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 IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

10
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...
10
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

17
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...
17

You might also read

Related Articles

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

Sort by
Same author

LaED: a novel lightweight, edge-aware and explainable deep learning model for privacy-preserving facial attendance tracking in resource-constrained educational environments.

Scientific reports·2026
Same author

Secure and flexible image watermarking using IWT, SVD, and chaos models for robustness and imperceptibility.

Scientific reports·2025
Same author

Author Correction: Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Scientific reports·2023
Same author

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Sensors (Basel, Switzerland)·2023
Same author

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data.

Computers in biology and medicine·2023
Same author

Detection of Ponzi scheme on Ethereum using machine learning algorithms.

Scientific reports·2023

Related Experiment Video

Updated: Oct 3, 2025

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

5.3K

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks.

Humaira Arshad1, Saima Abdullah1, Moatsum Alawida2

  • 1Department of Computer Sciences, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study introduces a multi-layer automation approach for online social network forensics, simplifying data collection and analysis for investigators. It proposes analysis operators to aid in drawing realistic conclusions from digital evidence.

Keywords:
automation toolsevidence analysisexperimental visualizationforensic applicationsforensic automationsemantic data presentationsocial network forensics

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Related Experiment Videos

Last Updated: Oct 3, 2025

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

5.3K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Area of Science:

  • Digital Forensics
  • Social Media Analysis
  • Legal Technology

Background:

  • Law enforcement and legal professionals increasingly use social media for investigations.
  • Challenges include heterogeneous data, unstructured formats, and privacy laws, leading to high investigator workloads.
  • Existing tools struggle with the complexity of online social network data for legal purposes.

Purpose of the Study:

  • To develop and present a multi-layer automation approach for online social network forensics.
  • To address technical and legal intricacies in collecting and analyzing social media data for legal use.
  • To provide tools that assist investigators in decision-making and drawing conclusions.

Main Methods:

  • A multi-layer automation framework for digital forensics on social networks.
  • Development of analysis operators based on domain correlations.
  • Implementation of operators using Twitter ontology.
  • Testing the approach through a case study.

Main Results:

  • A proof-of-concept for automated forensic analysis on online social networks.
  • Demonstration of a system that handles data collection to evidence analysis.
  • Validation of analysis operators for drawing realistic conclusions.

Conclusions:

  • Automating digital forensics on social media requires addressing both technical and legal/privacy challenges.
  • The proposed multi-layer automation approach offers a viable solution for streamlining investigations.
  • Analysis operators can enhance the efficiency and accuracy of forensic investigations on social networks.