Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Publisher | Association for Computing Machinery |
| Pages | 1691-1699 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781450362016 |
| DOIs | |
| State | Published - Jul 25 2019 |
| Event | 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States Duration: Aug 4 2019 → Aug 8 2019 |
Publication series
| Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|
Conference
| Conference | 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 |
|---|---|
| Country | United States |
| City | Anchorage |
| Period | 8/4/19 → 8/8/19 |
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Keywords
- Complex Pattern formation
- Data-driven Branching Process
- Social Media Analysis
- Structure of Information Flow
ASJC Scopus subject areas
- Software
- Information Systems
Cite this
Uncovering pattern formation of information flow. / Zang, Chengxi; Cui, Peng; Song, Chaoming; Zhu, Wenwu; Wang, Fei.
KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2019. p. 1691-1699 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Uncovering pattern formation of information flow
AU - Zang, Chengxi
AU - Cui, Peng
AU - Song, Chaoming
AU - Zhu, Wenwu
AU - Wang, Fei
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
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
ER -