Machine Learning in Detecting COVID-19 Misinformation on Twitter

Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of our daily lives. These social media platforms are effective tools for disseminating news, photos, and other types of information. In addition to the positives of the convenience of these platforms, they are often used for propagating malicious data or information. This misinformation may misguide users and even have dangerous impact on society’s culture, economics, and healthcare. The propagation of this enormous amount of misinformation is difficult to counter. Hence, the spread of misinformation related to the COVID-19 pandemic, and its treatment and vaccination may lead to severe challenges for each country’s frontline workers. Therefore, it is essential to build an effective machine-learning (ML) misinformation-detection model for identifying the misinformation regarding COVID-19. In this paper, we propose three effective misinformation detection models. The proposed models are long short-term memory (LSTM) networks, which is a special type of RNN; a multichannel convolutional neural network (MC-CNN); and k-nearest neighbors (KNN). Simulations were conducted to evaluate the performance of the proposed models in terms of various evaluation metrics. The proposed models obtained superior results to those from the literature.

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ClickbaitTR: Dataset for clickbait detection from Turkish news sites and social media with a comparative analysis via machine learning algorithms

Journal of Information Science ◽

10.1177/01655515211007746 ◽

2021 ◽

pp. 016555152110077

Author(s):

Şura Genç ◽

Elif Surer

Keyword(s):

Machine Learning ◽

Social Media ◽

Logistic Regression ◽

Random Forest ◽

Short Term Memory ◽

Ensemble Classifier ◽

Machine Learning Algorithms ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Clickbait is a strategy that aims to attract people’s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset – ClickbaitTR – with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.

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Fake News Detection using Machine Learning

ITM Web of Conferences ◽

10.1051/itmconf/20214003003 ◽

2021 ◽

Vol 40 ◽

pp. 03003

Author(s):

Prasad Kulkarni ◽

Suyash Karwande ◽

Rhucha Keskar ◽

Prashant Kale ◽

Sumitra Iyer

Keyword(s):

Machine Learning ◽

Social Media ◽

Data Extraction ◽

Fake News ◽

Detection Model ◽

News Websites ◽

Social Media Platforms ◽

Use Of Social Media ◽

Short Time ◽

The Given

Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.

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FNDNLSTM

10.4018/978-1-7998-8061-5.ch012 ◽

2021 ◽

pp. 218-232

Author(s):

Steni Mol T. S. ◽

P. S. Sreeja

Keyword(s):

Social Media ◽

Short Term Memory ◽

Classification Model ◽

Fake News ◽

Term Memory ◽

The Social ◽

Social Media Platforms ◽

Long Short Term Memory ◽

False News ◽

Model Technique

In the present scenario, social media platforms have become more accessible sources for news. Social media posts need not always be truthful information. These posts are widely disseminated with little regard for the truth. It is necessary to realize the evolution and origins of false news patterns in order to improve the progression of quality news and combat fake news on social media. This chapter discusses the most frequently used social media (Facebook) and the type of information exchanged to solve this issue. This chapter proposes a novel framework based on the “Fake News Detection Network – Long Short-Term Memory” (FNDN-LSTM) model to discriminate between fake news and real news. The social media news dataset is to be taken and preprocessed using the TF BERT model (technique). The preprocessed data will be passed through a feature selection model, which will select the significant features for classification. The selected features will be passed through the FNDN-LSTM classification model for identifying fake news.

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Language identification framework in code-mixed social media text based on quantum LSTM — the word belongs to which language?

Modern Physics Letters B ◽

10.1142/s0217984920500864 ◽

2020 ◽

Vol 34 (06) ◽

pp. 2050086 ◽

Cited By ~ 4

Author(s):

Shashi Shekhar ◽

Dilip Kumar Sharma ◽

M. M. Sufyan Beg

Keyword(s):

Machine Learning ◽

Social Media ◽

Social Networking Sites ◽

Quantum Dynamics ◽

Short Term Memory ◽

Neural Model ◽

Mixed Data ◽

Classification Code ◽

Social Media Platforms ◽

Media Texts

Machine learning (ML) architectures based on neural model have garnered considerable attention in the field of language classification. Code-mixing is a common phenomenon on social networking sites for exhibiting opinion on a topic. The code-mixed text is the approach of mixing two or more languages. This paper describes the application of the code-mixed index in Indian social media texts and compares the complexity to identify language at the word level using Bi-directional Long Short-Term Memory model. The major contribution of the work is to propose a technique for identifying the language of Hindi–English code-mixed data used in three social media platforms namely, Facebook, Twitter and WhatsApp. Here, we demonstrate that a special class of quantum LSTM network model is capable of learning and accurately predicting the languages used in social media texts. Our work paves the way for future applications of machine learning methods in quantum dynamics without relying on the explicit form of the Hamiltonian.

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COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Frontiers in Public Health ◽

10.3389/fpubh.2021.812735 ◽

2022 ◽

Vol 9 ◽

Author(s):

Zunera Jalil ◽

Ahmed Abbasi ◽

Abdul Rehman Javed ◽

Muhammad Badruddin Khan ◽

Mozaherul Hoque Abul Hasanat ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Social Networking Sites ◽

Daily Lives ◽

The Public ◽

Learning Techniques ◽

Social Media Platforms ◽

Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012055 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012055

Author(s):

H O Lekshmy ◽

Dhanyalaxmi Panickar ◽

Sandhya Harikumar

Keyword(s):

Machine Learning ◽

Random Forest ◽

Short Term Memory ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Seizure Prediction ◽

Short Term ◽

K Nearest Neighbors ◽

Term Memory ◽

Long Short Term Memory

Abstract Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.

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Detecting Fake News Using Social Media Platforms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38561 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1115-1120

Author(s):

Prof. B. J. Deokate

Keyword(s):

Machine Learning ◽

Social Media ◽

Random Forest ◽

Decision Tree ◽

Short Term Memory ◽

Fake News ◽

Online Social Media ◽

News Websites ◽

Computer Scientists ◽

Social Media Platforms

Abstract: Fake news detection is an interesting topic for computer scientists and social science. The recent growth of the online social media fake news has great impact to the society. There is a huge information from disparate sources among various users around the world. Social media platforms like Facebook, WhatsApp and Twitter are one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from these platforms is becoming a necessary and challenging task. This project proposes a machine learning method which can identify the credibility of an article that will be extracted from the Uniform Resource Locator (URL) entered by the user on the front end of a website. The project uses the five widely used machine learning methods: Long Short Term Memory (LSTM), Random Forest (random tree), Random Forest (decision tree), Decision Tree and Neural Network to give a response telling the user about the credibility of that news. Our initial definition of reliable and unreliable will rely on the human-curated data http://opensources.co. OpenSources.co has a list of about 20 credible news websites and a list of over 700 fake news websites. The proposed model is working well and defining the correctness of results upto 87.45% of accuracy. Keywords: Data Pre-processing, Fake news datasets, ML algorithms, Prediction.

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Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084069 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4069

Author(s):

Quyen G. To ◽

Kien G. To ◽

Van-Anh N. Huynh ◽

Nhung TQ Nguyen ◽

Diep TN Ngo ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Language Processing ◽

Short Term Memory ◽

Model Performance ◽

Vaccine Hesitancy ◽

Support Vector ◽

Short Term ◽

Long Short Term Memory ◽

Use Of Social Media

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽

10.3390/s21113678 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3678

Author(s):

Dongwon Lee ◽

Minji Choi ◽

Joohyun Lee

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Moving Average ◽

The Other ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Attention Model ◽

Long Short Term Memory ◽

Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

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