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

Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.9K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.9K
Statistical Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

1.3K
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
1.3K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Outliers and Influential Points01:08

Outliers and Influential Points

6.3K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.3K
Statistical Significance01:50

Statistical Significance

22.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.2K
Social Proof00:52

Social Proof

32.4K
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.
32.4K

You might also read

Related Articles

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

Sort by
Same author

Author Correction: Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

Scientific reports·2019
Same author

Inactivation of Bacillus stearothermophilus Spores in Soybean Water Extracts at Ultra-High Temperatures in a Scraped-Surface Heat Exchanger.

Journal of food protection·2019
Same author

Attitude and risk of substance use in adolescents diagnosed with Asperger syndrome.

Drug and alcohol dependence·2013
Same author

Specialty Care Programme for autism spectrum disorders in an urban population: A case-management model for health care delivery in an ASD population.

European psychiatry : the journal of the Association of European Psychiatrists·2011
Same author

Genetic variants associated with severe pneumonia in A/H1N1 influenza infection.

The European respiratory journal·2011
Same author

Intron retention as an alternative splice variant of the rat urocortin 1 gene.

Neuroscience·2006

Related Experiment Video

Updated: Feb 11, 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

5.8K

Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.

A Azcorra1,2, L F Chiroque3,4, R Cuevas1

  • 1Universidad Carlos III de Madrid, Leganés, Madrid, Spain.

Scientific Reports
|May 5, 2018
PubMed
Summary

A new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), identifies influential users on online social networks (OSNs). MUOD is scalable and classifies users by engagement, follower count, or infection capacity.

More Related Videos

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.6K
Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

12.5K

Related Experiment Videos

Last Updated: Feb 11, 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

5.8K
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.6K
Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

12.5K

Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Data Mining

Background:

  • Online Social Networks (OSNs) are vital for information dissemination and user engagement across various sectors.
  • Identifying influential users is crucial for marketing, politics, and product promotion on OSNs.
  • Existing methods may lack scalability or nuanced classification of user influence.

Purpose of the Study:

  • To introduce a novel unsupervised method for identifying influential users on large-scale OSNs.
  • To develop a scalable outlier detection technique for user influence analysis.
  • To classify influential users based on distinct features like engagement, reach, and propagation.

Main Methods:

  • Developed Massive Unsupervised Outlier Detection (MUOD), an unsupervised outlier detection algorithm.
  • Applied MUOD to a large dataset of approximately 400 million Google+ users.
  • Classified detected outliers into categories: shape, magnitude, and amplitude, based on their characteristics.

Main Results:

  • Successfully identified and automatically discriminated sets of outlier users on a large OSN.
  • Demonstrated MUOD's scalability for analyzing massive user datasets.
  • Highlighted that classified outlier users exhibit features aligning with different definitions of influence.

Conclusions:

  • MUOD provides an effective and scalable approach for identifying and classifying influential users on OSNs.
  • The method's ability to categorize outliers offers nuanced insights into different types of user influence.
  • This technique supports strategic leveraging of influential individuals in online environments.