Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

You might also read

Related Articles

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

Sort by

Same author

Image forgery detection and localization using deep learning techniques.

Journal of forensic sciences·2026

Same author

<i>TextConvoNet</i>: a convolutional neural network based architecture for text classification.

Applied intelligence (Dordrecht, Netherlands)·2022

Same author

Towards a super-resolution based approach for improved face recognition in low resolution environment.

Multimedia tools and applications·2022

Same journal

Advancing minority stress detection with transformers: insights from the social media datasets.

Social network analysis and mining·2026

Same journal

Assessing mobile instant messenger networks with donated data.

Social network analysis and mining·2026

Same journal

Applying a panel network formation model to limited partnership matching in the private capital market.

Social network analysis and mining·2026

Same journal

C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data.

Social network analysis and mining·2025

Same journal

A Bayesian mixture model for Poisson network autoregression.

Social network analysis and mining·2025

Same journal

Comparing methods for creating a national random sample of twitter users.

Social network analysis and mining·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Predictive modeling for suspicious content identification on Twitter.

Surendra Singh Gangwar¹, Santosh Singh Rathore¹, Satyendra Singh Chouhan²

¹ABV-IIITM, Gwalior, India.

Social Network Analysis and Mining

|October 11, 2022

Summary

This summary is machine-generated.

This study developed models to detect spam and misinformation on Twitter using profile and content features. Machine learning techniques achieved over 90% accuracy in classifying malicious tweets, improving platform security.

Keywords:

Machine learning techniques Natural language processing Social network Suspicious content detection User-content features

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Aug 26, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Computer Science
Social Media Analysis
Cybersecurity

Background:

Twitter's popularity makes it a target for spammers spreading misinformation.
Identifying malicious content and user profiles on Twitter is a significant research challenge.

Purpose of the Study:

To develop and evaluate models for identifying and classifying spam and misinformation on Twitter.
To compare the effectiveness of profile-based, content-based, and hybrid features in spam detection.

Main Methods:

Collected and labeled a large Twitter dataset to create a spam detection corpus.
Extracted profile, content, and hybrid features from the dataset.
Applied machine learning, ensemble, and deep learning techniques to build prediction models.

Main Results:

Models utilizing profile, content, and hybrid features achieved high performance in tweet spam classification.
Accuracy, precision, recall, and F1-score measures exceeded 90% for most techniques.
Hybrid features combined with advanced learning techniques demonstrated superior spam detection capabilities.

Conclusions:

Profile, content, user, and hybrid features are effective for building robust spam detection models on Twitter.
Machine learning and deep learning approaches offer promising solutions for combating misinformation and spam on social media platforms.
The developed models contribute to enhancing the security and trustworthiness of the Twitter platform.