Non-linguistic Features for Cyberbullying Detection on a Social Media Platform Using Machine Learning

2019 ◽  
pp. 391-406 ◽  
Author(s):  
YuYi Liu ◽  
Pavol Zavarsky ◽  
Yasir Malik
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Linguistic Features ◽  
Social Media Platform ◽  
Media Platform ◽  
Cyberbullying Detection

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

scholarly journals Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 539-543
Author(s):  
Sonali Gaikwad ◽  
Tejashri Borate ◽  
Nandpriya Ashtekar ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

10.2196/21660 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21660
Author(s):  
Tavleen Singh ◽  
Kirk Roberts ◽  
Trevor Cohen ◽  
Nathan Cobb ◽  
Jing Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Behavior ◽  
Health Behaviors ◽  
Peer Interactions ◽  
Social Media Platform ◽  
Risky Health Behaviors ◽  
Textual Data ◽  
Social Media Platforms ◽  
Media Platform

Background Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. Objective The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. Methods We performed a systematic review of the literature in September 2020 by searching three databases—PubMed, Web of Science, and Scopus—using relevant keywords, such as “social media,” “online health communities,” “machine learning,” “data mining,” etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Conclusions Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

scholarly journals A Survey on Identification of Online Public Shaming Using Machine Learning Framework

2021 ◽  
pp. 430-433
Author(s):  
Prof. Priti Jorvekar ◽  
Sonali Gaikwad ◽  
Nandpriya Ashtekar ◽  
Tejashri Borate ◽  
Umadevi Lade
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Negative Impact ◽  
Daily Life ◽  
Irrelevant Information ◽  
Social Media Platform ◽  
Learning Framework ◽  
Huge Impact ◽  
Social Media Platforms ◽  
Media Platform

Social Media Platforms involve not millions but billions of users around the globe. Interactions on these easily available social media sites like Twitter have a huge impact on people. Nowadays, there is undesirable negative impact for daily life. These hugely used major platforms of communication have now become a great source of dispersing unwanted data and irrelevant information, Twitter being one of the most extravagant social media platform in our times, the topmost popular microblogging services is now used as a weapon to share unethical, unreasonable amount of opinions, media. In this proposed work the dishonouring comments, tweets towards people are categorized into 9 types. The tweets are further classifies into one of these types or non-shaming tweets towards people. Observation says out of the multitude of taking an interested clients who posts remarks on a specific occasion, lions share are probably going to modify the person in question. Moreover, it is not the nonshaming devotee who checks the increment quicker but of shaming in twitter.

scholarly journals General Purpose Opinion Mining System for Social Media Platform using Machine Learning Based Api

2019 ◽  
Vol 8 (4) ◽  
pp. 9727-9732
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Opinion Mining ◽  
Research Work ◽  
General Purpose ◽  
Machine Learning Techniques ◽  
Mining System ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

With the growth of technology there is lot of data available on the internet. Social media platform like Twitter, FaceBook,Google+,whats app,instagram etc are the platform that allow people to share and express their views, ideas, thoughts and experiences about any topics, post messages across the world. There are mainly two types of textual information available on social media platforms. One is fact and another next one is sentiments or more formally it can also called opinion. The social media is a platform where people gives their opinion regularly. These opinions may contain some factual information. For the analysis of sentiments we required some tools. Mostly text based mining is used for opinion mining. Text mining required lots of different tools and research work. This paper, provides a machine learning techniques for opinion calculation in Twitter..

scholarly journals Online Hate Network Spreads Malicious COVID-19 Content Outside the Control of Individual Social Media Platforms

2020 ◽  
Author(s):  
Nicolas Velasquez ◽  
Rhys Leahy ◽  
Nicholas Johnson Restrepo ◽  
Yonatan Lupu ◽  
Richard Sear ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Social Media ◽  
Topic Analysis ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

Abstract We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

scholarly journals Sentiment Analysis: Classifier and Summarizer

2021 ◽  
Vol 9 (VII) ◽  
pp. 3997-4016
Author(s):  
Ayushi Aggarwal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Unstructured Data ◽  
Learning Approaches ◽  
Social Media Platform ◽  
Basic Model ◽  
Media Platform

Sentiment Analysis and summarization has a large number of application that are useful for determining the sentiment of the text and summarizing a big text into a small paragraph of few lines. Thus it has become an important topic to work on and for fulling the requirements of the customer. It has also become an important topic for researchers to focus on, as it is highly demanded and beneficial in different fields of product, services and growth of the business. At present when 89.9% of people are using social media platform, they express their reviews, feelings, emotions and share their comments and some exciting activities of their life through social media platform, so it becomes very important to analyses them and classify them as positive or negative, this can be done with the help of sentiment analysis. Also, to find the summary of a big document with large amount of data summarizer is very useful as we can get the summary of a document in the favorable number of line. The basic model of sentimental analysis classify the word as positive as negative with the help of some machine learning approaches, which will help in improving the quality of product and providing the service to the customer for building up a healthy competition in market and keeping the goodwill of the business . It also displays the output in the form of graph whose data is taken from social media platform. Sentimental analysis also helps in getting the summary of the document by picking the lines containing the words having maximum repetitions. It has been found that sentiment analysis able to classify the positive sentence by giving the output as 1 and negative sentences as 0. The model which is being built also graphically represents the classifications of positive and negative words picked from the dataset and it’s also useful in summarizing a document Thus sentiment analysis comes out to be very important for classify the unstructured data on social media platform and so there is always a scope of building a better model which is more accurate and efficient.

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