TY - GEN
T1 - Uncovering pattern formation of information flow
AU - Zang, Chengxi
AU - Cui, Peng
AU - Song, Chaoming
AU - Zhu, Wenwu
AU - Wang, Fei
N1 - Funding Information:
The work is in part by supported by NSF IIS-1750326 and IIS-1716432, National Natural Science Foundation of China No. 61772304, No. 61521002, No. 61531006, No. U1611461, National Program on Key Basic Research Project (No. 2015CB352300), Beijing Academy of Artificial Intelligence (BAAI), the research fund of Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology, and the Young Elite Scientist Sponsorship Program by CAST.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/25
Y1 - 2019/7/25
N2 - Pattern formation is a ubiquitous phenomenon that describes the generation of orderly outcomes by self-organization. In both physical society and online social media, patterns formed by social interactions are mainly driven by information flow. Despite an increasing number of studies aiming to understand the spreads of information flow, little is known about the geometry of these spreading patterns and how they were formed during the spreading. In this paper, by exploring 432 million information flow patterns extracted from a large-scale online social media dataset, we uncover a wide range of complex geometric patterns characterized by a three-dimensional metric space. In contrast, the existing understanding of spreading patterns are limited to fanning-out or narrow tree-like geometries. We discover three key ingredients that govern the formation of complex geometric patterns of information flow. As a result, we propose a stochastic process model incorporating these ingredients, demonstrating that it successfully reproduces the diverse geometries discovered from the empirical spreading patterns. Our discoveries provide a theoretical foundation for the microscopic mechanisms of information flow, potentially leading to wide implications for prediction, control and policy decisions in social media.
AB - Pattern formation is a ubiquitous phenomenon that describes the generation of orderly outcomes by self-organization. In both physical society and online social media, patterns formed by social interactions are mainly driven by information flow. Despite an increasing number of studies aiming to understand the spreads of information flow, little is known about the geometry of these spreading patterns and how they were formed during the spreading. In this paper, by exploring 432 million information flow patterns extracted from a large-scale online social media dataset, we uncover a wide range of complex geometric patterns characterized by a three-dimensional metric space. In contrast, the existing understanding of spreading patterns are limited to fanning-out or narrow tree-like geometries. We discover three key ingredients that govern the formation of complex geometric patterns of information flow. As a result, we propose a stochastic process model incorporating these ingredients, demonstrating that it successfully reproduces the diverse geometries discovered from the empirical spreading patterns. Our discoveries provide a theoretical foundation for the microscopic mechanisms of information flow, potentially leading to wide implications for prediction, control and policy decisions in social media.
KW - Complex Pattern formation
KW - Data-driven Branching Process
KW - Social Media Analysis
KW - Structure of Information Flow
UR - http://www.scopus.com/inward/record.url?scp=85071181999&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071181999&partnerID=8YFLogxK
U2 - 10.1145/3292500.3330971
DO - 10.1145/3292500.3330971
M3 - Conference contribution
AN - SCOPUS:85071181999
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1691
EP - 1699
BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Y2 - 4 August 2019 through 8 August 2019
ER -