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Information Evolution in Social Networks

Published: 08 February 2016 Publication History

Abstract

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.

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    cover image ACM Conferences
    WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
    February 2016
    746 pages
    ISBN:9781450337168
    DOI:10.1145/2835776
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 08 February 2016

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    Author Tags

    1. evolution
    2. memes
    3. social computing
    4. social networks

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    WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining
    February 22 - 25, 2016
    California, San Francisco, USA

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    WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;
    Overall Acceptance Rate 498 of 2,863 submissions, 17%

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    • (2023)DccGraph: Detecting Criminal Communities with Augmented Criminal Network Construction and Graph Neural Network2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191121(1-8)Online publication date: 18-Jun-2023
    • (2023)The Interplay of Clustering and Evolution in the Emergence of Epidemics on NetworksICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279814(5582-5588)Online publication date: 28-May-2023
    • (2023)Stochastic evolution of bad memesPhysical Review E10.1103/PhysRevE.108.064103108:6Online publication date: 4-Dec-2023
    • (2023)Memecry: tracing the repetition-with-variation of formulas on 4chan/pol/Information, Communication & Society10.1080/1369118X.2023.2216769(1-32)Online publication date: 26-May-2023
    • (2023)Spreading processes with mutations over multilayer networksProceedings of the National Academy of Sciences10.1073/pnas.2302245120120:24Online publication date: 8-Jun-2023
    • (2022)Cognitive cascades: How to model (and potentially counter) the spread of fake newsPLOS ONE10.1371/journal.pone.026181117:1(e0261811)Online publication date: 7-Jan-2022
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