scholarly journals On the challenges of predicting microscopic dynamics of online conversations

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
John Bollenbacher ◽  
Diogo Pacheco ◽  
Pik-Mai Hui ◽  
Yong-Yeol Ahn ◽  
Alessandro Flammini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Long Range ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Generative Models ◽  
Machine Learning Algorithms ◽  
Macroscopic Structure ◽  
Online Conversation ◽  
Near Future

AbstractTo what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. Our model’s limited performance in long-range predictions highlights the challenges faced by generative models due to the accumulation of errors.

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

The Ultimate Data Flow for Ultimate Super Computers-on-a-Chip

2021 ◽  
pp. 312-318
Author(s):  
Veljko Milutinović ◽  
Miloš Kotlar ◽  
Ivan Ratković ◽  
Nenad Korolija ◽  
Miljan Djordjevic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systolic Array ◽  
Data Flow ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Flexible Structure ◽  
Quantum Optical ◽  
Many Core ◽  
Fixed Structure ◽  
Near Future

This chapter starts from the assumption that near future 100BTransistor SuperComputers-on-a-Chip will include N big multi-core processors, 1000N small many-core processors, a TPU-like fixed-structure systolic array accelerator for the most frequently used machine learning algorithms needed in bandwidth-bound applications, and a flexible-structure reprogrammable accelerator for less frequently used machine learning algorithms needed in latency-critical applications. The future SuperComputers-on-a-Chip should include effective interfaces to specific external accelerators based on quantum, optical, molecular, and biological paradigms, but these issues are outside the scope of this chapter.

scholarly journals Towards scaling Twitter for digital epidemiology of birth defects

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Ari Z. Klein ◽  
Abeed Sarker ◽  
Davy Weissenbacher ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Birth Defects ◽  
Birth Defect ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Svm Classifier

Abstract Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes—the leading cause of infant mortality—could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms—feature-engineered and deep learning-based classifiers—that automatically distinguish tweets referring to the user’s pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the “defect” class and 0.51 for the “possible defect” class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.

Application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms

2021 ◽  
pp. 68-80
Author(s):  
Muhammad Umer Hashmi ◽  
Ngoc Duy Nguyen ◽  
Michael Johnstone ◽  
Kathryn Backholer ◽  
Asim Bhatti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Social Media Platforms

scholarly journals A Comparative Study of Supervised Machine Learning Algorithms for the Prediction of Long-Range Chromatin Interactions

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 985 ◽  
Author(s):  
Thomas Vanhaeren ◽  
Federico Divina ◽  
Miguel García-Torres ◽  
Francisco Gómez-Vela ◽  
Wim Vanhoof ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transcription Factors ◽  
Long Range ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Supervised Machine Learning ◽  
Chromatin Interaction ◽  
Gradient Boosting ◽  
Chromatin Interactions

The role of three-dimensional genome organization as a critical regulator of gene expression has become increasingly clear over the last decade. Most of our understanding of this association comes from the study of long range chromatin interaction maps provided by Chromatin Conformation Capture-based techniques, which have greatly improved in recent years. Since these procedures are experimentally laborious and expensive, in silico prediction has emerged as an alternative strategy to generate virtual maps in cell types and conditions for which experimental data of chromatin interactions is not available. Several methods have been based on predictive models trained on one-dimensional (1D) sequencing features, yielding promising results. However, different approaches vary both in the way they model chromatin interactions and in the machine learning-based strategy they rely on, making it challenging to carry out performance comparison of existing methods. In this study, we use publicly available 1D sequencing signals to model cohesin-mediated chromatin interactions in two human cell lines and evaluate the prediction performance of six popular machine learning algorithms: decision trees, random forests, gradient boosting, support vector machines, multi-layer perceptron and deep learning. Our approach accurately predicts long-range interactions and reveals that gradient boosting significantly outperforms the other five methods, yielding accuracies of about 95%. We show that chromatin features in close genomic proximity to the anchors cover most of the predictive information, as has been previously reported. Moreover, we demonstrate that gradient boosting models trained with different subsets of chromatin features, unlike the other methods tested, are able to produce accurate predictions. In this regard, and besides architectural proteins, transcription factors are shown to be highly informative. Our study provides a framework for the systematic prediction of long-range chromatin interactions, identifies gradient boosting as the best suited algorithm for this task and highlights cell-type specific binding of transcription factors at the anchors as important determinants of chromatin wiring mediated by cohesin.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

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