Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Social Foundations of Self II: The Generalized Other01:20

Social Foundations of Self II: The Generalized Other

458
According to George Herbert Mead, as children progress beyond the game stage, they develop a more comprehensive understanding of societal rules and norms. This cognitive and social development enables them to internalize the expectations of the broader community, refining their ability to regulate behavior.Consistent participation in organized activities is crucial in helping children recognize that their actions are not isolated but contribute to a more significant, interconnected group...
458
Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

280
Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
280
Social Exchange Theory02:06

Social Exchange Theory

26.2K
We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
26.2K
Social Exchange Theory01:26

Social Exchange Theory

888
As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
888
Social Proof00:52

Social Proof

24.9K
Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.
24.9K
Causes of Social Behavior I: Actions and Characteristics of Individuals01:30

Causes of Social Behavior I: Actions and Characteristics of Individuals

477
The actions and characteristics of others heavily influence the causes of social behaviors. Emotional expressions serve as powerful social signals, shaping behaviors and interactions in significant ways. Whether through direct observation or subconscious processing, individuals constantly adjust their responses based on the emotions and attributes of those around them.Emotional Cues and Social ResponsesFacial expressions, tone of voice, and body language provide crucial emotional cues that...
477

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys.

Translational psychiatry·2026
Same author

Detecting changepoints in dynamical systems: Modeling time-varying transmission of seasonal influenza.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Using smartphone surveys to predict next-week suicide attempts.

Journal of psychopathology and clinical science·2026
Same author

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.

Statistical methods in medical research·2026
Same author

Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment.

IEEE transactions on affective computing·2026
Same author

Punctuated decline of human cooperation.

Nature·2026

Related Experiment Video

Updated: Apr 27, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

10.9K

A simple generative model of collective online behavior.

James P Gleeson1, Davide Cellai2, Jukka-Pekka Onnela3

  • 1Mathematics Applications Consortium for Science and Industry, Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland; james.gleeson@ul.ie.

Proceedings of the National Academy of Sciences of the United States of America
|July 9, 2014
PubMed
Summary

Recent software choices by millions of social networking site users are more influential than cumulative popularity in shaping collective online behavior. Temporal data modeling reveals this key aspect of user decision-making.

Keywords:
branching processescomplex systems

More Related Videos

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K
Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.6K

Related Experiment Videos

Last Updated: Apr 27, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

10.9K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K
Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.6K

Area of Science:

  • Computational Social Science
  • Network Science
  • Behavioral Economics

Background:

  • Online environments facilitate the study of individual behaviors and population-level outcomes.
  • Understanding collective behavior in digital spaces is crucial for various applications.

Purpose of the Study:

  • To introduce a generative model for collective behavior of social networking site users choosing software applications.
  • To identify the key mechanisms driving user adoption and collective online behavior.

Main Methods:

  • Developed a simple generative model incorporating user decision recency and application cumulative popularity.
  • Utilized observational data from millions of social networking site users.
  • Employed temporal data-driven modeling to analyze behavioral dynamics.

Main Results:

  • Models emphasizing recent application popularity over cumulative popularity accurately reproduced observed temporal dynamics.
  • Various model combinations were consistent with long-time behavior but failed to capture temporal nuances.
  • Temporal data modeling effectively distinguished between competing microscopic mechanisms.

Conclusions:

  • Recent user decisions are a stronger driver of collective online behavior than cumulative popularity.
  • Temporal data analysis is a powerful tool for uncovering hidden aspects of online collective behavior.
  • This study provides insights into the dynamics of user choice in digital environments.