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 Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

2021 ◽  
Vol 23 (4) ◽  
pp. 1-21
Author(s):  
Nureni Ayofe AZEEZ ◽  
Sanjay Misra ◽  
Omotola Ifeoluwa LAWAL ◽  
Jonathan Oluranti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Chi Square ◽  
Social Media Platforms ◽  
Bayes Algorithm ◽  
Use Of Social Media

The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.

scholarly journals Machine Learning Algorithms in Social Media: the Emergence of a Split Subject?

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Keldt Schoeman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Moral Agency ◽  
Industrial Revolution ◽  
Learning Algorithms ◽  
Physical Reality ◽  
Machine Learning Algorithms ◽  
Social Actions ◽  
Social Media Platforms ◽  
Scale Usage

Machine learning algorithms are the most common way in which most people interact with artificial intelligence. Wide scale usage of Machine learning has grown dramatically during the last decade, particularly within social media platforms. Considering the almost three billion monthly active users at Facebook and that most of their services rely heavily on machine learning, the aim of this essay is to investigate some of the social and moral implications of ML algorithms employed in social media. Guided by the adage ‘we shape our tools and then they shape us’ the common thread among several varied effects of social media was the outsourcing of important social actions from our physical reality to a virtual one. And, with current ML algorithms being successfully utilised to increase user time expenditure, social media platforms are likely to operate as an amplifier of social media effects i.e., greater time expenditure leads to greater amounts of important social actions outsourced to virtual reality. Now, considering that such extraordinary change as could be wrought by a fourth industrial revolution has historically been accompanied by change in the philosophical subject, it is not unreasonable to consider the possibility that change is occurring once more. Yet, I posit the view that we are currently in an intermediary phase between the physical and virtual realities, that we stand today as split subjects. For, while devices like our phones, consoles, watches and computers mean we are always on, many important social actions remain in the physical real. Though, even the effects of a partial transformation of the subject are substantial, as the kind of splitting many of us do today is reminiscent of compartmentalization, a psychologically significant coping mechanism known for its corrosion of moral agency. As such, with a potentially transient contemporary subject and a variety of associated effects the split subject is rich ground for further research.

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

scholarly journals Machine Learning-Based Detection of Spam Emails

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zeeshan Bin Siddique ◽  
Mudassar Ali Khan ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Irfan Mohiuddin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
South Asia ◽  
Social Communication ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Native Languages ◽  
Social Media Platforms ◽  
E Mail

Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu’s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.

scholarly journals A Review of Machine Learning Approach for Twitter Sentiment Analysis

2021 ◽  
Vol 24 (4) ◽  
pp. 52-58
Author(s):  
Mohammed W. Habib ◽  
◽  
Zainab N. Sultani ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Textual Data ◽  
Machine Learning Approach ◽  
Social Media Platforms

One of the active sciences or studies whose importance is rising is the science of sentiment analysis. The reason is due to the increasing sources of data that require investigation. Among the most valuable sources is Twitter, in addition to Facebook and other social media platforms. The objective of sentiment analysis is to classify sentiment/opinions of users as positive, negative, or neutral from textual data. This analysis is valuable for many applications that require understanding people's or users' opinions and emotions about a particular topic, product, or service. Several researchers tackle the problem of sentiment analysis using machine learning algorithms. In this paper, a comparative study is presented of various researches conducted a sentiment analysis on social media and especially on Tweets. The survey carried out in this paper provides an overview of preprocessing steps, machine learning algorithms, and approaches used for sentiment classification during the period 2015-2020.

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 Using Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat

2019 ◽  
Vol 9 (1) ◽  
pp. 5995-6000 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Intelligent System ◽  
Machine Learning Algorithms ◽  
Internet Technology ◽  
Social Media Platforms ◽  
Future Events ◽  
Global Terrorism ◽  
Terrorist Event ◽  
Global Terrorism Database

It is evident that there has been enormous growth in terrorist attacks in recent years. The idea of online terrorism has also been growing its roots in the internet world. These types of activities have been growing along with the growth in internet technology. These types of events include social media threats such as hate speeches and comments provoking terror on social media platforms such as twitter, Facebook, etc. These activities must be prevented before it makes an impact. In this paper, we will make various classifiers that will group and predict various terrorism activities using k-NN algorithm and random forest algorithm. The purpose of this project is to use Global Terrorism Database as a dataset to detect terrorism. We will be using GTD which stands for Global Terrorism Database which is a publicly available database which contains information on terrorist event far and wide from 1970 through 2017 to train a machine learning-based intelligent system to predict any future events that could bring threat to the society.

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.

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