scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

2021 ◽  
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

scholarly journals A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2664
Author(s):  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Abdul Razaque
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Online Social Networks ◽  
Hate Speech ◽  
Message Service ◽  
Integral Role ◽  
Social Media Platforms ◽  
Cyberbullying Detection

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Proactively Discouraging Cyberbullying Activities

2021 ◽  
Vol 9 (10) ◽  
pp. 1601-1607
Author(s):  
Puneetha KR
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Leisure Activities ◽  
Cyber Bullying ◽  
Future Research ◽  
Internet Users ◽  
The Social ◽  
Social Media Platforms ◽  
Root Word ◽  
Cyberbullying Detection

Abstract: Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. Cyber bullying is one of the most common problems faced by the internet users making internet a vulnerable space hence there has to be some detection that is needed on the social media platforms. Detecting the bullies online at the earliest makes sure that these platforms are safer for the user and internet indeed becomes a platform to share information and use it for other leisure activities. Even though there has been some research going on implementing detection and prevention of cyber bullying, it is not completely feasible due to certain limitations imposed. In this paper lexicon-based approach of the NLTK sentiwordnetis used to differentiate the positive and negative words and produce results. These words are given negative and positive values greater than or less than zero for positive and negative words respectively. Lexicon based systems utilize word lists and use the presence of words within the lists to detect cyberbullying. Lemmatization is used to find the root word. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in thisfield. Keywords: Abuse and crime involving computers, natural language processing, sentiment analysis, social networking

scholarly journals NOT EVEN THE SKY IS THE LIMIT: THE MEANINGS OF CONSUMPTION AND THE DYNAMICS OF SOCIAL MOBILITY ON THE @blogueiradebaixarenda PROFILE ON INSTAGRAM AND YOUTUBE

2020 ◽  
Vol 10 (3) ◽  
pp. 831-859
Author(s):  
Carla Barros
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Mobility ◽  
Low Income ◽  
Online Social Networks ◽  
Social Dynamics ◽  
Observational Research ◽  
Cultural Mediator ◽  
Consumption Practices ◽  
Social Media Platforms

Abstract The article sets out to explore the meanings surrounding consumption on the @blogueiradebaixarenda profile on the Instagram and YouTube online social networks, considering the perceptions of materiality and their articulations with the dynamics of social mobility. It analyses the elements making up the “low-income lifestyle” as a native category within the context of “digital influencers.” Through online observational research, the posts, hashtags and comments on both social media platforms were analysed, seeking to explore how consumption practices appear as mediators of social dynamics and identity constructs. Among the results, the articulations between materiality and social mobility, the idea of minimalism within the “low-income lifestyle” and the blogger’s status as a cultural mediator are highlighted.

scholarly journals Turn Threats into “Treatments”? Social Bots’ Possible Applications in Health Communication during Pandemics

2021 ◽  
Vol 2 (2) ◽  
pp. 281-288
Author(s):  
Zhenling Sun
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Health Communication ◽  
Online Social Networks ◽  
Negative Association ◽  
Study Analysis ◽  
Positive Role ◽  
Political Issues ◽  
Social Media Platforms ◽  
Cyber Space

COVID-19 pandemic is a global Crisis, social media platforms have been a significant site of getting information and arouse discussions. However, social bots have risen on the online social networks, social bots are applications that existing in cyber space merely and they can mimic human users to interact with you following their own logic, there are the features of “Intangible”、“personate” and “automatic”. Evidence suggests that social bots did harm to the Health Communication during COVID-19 pandemic, researchers found that social bots contributed to diffuse political issues stir negative emotions, spread rumor. Social bots often have a negative association, but there are many bots which perform benign tasks. This study analysis the reasons bots performed badly in COVID-19 pandemic first, then discuss about how to turn the “threats” to “treatments”, proving that social bots can act as a positive role in different periods of Health Emergencies.

scholarly journals Healthy Food on the Twitter Social Network: Vegan, Homemade, and Organic Food

2021 ◽  
Vol 18 (7) ◽  
pp. 3815
Author(s):  
Ladislav Pilař ◽  
Lucie Kvasničková Stanislavská ◽  
Roman Kvasnička
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Healthy Lifestyle ◽  
Healthy Food ◽  
Communication Analysis ◽  
Research Areas ◽  
Social Media Platforms

Online social networks have become an everyday aspect of many people’s lives. Users spend more and more time on these platforms and, through their interactions on social media platforms, they create active and passive digital footprints. These data have a strong potential in many research areas; indeed, understanding people’s communication on social media is essential for understanding their attitudes, experiences, behaviors and values. Researchers have found that the use of social networking sites impacts eating behavior; thus, analyzing social network data is important for understanding the meaning behind expressions used in the context of healthy food. This study performed a communication analysis of data from the social network Twitter, which included 666,178 messages posted by 168,134 individual users. These data comprised all tweets that used the #healthyfood hashtag between 2019 and 2020 on Twitter. The results revealed that users most commonly associate healthy food with a healthy lifestyle, diet, and fitness. Foods associated with this hashtag were vegan, homemade, and organic. Given that people change their behavior according to other people’s behavior on social networks, these data could be used to identify current and future associations with current and future perceptions of healthy food characteristics.

scholarly journals Automatic morphological analysis on the material of Russian social media texts

10.29007/dlff ◽  
2019 ◽  
Author(s):  
Alena Fenogenova ◽  
Viktor Kazorin ◽  
Ilia Karpov ◽  
Tatyana Krylova
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Morphological Analysis ◽  
State Of The Art ◽  
Test Set ◽  
The Social ◽  
Media Texts

Automatic morphological analysis is one of the fundamental and significant tasks of NLP (Natural Language Processing). Due to special features of Internet texts, as they can be both normative texts (news, fiction, nonfiction) and less formal texts (such as blogs and texts from social networks), the morphological tagging has become non-trivial and an actual task. In this paper we describe our experiments in tagging of Internet texts presenting our approach based on deep learning. The new social media test set was created, that allows to compare our system with state-of-the-art open source analyzers on the social media texts material.

scholarly journals Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram

10.2196/20794 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e20794
Author(s):  
Tim Ken Mackey ◽  
Jiawei Li ◽  
Vidya Purushothaman ◽  
Matthew Nali ◽  
Neal Shah ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Second Phase ◽  
The Public ◽  
Health Products ◽  
Social Media Platforms

Background The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel “infodemic,” including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable “cures.” Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19–related health care products from Twitter and Instagram. Methods This study is conducted in two phases beginning with the collection of COVID-19–related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. Results We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19–related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. Conclusions Results from this study provide initial insight into one front of the “infodemic” fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

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