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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

Persistent fluctuations in answering activity in data and in simulations.(a) In the empirical dataset, time series of new users p(t), and time series of the number of answers and comments, and the number of events involving a particular tag (“calculus”). (b) The fluctuations F2(n) ~ nH around the local trend are plotted against the time interval n for time series in (a) as well as their trends, and time series involving a particular tag: LA—“linear algebra”, RA—“real algebra”, Prob—“Probability”. (c) and (d) Time series and their fluctuations in the simulations: time series of the number of all answers, and the number of answers containing a particular tag no.2, as well as series containing a particular combination of eight tags R2(8), one-tag, R5(1), and two tags combination, R100(2), all for the distribution of expertise ExpS, and the answers containing tag no.12, in the case of Exp1. Lines are shifted vertically for better display. On each line, the scaling region is indicated by a straight line, whose slope gives the displayed value of the exponent H within error bars ±0.009.
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f5: Persistent fluctuations in answering activity in data and in simulations.(a) In the empirical dataset, time series of new users p(t), and time series of the number of answers and comments, and the number of events involving a particular tag (“calculus”). (b) The fluctuations F2(n) ~ nH around the local trend are plotted against the time interval n for time series in (a) as well as their trends, and time series involving a particular tag: LA—“linear algebra”, RA—“real algebra”, Prob—“Probability”. (c) and (d) Time series and their fluctuations in the simulations: time series of the number of all answers, and the number of answers containing a particular tag no.2, as well as series containing a particular combination of eight tags R2(8), one-tag, R5(1), and two tags combination, R100(2), all for the distribution of expertise ExpS, and the answers containing tag no.12, in the case of Exp1. Lines are shifted vertically for better display. On each line, the scaling region is indicated by a straight line, whose slope gives the displayed value of the exponent H within error bars ±0.009.

Mentions: The quantitative measures displayed in Fig. 1(c–f) signify a highly cooperative process with the cognitive elements encoded by tags in the empirical dataset. Specifically, the entropy in Fig. 1f shows a distinctly non-random pattern of the appearance of each tag. In accordance with the entropy, the use of different contents shows temporal correlations. The distribution of time intervals between consecutive events with a particular tag ranges over five decades, Fig. 1d, suggesting a variety of roles that different cognitive elements play in the process. The dynamics of tags closely reflects the heterogeneity of the users’ activity profile and their expertise. Figure 1d also shows the broad distribution of the interactivity time of a particular user; the presence of a daily cycle is characteristic of online social dynamics1517. The long delays between actions of some users, contrasted with a frequent activity of others, yield the power-law distribution of the number of activities Ni per user (Fig. 3a in SI). Further, the role of each user in the process can be distinguished. For instance, in Fig. 1c, the probability for posting questions gi decays with the number of the user’s actions Ni. Essential for the cognitive process, however, is the broad range of the user’s expertise. As discussed in Methods, it is measured by the entropy distribution shown in Fig. 1e. While the majority expertise includes between one and four tags, few individuals have an activity record for a large number of topics. Consequently, the appearance of a particular combination of cognitive elements shows a complex pattern. All distinct combinations of tags found in the dataset obey Zipf’s law, see Fig. 2. It is a marked feature of scale-invariance in the collective dynamics2829. The ranking distribution of individual tags is also broad, Fig. 2 in SI. Furthermore, by directly inspecting the related time series, Figs 4 and 5, we find that an actively self-organized social process underlies the observed dynamics of cognitive elements.


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

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

Persistent fluctuations in answering activity in data and in simulations.(a) In the empirical dataset, time series of new users p(t), and time series of the number of answers and comments, and the number of events involving a particular tag (“calculus”). (b) The fluctuations F2(n) ~ nH around the local trend are plotted against the time interval n for time series in (a) as well as their trends, and time series involving a particular tag: LA—“linear algebra”, RA—“real algebra”, Prob—“Probability”. (c) and (d) Time series and their fluctuations in the simulations: time series of the number of all answers, and the number of answers containing a particular tag no.2, as well as series containing a particular combination of eight tags R2(8), one-tag, R5(1), and two tags combination, R100(2), all for the distribution of expertise ExpS, and the answers containing tag no.12, in the case of Exp1. Lines are shifted vertically for better display. On each line, the scaling region is indicated by a straight line, whose slope gives the displayed value of the exponent H within error bars ±0.009.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Persistent fluctuations in answering activity in data and in simulations.(a) In the empirical dataset, time series of new users p(t), and time series of the number of answers and comments, and the number of events involving a particular tag (“calculus”). (b) The fluctuations F2(n) ~ nH around the local trend are plotted against the time interval n for time series in (a) as well as their trends, and time series involving a particular tag: LA—“linear algebra”, RA—“real algebra”, Prob—“Probability”. (c) and (d) Time series and their fluctuations in the simulations: time series of the number of all answers, and the number of answers containing a particular tag no.2, as well as series containing a particular combination of eight tags R2(8), one-tag, R5(1), and two tags combination, R100(2), all for the distribution of expertise ExpS, and the answers containing tag no.12, in the case of Exp1. Lines are shifted vertically for better display. On each line, the scaling region is indicated by a straight line, whose slope gives the displayed value of the exponent H within error bars ±0.009.
Mentions: The quantitative measures displayed in Fig. 1(c–f) signify a highly cooperative process with the cognitive elements encoded by tags in the empirical dataset. Specifically, the entropy in Fig. 1f shows a distinctly non-random pattern of the appearance of each tag. In accordance with the entropy, the use of different contents shows temporal correlations. The distribution of time intervals between consecutive events with a particular tag ranges over five decades, Fig. 1d, suggesting a variety of roles that different cognitive elements play in the process. The dynamics of tags closely reflects the heterogeneity of the users’ activity profile and their expertise. Figure 1d also shows the broad distribution of the interactivity time of a particular user; the presence of a daily cycle is characteristic of online social dynamics1517. The long delays between actions of some users, contrasted with a frequent activity of others, yield the power-law distribution of the number of activities Ni per user (Fig. 3a in SI). Further, the role of each user in the process can be distinguished. For instance, in Fig. 1c, the probability for posting questions gi decays with the number of the user’s actions Ni. Essential for the cognitive process, however, is the broad range of the user’s expertise. As discussed in Methods, it is measured by the entropy distribution shown in Fig. 1e. While the majority expertise includes between one and four tags, few individuals have an activity record for a large number of topics. Consequently, the appearance of a particular combination of cognitive elements shows a complex pattern. All distinct combinations of tags found in the dataset obey Zipf’s law, see Fig. 2. It is a marked feature of scale-invariance in the collective dynamics2829. The ranking distribution of individual tags is also broad, Fig. 2 in SI. Furthermore, by directly inspecting the related time series, Figs 4 and 5, we find that an actively self-organized social process underlies the observed dynamics of cognitive elements.

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