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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Detection of Black Holes01:10

Detection of Black Holes

2.2K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.2K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

90.4K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
90.4K
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

682
The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
682
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.8K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Manual curation of chemosensory genes and antennal RNAseq reveals candidate genes associated with host preference in Culex pipiens assemblage members.

Genomics·2026
Same author

Strengthening Actions for Menstrual Health and Hygiene Interventions for Promotion of Women's Health in Nepal (SAMIP): Protocol for a Participatory Intervention Development Study Using Realist Synthesis, Human-Centered Design, Intervention Mapping, and Arts-Based Methods.

JMIR research protocols·2026
Same author

Between Tradition and Transformation: Visual Accounts of Menstrual Experiences in Rural Nepal Using Collaborative Filmmaking.

Community health equity research & policy·2026
Same author

A large language model-based tool for identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame.

Environmental health : a global access science source·2025
Same author

What Happens When [Terai] Girls Play? Understanding the Relationship Between Restrictive Gender Norms, Girls' Experiences of Playing Sport in South-Eastern Nepal, and the Factors That Influence Their Participation.

Journal of adolescence·2025
Same author

Domestic Violence Against Women in Nepal: A Systematic Review of Risk Factors.

Trauma, violence & abuse·2024

Related Experiment Video

Updated: Aug 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

212

Is hate speech detection the solution the world wants?

Sara Parker1, Derek Ruths1

  • 1School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|February 27, 2023
PubMed
Summary
This summary is machine-generated.

Automated hate speech detection is a key focus for machine learning (ML) researchers, but a significant disconnect exists with other stakeholders, risking progress in online safety.

Keywords:
hate speechmachine learningnatural language processing

More Related Videos

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

500
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K

Related Experiment Videos

Last Updated: Aug 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

212
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

500
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.6K

Area of Science:

  • Computational Social Science
  • Hate Speech Detection
  • Online Discourse Analysis

Background:

  • The machine learning (ML) community prioritizes automated hate speech detection for online behavior mitigation.
  • A notable gap exists between ML researchers' views and those of other key stakeholders regarding hate speech.
  • This disconnect may hinder the acceptance and adoption of automated detection tools.

Purpose of the Study:

  • To investigate how various stakeholders perceive the challenge of hate speech and the role of automated detection.
  • To analyze the discourse surrounding hate speech mitigation efforts by different groups.
  • To identify the extent of the disconnect between the ML community and other stakeholders.

Main Methods:

  • Development and application of a structured approach to dissect stakeholder discourses.
  • Analysis of discussions on hate speech from online platforms, governments, and non-profit organizations.
  • Comparative analysis of perspectives on automated hate speech detection.

Main Results:

  • A profound disconnect was identified between the computer science research community and other stakeholders concerning hate speech mitigation.
  • This divergence in understanding and approach poses a significant risk to progress in addressing online hate speech.
  • Stakeholder perspectives on the utility and implementation of automated hate speech detection vary considerably.

Conclusions:

  • Urgent steps are needed to bridge the gap between computational researchers and other stakeholders.
  • Fostering a unified, multistakeholder community is crucial for advancing online civil discourse.
  • Integrating diverse perspectives is essential for effective hate speech mitigation strategies.