| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
|
| Initial Amendment Date: | August 13, 2015 |
| Latest Amendment Date: | May 16, 2017 |
| Award Number: | 1535900 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Tracy Kimbrel
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $356,845.00 |
| Total Awarded Amount to Date: | $372,845.00 |
| Funds Obligated to Date: |
FY 2017 = $16,000.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
W5510 FRANKS MELVILLE MEMORIAL LIBRARY STONY BROOK NY US 11794-0001 (631)632-9949 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
Stony Brook NY US 11794-4400 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
Algorithms in the Field, Algorithmic Foundations |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Information, beliefs, diseases, technologies, and behaviors propagate through social interactions as a contagion. Understanding of how these contagions spread is crucial in encouraging beneficial and healthy behaviors and discouraging the ones that are destructive and damaging. Rigorous, mathematical understanding of complex social contagions is not just an abstraction, but will guide applications from healthcare to word-of-mouth advertising. The technical content of this project is inherently interdisciplinary, and its lessons will apply to related fields such as probability, economics, sociology, and statistical physics. The research efforts are integrated with the educational and outreach activities of the PIs, who have strong records of broadly disseminating cutting-edge research to high school, undergraduate, and graduate students through teaching, outreach programs, and personal mentoring.
This project will transform our understanding of social contagions by: 1) Developing a suite of technical tools to enable improved understanding of specific complex processes; 2) Determining how various parameters of cascade and social structure together impact the chances of a cascade's success or failure; and 3) Obtaining empirical evidence to both corroborate the theoretical findings, and uncover the space of realistic setting for certain parameters.
Many existing models of contagion assume that increasing the number of infected (or affected) neighbors marginally decreases the chance of infection. Many contagions, such as adoption of expensive new technology, fail to have this property, but instead have more complex rules for infection. This leads to different spreading behaviors even on the same networks. Motivated by sociology research findings, this project will greatly enhance our understanding of social contagions in three aspects. First this project will provide rigorous study of the spreading behavior of a simplified theoretical model called k-complex contagions and its interactions with structures in the underlying graph such as tie strength, unusually influential nodes, and community structures. Second, this project presents a general model for studying cascades that is both theoretically tractable and practically motivated. The general model generalizes most previous theoretical models of complex and simple contagions and includes homophily and environmental factors on cascades. Finally, this project will use post-hoc analysis as well as real world social experiments to verify the veracity of the model and fit the parameters in different settings.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Human activity is embedded in a network of social interactions, which can spread information, beliefs, diseases, technologies, and behaviors. A better understanding of these social interactions promises a better understanding of and the ability to influence a wide range of phenomena -- financial practices, healthy/unhealthy habits, and voting practices, to name a few.
Many cascade models have been proposed. In these models, there is a set of nodes on a network, and some set of these nodes start off "infected". Over time infected nodes may influence their neighbors to become infected. Centola and Macy in 2007 proposed to classify the myriad models of cascades by dividing them into just two categories: simple and complex. The differentiating feature of complex contagion, compared to simple contagions is that complex contagions require reinforcement from multiple social contacts, while simple contagions only require one social contact.
Simple and complex contagions differ substantially in how they spread in a social network. In simple contagions, there is no synergy. A node's influence is only eroded by other nodes. In contrast, in a complex cascade the marginal probability of being infected may increase as more neighbors are infected. As a consequence of that, simple contagions often spread fast in the network and such spreading is robust under the choices of parameters and network models. However, the spreading of complex contagion is much more delicate with fast spreading crucially depending on the network structures.
This project is devoted to a deeper understanding of the behavior of complex contagions and its dependency on a range of parameters: the choice of network models, the placement of initial seeds, and the choice of parameters in the contagion models. We have performed rigorous analysis on many well known network models such as the small world models and graph models with power law degree distribution. In addition, we also explicitly confirmed the existence of complex contagions, in charitable donation. We designed and ran controlled experiments on Mechanical Turk to observer how people respond to donation requests with different conditions on how others behaved. This confirmed that charitable donation is contagious -- that others donating will encourage one to donate -- and the decision of donating or not is a simple one, but which charity will receive the donation is a complex one. This is also the first time that complex contagion is explicitly observed and measured.
These findings are of theoretical importance to understanding complex contagion and of practical importance to many real world applications such as organizations wishing to maximize donations.
Last Modified: 12/16/2019
Modified by: Jie Gao
Please report errors in award information by writing to: awardsearch@nsf.gov.
