Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Santos Tiago, Walk Simon, Kern Roman, Strohmaier Markus, Helic Denis

Activity Archetypes in Question-and-Answer (Q8A) Websites—A Study of 50 Stack Exchange Instances

ACM Transactions on Social Computing, 2019

Journal
Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q&A) instances. However, not all instances and topics receive the same amount of attention, as some thrive and achieve selfsustaining levels of activity, while others fail to attract users and either never grow beyond being a smallniche community or become inactive. Hence, it is imperative to not only better understand but also to distilldeciding factors and rules that define and govern sustainable Q&A instances. We aim to empower communitymanagers with quantitative methods for them to better understand, control, and foster their communities,and thus contribute to making the Web a more efficient place to exchange information. To that end, we extract, model, and cluster a user activity-based time series from 50 randomly selected Q&A instances from theStack Exchange network to characterize user behavior. We find four distinct types of user activity temporalpatterns, which vary primarily according to the users’ activity frequency. Finally, by breaking down totalactivity in our 50 Q&A instances by the previously identified user activity profiles, we classify those 50 Q&Ainstances into three different activity profiles. Our parsimonious categorization of Q&A instances aligns withthe stage of development and maturity of the underlying communities, and can potentially help operatorsof such instances: We not only quantitatively assess progress of Q&A instances, but we also derive practicalimplications for optimizing Q&A community building efforts, as we, e.g., recommend which user types tofocus on at different developmental stages of a Q&A community
2018

Hasani-Mavriqi Ilire, Kowald Dominik, Helic Denis, Lex Elisabeth

Consensus Dynamics in Online Collaboration Systems

Journal of Computational Social Networks , Ding-Zhu Du and My T. Thai, Springer Open, 2018

Journal
In this paper, we study the process of opinion dynamics and consensus building inonline collaboration systems, in which users interact with each other followingtheir common interests and their social pro les. Speci cally, we are interested inhow users similarity and their social status in the community, as well as theinterplay of those two factors inuence the process of consensus dynamics. Forour study, we simulate the di usion of opinions in collaboration systems using thewell-known Naming Game model, which we extend by incorporating aninteraction mechanism based on user similarity and user social status. Weconduct our experiments on collaborative datasets extracted from the Web. Our ndings reveal that when users are guided by their similarity to other users, theprocess of consensus building in online collaboration systems is delayed. Asuitable increase of inuence of user social status on their actions can in turnfacilitate this process. In summary, our results suggest that achieving an optimalconsensus building process in collaboration systems requires an appropriatebalance between those two factors.
2016

Hasani-Mavriqi Ilire, Geigl Florian, Pujari Suhbash Chandra, Lex Elisabeth, Helic Denis

The Influence of Social Status and Network Structure on Consensus Building in Collaboration Networks

Social Network Analysis and Mining, Reda Alhajj, Springer Vienna, 2016

Journal
In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors influence the process of consensus dynamics. For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web. Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process. In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.
2012

Strohmaier M., Helic Denis, Benz D., Körner C., Kern Roman

Evaluation of Folksonomy Induction Algorithms

In the ACM Transactions on Intelligent Systems and Technology, 3(4), 2012, 2012

Journal
2010

Trattner Christoph, Strohmaier M., Helic Denis

The benefits and limitations of tag clouds as a tool for social navigation from a network-theoretic perspective

Journal of Universal Computer Science, 2010

Journal
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