scholarly journals Going beyond simplicity: Using machine learning to predict belief in conspiracy theories

2021 ◽  
Author(s):  
Nils Brandenstein
Keyword(s):  
Machine Learning ◽  
Psychological Factors ◽  
Theoretical Explanation ◽  
Theory Building ◽  
Learning Models ◽  
Current Analysis ◽  
Conspiracy Theories ◽  
The Past ◽  
To Come ◽  
Machine Learning Models

Public and scientific interest in why people believe in conspiracy theories (CT) surged in the past years. To come up with a theoretical explanation, researchers investigated relationships of CT belief with psychological factors such as political attitudes, emotions or personality (van Prooijen & Douglas, 2018). However, recent studies put the robustness of these relationships into question (e.g., Stojanov & Halberstadt, 2020). In this study, the analysis of a representative dataset with 2025 adults uncovered that the simplicity of the current analysis routine, exhibiting high sample-specificity and neglecting complex associations of psychological factors and belief in CTs, may obscure these relationships. Further, poor replicability of CT belief associations can be detected and remedied by using a prediction-based modeling approach and machine learning models, which proposes a timely shift in the field’s analysis routine. Conceptual and theoretical implications for CT belief research and theory building are derived.

scholarly journals Collaboration Analytics — Current State and Potential Futures

2021 ◽  
Vol 8 (1) ◽  
pp. 1-12
Author(s):  
Bertrand Schneider ◽  
Nia Dowell ◽  
Kate Thompson
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Theory Building ◽  
Learning Models ◽  
Special Issue ◽  
Current State ◽  
Machine Learning Models

This special issue brings together a rich collection of papers in collaboration analytics. With topics including theory building, data collection, modelling, designing frameworks, and building machine learning models, this issue represents some of the most active areas of research in the field. In this editorial, we summarize the papers; discuss the nature of collaboration analytics based on this body of work; describe the associated opportunities, challenges, and risks; and depict potential futures for the field. We conclude by discussing the implications of this special issue for collaboration analytics.

Twitter Data Sentimental Analysis Using Multiple Classifications

2020 ◽  
Vol 17 (8) ◽  
pp. 3776-3781
Author(s):  
M. Adimoolam ◽  
Raghav Sharma ◽  
A. John ◽  
M. Suresh Kumar ◽  
K. Ashok Kumar
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Learning Models ◽  
The Past ◽  
Twitter Data ◽  
Learning Techniques ◽  
Machine Learning Models

In the past few decades human beings have knowledgeable tremendous intensification in the interaction in particular micro blogging websites and various social media as online resources. Many kinds of data have been used and classification data to group and store are challenging in this real world scenario. Various machine and Natural Language Processing (NLP) were being applied to analysis the sentiment. A major concentration of this work was on using several machine learning algorithms to perform sentimental analysis and comparing various machine learning models for the sentiment classification. This work analysed various sentimental using multiple classifications. From the evaluation of this experiment, it can be concluded that NLP and machine learning Techniques are efficient for sentimental analysis.

scholarly journals Opportunities and Challenges for Machine Learning in Materials Science

2020 ◽  
Vol 50 (1) ◽  
pp. 71-103
Author(s):  
Dane Morgan ◽  
Ryan Jacobs
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Materials Science ◽  
Molecular Simulations ◽  
Learning Models ◽  
Novel Materials ◽  
Domain Of Applicability ◽  
Rapid Changes ◽  
To Come ◽  
Machine Learning Models

Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas in which machine learning has recently had significant impact in materials science, and then we provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.

scholarly journals Machine learning-based analysis of adolescent gambling factors

2020 ◽  
Vol 9 (3) ◽  
pp. 734-743
Author(s):  
Wonju Seo ◽  
Namho Kim ◽  
Sang-Kyu Lee ◽  
Sung-Min Park
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Problem Gambling ◽  
Online Gambling ◽  
Easy Access ◽  
Support Vector ◽  
Gambling Problems ◽  
Learning Models ◽  
The Past ◽  
Machine Learning Models

AbstractBackground and aimsProblem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling.MethodsOf the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression.ResultsThe online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set.DiscussionThe results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling.ConclusionMachine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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