Attention Based Context Boosted Cyberbullying Detection in Social Media

2021 ◽  
Vol 2 (4) ◽  
pp. 418-433
Author(s):  
Nabi Rezvani ◽  
Amin Beheshti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Contextual Information ◽  
Language Model ◽  
Multiple Sources ◽  
Learner Model ◽  
Deep Architecture ◽  
Efficient Machine ◽  
Cyberbullying Detection ◽  
Textual Content

Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

2021 ◽  
Vol 4 (1) ◽  
pp. 01-26
Author(s):  
Muhammad Arif
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Social Media Networks ◽  
Current State ◽  
Art Research ◽  
Cyberbullying Detection

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2021 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

scholarly journals Social Media Cyberbullying Detection using Machine Learning

2019 ◽  
Vol 10 (5) ◽  
Author(s):  
John Hani ◽  
Mohamed Nashaat ◽  
Mostafa Ahmed ◽  
Zeyad Emad ◽  
Eslam Amer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Cyberbullying Detection

scholarly journals Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

2018 ◽  
Vol 44 (4) ◽  
pp. 833-858 ◽  
Author(s):  
Oana Cocarascu ◽  
Francesca Toni
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Deception Detection ◽  
Contextual Information ◽  
Small Data ◽  
Data Sets ◽  
Supervised Classifiers ◽  
Small Data Sets ◽  
Use Of Social Media ◽  
Textual Content

The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation–based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement toward a statement is a useful step toward determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.

scholarly journals Blockchain Leveraged Cyberbullying Preventing framework

2020 ◽  
Author(s):  
Md Anawar Hossen Wadud ◽  
Md Ashraf Uddin ◽  
Shamima Parvez ◽  
Mohammad Motiur Rahman ◽  
Ammar Alazab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Natural Language Processing Technique ◽  
Being Bullied ◽  
Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

Social media engagement, service complexity, and experiential quality in US hospitals

2020 ◽  
Vol 34 (6) ◽  
pp. 833-845 ◽  
Author(s):  
Youngsu Lee ◽  
Joonhwan In ◽  
Seung Jun Lee
Keyword(s):  
Social Media ◽  
Demographic Data ◽  
Positive Association ◽  
Customer Engagement ◽  
Service Firms ◽  
Multiple Sources ◽  
Cross Sectional ◽  
Content Type ◽  
Social Media Engagement ◽  
Experiential Quality

Purpose As social media platforms become increasingly popular among service firms, many US hospitals have been using social media as a means to improve their patients’ experiences. However, little research has explored the implications of social media use within a hospital context. The purpose of this paper is to investigate a hospital’s customer engagement through social media and its association with customers’ experiential quality. Also, this study examines the role of a hospital’s service characteristics, which could shape the nature of the interactions between patients and the hospital. Design/methodology/approach Data from 669 hospitals with complete experiential quality and demographic data were collected from multiple sources of secondary data, including the rankings of social media friendly hospitals, the Hospital Compare database, the Center for Medicare and Medicaid (CMS) cost report, the CMS impact file, the Healthcare Information and Management Systems Society Analytics database and the Dartmouth Atlas of Health Care. Specifically, the authors designed the instrumental variable estimate to address the endogeneity issue. Findings The empirical results suggest a positive association between a hospital’s social media engagement and experiential quality. For hospitals with a high level of service sophistication, the association between online engagement and experiential quality becomes more salient. For hospitals offering various services, offline engagement is a critical predictor of experiential quality. Research limitations/implications A hospital with more complex services should make efforts to engage customers through social media for better patient experiences. The sample is selected from databases in the US, and the databases are cross-sectional in nature. Practical implications Not all hospitals may be better off improving the patient experience by engaging customers through social media. Therefore, practitioners should exercise caution in applying the study’s results to other contexts and in making causal inferences. Originality/value The current study delineates customer engagement through social media into online and offline customer engagement. This study is based on the theory of customer engagement and reflects the development of mobile technology. Moreover, this research may be considered as pioneering in that it considers the key characteristics of a hospital’s service operations (i.e., service complexity) when discovering the link between customers’ engagement through a hospital’s social media and experiential quality.

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