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The dynamics of meaningful social interactions and the emergence of collective knowledge.

Dankulov MM, Melnik R, Tadić B - Sci Rep (2015)

Bottom Line: The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities.These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics.The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

View Article: PubMed Central - PubMed

Affiliation: 1] Department for Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia [2] Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia.

ABSTRACT
Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions &Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

No MeSH data available.


Related in: MedlinePlus

Tags-matching illustration and the activity patterns of users and tags in Mathematics.(a) Schematically shown a sequence of events with matching of tags (colored boxes) between actors’ expertise (displayed as a particular set of tags above blue circles—actors, Ui), the answers Aj, and questions Qj containing the tags of the related actor’s expertise. The direction of lines towards/outwards each actor indicates the process of reading/posting event. (b) Bipartite network of users (blue) and answers (red) at a favorite question (big red node). (c) Probability gi of posting a new question by the user i plotted against its total activity Ni, averaged over all users in the dataset. (d) The distributions of the interactivity time ΔT for users and tags. (e) The distribution of the user’s expertise entropy Si averaged over all users in the data. (f) Each point indicates the entropy related with the probability of the appearance of a particular tag along a sequence of m time intervals, where m is the tag’s frequency. Lower set of points represents the entropies for all tags computed from the sequence of events in the empirical data while the upper set is obtained from its randomized version.
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f1: Tags-matching illustration and the activity patterns of users and tags in Mathematics.(a) Schematically shown a sequence of events with matching of tags (colored boxes) between actors’ expertise (displayed as a particular set of tags above blue circles—actors, Ui), the answers Aj, and questions Qj containing the tags of the related actor’s expertise. The direction of lines towards/outwards each actor indicates the process of reading/posting event. (b) Bipartite network of users (blue) and answers (red) at a favorite question (big red node). (c) Probability gi of posting a new question by the user i plotted against its total activity Ni, averaged over all users in the dataset. (d) The distributions of the interactivity time ΔT for users and tags. (e) The distribution of the user’s expertise entropy Si averaged over all users in the data. (f) Each point indicates the entropy related with the probability of the appearance of a particular tag along a sequence of m time intervals, where m is the tag’s frequency. Lower set of points represents the entropies for all tags computed from the sequence of events in the empirical data while the upper set is obtained from its randomized version.

Mentions: In the process, which is schematically depicted in Fig. 1a, an actor (U) posts a question (Q), which may receive answers or comments (A) by other actors over time. Subsequently, new Q and the already present Q&A are subject to further answers, and so on. Representing each action by a directed link, this process co-evolves a bipartite network, where actors are one partition and Q&A form another partition. An example of a single-question network from the empirical data is shown in Fig. 1b. The cognitive content of each question is marked by up to 5 different tags, which thus specify the required expertise of the answering actors. Matching by at least one tag is required. The actor’s expertise is transferred to its answer. The excess expertise of the involved actors leads to the innovation262728 and an accumulation of expertise around a particular question. At the same time, it extends the sample space of matching events, thus accelerating the process in a self-organized manner.


The dynamics of meaningful social interactions and the emergence of collective knowledge.

Dankulov MM, Melnik R, Tadić B - Sci Rep (2015)

Tags-matching illustration and the activity patterns of users and tags in Mathematics.(a) Schematically shown a sequence of events with matching of tags (colored boxes) between actors’ expertise (displayed as a particular set of tags above blue circles—actors, Ui), the answers Aj, and questions Qj containing the tags of the related actor’s expertise. The direction of lines towards/outwards each actor indicates the process of reading/posting event. (b) Bipartite network of users (blue) and answers (red) at a favorite question (big red node). (c) Probability gi of posting a new question by the user i plotted against its total activity Ni, averaged over all users in the dataset. (d) The distributions of the interactivity time ΔT for users and tags. (e) The distribution of the user’s expertise entropy Si averaged over all users in the data. (f) Each point indicates the entropy related with the probability of the appearance of a particular tag along a sequence of m time intervals, where m is the tag’s frequency. Lower set of points represents the entropies for all tags computed from the sequence of events in the empirical data while the upper set is obtained from its randomized version.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4502430&req=5

f1: Tags-matching illustration and the activity patterns of users and tags in Mathematics.(a) Schematically shown a sequence of events with matching of tags (colored boxes) between actors’ expertise (displayed as a particular set of tags above blue circles—actors, Ui), the answers Aj, and questions Qj containing the tags of the related actor’s expertise. The direction of lines towards/outwards each actor indicates the process of reading/posting event. (b) Bipartite network of users (blue) and answers (red) at a favorite question (big red node). (c) Probability gi of posting a new question by the user i plotted against its total activity Ni, averaged over all users in the dataset. (d) The distributions of the interactivity time ΔT for users and tags. (e) The distribution of the user’s expertise entropy Si averaged over all users in the data. (f) Each point indicates the entropy related with the probability of the appearance of a particular tag along a sequence of m time intervals, where m is the tag’s frequency. Lower set of points represents the entropies for all tags computed from the sequence of events in the empirical data while the upper set is obtained from its randomized version.
Mentions: In the process, which is schematically depicted in Fig. 1a, an actor (U) posts a question (Q), which may receive answers or comments (A) by other actors over time. Subsequently, new Q and the already present Q&A are subject to further answers, and so on. Representing each action by a directed link, this process co-evolves a bipartite network, where actors are one partition and Q&A form another partition. An example of a single-question network from the empirical data is shown in Fig. 1b. The cognitive content of each question is marked by up to 5 different tags, which thus specify the required expertise of the answering actors. Matching by at least one tag is required. The actor’s expertise is transferred to its answer. The excess expertise of the involved actors leads to the innovation262728 and an accumulation of expertise around a particular question. At the same time, it extends the sample space of matching events, thus accelerating the process in a self-organized manner.

Bottom Line: The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities.These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics.The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

View Article: PubMed Central - PubMed

Affiliation: 1] Department for Theoretical Physics, Jožef Stefan Institute, Ljubljana, Slovenia [2] Scientific Computing Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia.

ABSTRACT
Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions &Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

No MeSH data available.


Related in: MedlinePlus