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 Exchange Theory02:06

Social Exchange Theory

39.4K
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).
39.4K
Social Exchange Theory01:26

Social Exchange Theory

345
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...
345
Nonconscious Mimicry01:13

Nonconscious Mimicry

5.1K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
5.1K
Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

157
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...
157
Relationship Formation02:12

Relationship Formation

45.2K
What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
45.2K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

196
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
196

You might also read

Related Articles

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

Sort by
Same author

An optimal federated learning-based intrusion detection for IoT environment.

Scientific reports·2025
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.5K

Distributed attention synchronization networks for advanced social media interaction analytics.

A Karunamurthy1

  • 1Department of MCA, Sri Manakula Vinayagar Engineering College, Puducherry, India. karunamurthy26@gmail.com.

Scientific Reports
|December 10, 2025
PubMed
Summary

We introduce Distributed Attention Synchronization Networks (DASN), a new framework for analyzing social media data. DASN enhances prediction accuracy and trend coherence across decentralized sources by modeling multi-scale dependencies.

Keywords:
Distributed attention synchronization networksGraph-attention fusionMulti-scale dependenciesReinforcement learningSocial media analyticsSynchronization protocolsTransformer-based architectures

More Related Videos

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.3K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.1K

Related Experiment Videos

Last Updated: Jan 9, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.5K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.3K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.1K

Area of Science:

  • Social Media Analytics
  • Distributed Systems
  • Machine Learning

Background:

  • Analyzing decentralized social media data presents challenges in maintaining coherence and modeling complex dependencies.
  • Existing methods often struggle with multi-scale temporal, contextual, and relational patterns across distributed sources.

Purpose of the Study:

  • To propose Distributed Attention Synchronization Networks (DASN), a novel framework for social media interaction analytics.
  • To effectively model multi-scale dependencies and maintain coherence across decentralized data sources.
  • To enhance prediction accuracy, trend coherence, and synchronization efficiency in social media analytics.

Main Methods:

  • DASN integrates transformer-based architectures with distributed synchronization protocols.
  • Key components include Dynamic Metric-Attention Transformer (DMAT), Reinforcement-Based Synchronization Controller (RBSC), and Graph-Attention Fusion Layer (GAFL).
  • The framework dynamically captures temporal, contextual, and relational patterns using adaptive attention and reinforcement learning for alignment.

Main Results:

  • DASN significantly improves prediction accuracy compared to state-of-the-art baselines.
  • The framework demonstrates enhanced trend coherence across decentralized datasets (Twitter, Reddit, Weibo).
  • Experimental results show superior synchronization efficiency in distributed social media analytics.

Conclusions:

  • DASN offers a robust solution for analyzing decentralized social media data.
  • The proposed framework advances both theoretical understanding and practical applications in distributed social media analytics.
  • DASN effectively models multi-scale dependencies and synchronizes information across diverse platforms.