Detection of misinformation on garlic and COVID-19 in Twitter: A machine learning-based approach (Preprint)

2021 ◽  
Author(s):  
Myeong Gyu Kim ◽  
Jae Hyun Kim ◽  
Kyungim Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Accurate Information ◽  
Probability Number

BACKGROUND Garlic-related misinformation is prevalent whenever a virus outbreak occurs. Again, with the outbreak of coronavirus disease 2019 (COVID-19), garlic-related misinformation is spreading through social media sites, including Twitter. Machine learning-based approaches can be used to detect misinformation from vast tweets. OBJECTIVE This study aimed to develop machine learning algorithms for detecting misinformation on garlic and COVID-19 in Twitter. METHODS This study used 5,929 original tweets mentioning garlic and COVID-19. Tweets were manually labeled as misinformation, accurate information, and others. We tested the following algorithms: k-nearest neighbors; random forest; support vector machine (SVM) with linear, radial, and polynomial kernels; and neural network. Features for machine learning included user-based features (verified account, user type, number of followers, and follower rate) and text-based features (uniform resource locator, negation, sentiment score, Latent Dirichlet Allocation topic probability, number of retweets, and number of favorites). A model with the highest accuracy in the training dataset (70% of overall dataset) was tested using a test dataset (30% of overall dataset). Predictive performance was measured using overall accuracy, sensitivity, specificity, and balanced accuracy. RESULTS SVM with the polynomial kernel model showed the highest accuracy of 0.670. The model also showed a balanced accuracy of 0.757, sensitivity of 0.819, and specificity of 0.696 for misinformation. Important features in the misinformation and accurate information classes included topic 4 (common myths), topic 13 (garlic-specific myths), number of followers, topic 11 (misinformation on social media), and follower rate. Topic 3 (cooking recipes) was the most important feature in the others class. CONCLUSIONS Our SVM model showed good performance in detecting misinformation. The results of our study will help detect misinformation related to garlic and COVID-19. It could also be applied to prevent misinformation related to dietary supplements in the event of a future outbreak of a disease other than COVID-19.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

scholarly journals Machine Learning Readmission Risk Modeling: A Pediatric Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Patricio Wolff ◽  
Manuel Graña ◽  
Sebastián A. Ríos ◽  
Maria Begoña Yarza
Keyword(s):  
Machine Learning ◽  
Multilayer Perceptron ◽  
Naive Bayes ◽  
Class Imbalance ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Distribution Model ◽  
Training Dataset ◽  
Support Vector ◽  
Pediatric Hospital

Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

scholarly journals Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms

2021 ◽  
Author(s):  
Rumana Rois ◽  
Manik Ray ◽  
Atikur Rahman ◽  
Swapan. K. Roy
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Logistic Regression ◽  
Prognostic Factors ◽  
University Students ◽  
Perceived Stress ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Support Vector

Abstract Background: Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these analyses were predominantly performed using the basic logistic regression model. As an alternative, we used the advanced machine learning approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students.Methods: This prevalence study surveyed 355 students from twenty-eight different Bangladeshi universities using questions concerning anthropometric measurements, academic, lifestyles, and health-related information, which referred to the perceived stress status of the respondents (yes or no). Boruta algorithm was used in determining the significant prognostic factors of the prevalence of stress. Prediction models were built using decision tree (DT), random forest (RF), support vector machine (SVM), and logistic regression (LR), and their performances were evaluated using parameters of confusion matrix, ROC curves, and k-fold cross-validation techniques. Results: One-third of university students reported stress within the last 12 months. Students’ pulse rate, systolic and diastolic blood pressures, sleep status, smoking status, and academic background were selected as the important features for predicting the prevalence of stress. Evaluated performance revealed that the highest performance observed from RF (accuracy=0.8972, precision=0.9241, sensitivity=0.9250, specificity=0.8148, AUC=0.8715, k-fold accuracy=0.8983) and the lowest from LR (accuracy=0.7476, precision=0.8354, sensitivity=0.8250, specificity=0.5185, AUC=0.7822, k-fold accuracy=07713) and SVM with polynomial kernel of degree 2 (accuracy=0.7570, precision=0.7975, sensitivity=0.8630, specificity=0.5294, AUC=0.7717, k-fold accuracy=0.7855). The RF model perfectly predicted stress including individual and interaction effects of predictors.Conclusion: The machine learning framework can be detected the significant prognostic factors and predicted this psychological problem more accurately, thereby helping the policy-makers, stakeholders, and families to understand and prevent this serious crisis by improving policy-making strategies, mental health promotion, and establishing effective university counseling services.

scholarly journals Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2464 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Chapi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Land Subsidence ◽  
Slope Angle ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Training Dataset ◽  
Validation Dataset ◽  
Support Vector ◽  
Susceptibility Map

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.

scholarly journals Detecting Spam Messages in Twitter Data by Machine learning Algorithms using Cross Validation

