios of time-aware ranking.

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Personalized incremental users' engagement

Cited by 3 publications

References 31 publications

Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects

Finding Volunteers' Engagement Profiles in Human Computation for Citizen Science Projects

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Time-aware analysis and ranking of lurkers in social networks

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