2019 ◽  
Vol 8 (12) ◽  
pp. 2941-2946
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Cross Validation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Human Relations ◽  
Detection Model ◽  
Social Media Networks ◽  
Twitter Data

Now a day’s human relations are maintained by social media networks. Traditional relationships now days are obsolete. To maintain in association, sharing ideas, exchange knowledge between we use social media networking sites. Social media networking sites like Twitter, Facebook, LinkedIn etc are available in the communication environment. Through Twitter media users share their opinions, interests, knowledge to others by messages. At the same time some of the user’s misguide the genuine users. These genuine users are also called solicited users and the users who misguidance are called spammers. These spammers post unwanted information to the non spam users. The non spammers may retweet them to others and they follow the spammers. To avoid this spam messages we propose a methodology by us using machine learning algorithms. To develop our approach used a set of content based features. In spam detection model we used Support vector machine algorithm(SVM) and Naive bayes classification algorithm. To measure the performance of our model we used precision, recall and F measure metrics.

scholarly journals Sentiment Analysis on Social Media using Machine Learning Approach

2021 ◽  
Author(s):  
Erick Omuya ◽  
George Okeyo ◽  
Michael Kimwele
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

Prediction of Dansgaard-Oeschger events using machine learning

2021 ◽  
Author(s):  
Nuno Moniz ◽  
Susana Barbosa
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Ice Core ◽  
Model Performance ◽  
Predictive Performance ◽  
Oxygen Isotopic Composition ◽  
Machine Learning Algorithms ◽  
Support Vector

<p>The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.<br>This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.<br>Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.</p>

scholarly journals Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1806
Author(s):  
Silvio Semanjski ◽  
Ivana Semanjski ◽  
Wim De Wilde ◽  
Sidharta Gautama
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Added Value ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Support Vector ◽  
Correlation Pattern ◽  
The Real

Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications.

scholarly journals Modeling the Settling Velocity of a Sphere in Newtonian and Non-Newtonian Fluids with Machine-Learning Algorithms

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Sayeed Rushd ◽  
Noor Hafsa ◽  
Majdi Al-Faiad ◽  
Md Arifuzzaman
Keyword(s):  
Machine Learning ◽  
Settling Velocity ◽  
Learning Algorithms ◽  
Newtonian Fluids ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Data Set ◽  
Traditional Procedure

The traditional procedure of predicting the settling velocity of a spherical particle is inconvenient as it involves iterations, complex correlations, and an unpredictable degree of uncertainty. The limitations can be addressed efficiently with artificial intelligence-based machine-learning algorithms (MLAs). The limited number of isolated studies conducted to date were constricted to specific fluid rheology, a particular MLA, and insufficient data. In the current study, the generalized application of ML was comprehensively investigated for Newtonian and three varieties of non-Newtonian fluids such as Power-law, Bingham, and Herschel Bulkley. A diverse set of nine MLAs were trained and tested using a large dataset of 967 samples. The ranges of generalized particle Reynolds number (ReG) and drag coefficient (CD) for the dataset were 10−3 < ReG (-) < 104 and 10−1 < CD (-) < 105, respectively. The performances of the models were statistically evaluated using an evaluation metric of the coefficient-of-determination (R2), root-mean-square-error (RMSE), mean-squared-error (MSE), and mean-absolute-error (MAE). The support vector regression with polynomial kernel demonstrated the optimum performance with R2 = 0.92, RMSE = 0.066, MSE = 0.0044, and MAE = 0.044. Its generalization capability was validated using the ten-fold-cross-validation technique, leave-one-feature-out experiment, and leave-one-data-set-out validation. The outcome of the current investigation was a generalized approach to modeling the settling velocity.

A Survey on Prediction of Suicidal Ideation Using Machine and Ensemble Learning

2019 ◽  
Author(s):  
Akshma Chadha ◽  
Baijnath Kaushik
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Suicidal Ideation ◽  
Ensemble Learning ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Machine Learning Algorithms ◽  
Support Vector

Abstract Suicide is a major health issue nowadays and has become one of the highest reason for deaths. There are many negative emotions like anxiety, depression, stress that can lead to suicide. By identifying the individuals having suicidal ideation beforehand, the risk of them completing suicide can be reduced. Social media is increasingly becoming a powerful platform where people around the world are sharing emotions and thoughts. Moreover, this platform in some way is working as a catalyst for invoking and inciting the suicidal ideation. The objective of this proposal is to use social media as a tool that can aid in preventing the same. Data is collected from Twitter, a social networking site using some features that are related to suicidal ideation. The tweets are preprocessed as per the semantics of the identified features and then it is converted into probabilistic values so that it will be suitably used by machine learning and ensemble learning algorithms. Different machine learning algorithms like Bernoulli Naïve Bayes, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Support Vector Machine were applied on the data to predict and identify trends of suicidal ideation. Further the proposed work is evaluated with some ensemble approaches like Random Forest, AdaBoost, Voting Ensemble to see the improvement.

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