Growth Of Social Media Usage Research Articles

Cyberbullying or Just Sarcasm? Unmasking Coordinated Networks on Reddit

the rapid growth of social media usage, a common trend has emerged where users often make sarcastic comments on posts. While sarcasm can sometimes be harmless, it can blur the line with cyberbullying, especially when used in negative or harmful contexts. This growing issue has been exacerbated by the anonymity and vast reach of the internet, making cyberbullying a significant concern on platforms like Reddit. Our research focuses on distinguishing cyberbullying from sarcasm, particularly where online language nuances make it difficult to discern harmful intent. This study proposes a framework using natural language processi ng (NLP) and machine learning to differentiate between the two, addressing the limitations of traditional sentiment analysis in detecting nuanced behaviors. By analyzing a custom dataset scraped from Reddit, we achieved a 95.15% accuracy in distinguishing harmful content from sarcasm. Our findings also reveal that teenagers and minority groups are particularly vulnerable to cyberbullying. Additional ly, our research uncovers coordinated graphs of groups involved in cyberbullying, identifying common patterns in their behavior. This research contributes to improving detection capabilities for safer online communities.

Image-text crisis tweet categorization:a caption-based approach

The growth of social media usage this last decade has made available a massive and valuable volume of multimedia data. However, the lack of large multimodal annotated datasets, along with the inherent noise and the diversity of multimodal relations in this type of data presents challenges for machine learning methods. Unlike classic multimodal data, social media data comes with a large diversity of relations between image and text making the interaction between the two modalities more difficult.Previous research concentrated on fusion strategies with separate encoders for each modality. This paper introduces CMB (Caption-based Multimodal BERT), a method of classifying crisis-related social media posts by utilizing information from both images and texts. CMB translates the image modality into a text-compatible space, facilitating intermodal interaction. Furthermore, CMB presents training opportunities to enhance the model's robustness to missing modalities. Experimental results show that CMB is competitive with well-established, costly, and manually crafted multimodal models.

Adaptations of Dermatology Residency Programs to Changes in Medical Education Amid the COVID-19 Pandemic: Virtual Opportunities and Social Media

Background: The COVID-19 pandemic has caused a drastic change in the 2020-2021 residency application cycle, limiting how programs interact with applicants. Objective: To describe how dermatology residency programs have adapted by developing social media platforms and virtual opportunities. Methods: A list of participating programs was obtained from the Electronic Residency Application Service. Twitter, Instagram, Facebook, and websites were reviewed for virtual opportunities. The Visiting Student Application Service (VSAS) and the Dermatology Interest Group Association (DIGA) website were reviewed for virtual opportunities. Results: Of the 133 programs, 74 social media accounts were created. Twenty-two programs have Twitter, 27 have Instagram, and 25 have Facebook accounts. Virtual open houses were advertised on 27 program webpages. Eight virtual sub-internships were on VSAS. Eighty virtual meet and greets and 27 virtual electives were advertised on the DIGA website. Limitations: Considering the ongoing application cycle and the growth of social media usage, the numbers presented may not represent the numbers on the date of publication. Conclusion: Dermatology residency programs have adapted to the COVID-19 pandemic by developing social media platforms and virtual opportunities. There is an underutilization of social media by programs. Programs are working with the DIGA to distribute information about virtual opportunities.

Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

Ria Ricis and New Platform of Islamic Popular Culture

Not only change the landscape of popular culture, but the presence of social media also reshapes the structure and the agency. Nowadays, social media can turn ordinary people to celebrities. Using Instagram and YouTube, Ria Ricis has become a piety celebrity who shows her Islamic identity through Islamic performance by wearing the veil in a casual way and earns money from her uploaded videos in social media. Based on a case study of this figure, this paper raises questions related to Islamic popular culture in Indonesia: How does Indonesian define their public sphere currently amid the growth of social media usage? How does Indonesian Muslim respond to social media as a part of digital technology amidst Islamization in the post of an authoritarian regime? What is the possibility of tension for that young Indonesian Muslim as micro-celebrity while facing the three factors related, Islamic identities, enjoyment, and economic benefits? This paper argues that the new media platform has not only affected Indonesian Muslims’ lifestyles, but also the way in which they negotiate Islamic values, secular life, and economic interest. Keywords: Ria Ricis, Social Media, Islamic Popular Culture, and Digital Economy

A Neuro-fuzzy approach for user behaviour classification and prediction

Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

Regulating online advertising for gambling – once the genie is out of the bottle …

ABSTRACTThe article focuses on advertising for online gambling products on social media platforms and examines advertising practices from the viewpoint of consumer fairness. It shows how online advertising is fundamentally different from traditional advertising in print media, offline media sites (such as billboards) and broadcasting. The growth of social media usage has created an opportunity for online advertising to exploit ways of advertising which are only beginning to be understood fully and receive regulatory attention, and which, therefore, may exploit current regulatory loopholes. In this article, we identify two major issues in respect of online advertising of online gambling: first the potential for unethical placing of gambling advertising targeted at vulnerable users, and secondly the opaque use of commercial advertising in user-generated content on social media platforms. Having identified these two problems of gambling advertising, we take stock of how the existing regulatory structures deal with gambling advertising online, with a view to making recommendations on how to tackle these problems. We argue that data protection law and gambling regulation have not yet satisfactorily addressed these issues and that a much more radical approach is needed, as set out in the article.

Social Media Usage and Tertiary Students’ Academic Performance: Examining the Influences of Academic Self-Efficacy and Innovation Characteristics

The universal growth of social media usage among tertiary students has been linearly associated with academic performance. As social media use continues its constant growth, its application among tertiary students is inevitable. Its influence on academic performance turns out to be an ever more important question to think about. Researchers have mixed results, some found social media usage having little to no effect, and others found negative and positive effects on academic performance. Using a sample of 808 students in ten public tertiary institutions, this study makes an effort on how to deal with these differing outcomes and to investigate the effect of social media usage on tertiary students’ academic performance. We explored the relationship of the frequency of students’ use of social media for educational purposes and their academic performance, as measured by their cumulative grade point average (i.e., CGPA) with academic self-efficacy and innovation characteristics as mediator and moderator, respectively. The results revealed that social media usage for educational purposes positively related to academic performance. It also demonstrated that the use of social media can negatively affect academic performance. This study makes it more noticeable the effect of academic self-efficacy as a mediator in further improving the academic performance of students. Additionally, the empirical results of the study demonstrated that the moderating effect of innovation characteristics between social media usage and academic performance was stronger. The practical relevance of the study is to help governments, politicians, policy makers, students, educational institutions, and other stakeholders to carve specific policies, guidelines, and initiatives in support of social media usage as an innovative and effective tool for learning and sustainable academic performance.

‘Fake news’ meets tourism: a proposed research agenda

• This essay represents the first piece of research investigating the recent phenomenon of ‘fake news’ in the travel and tourism context. • ‘Fake news’ is deeply rooted in the on-line availability and provision of information and the growth of social media usage. • The tourism industry is highly associated with the issue as information represents the foundation of travel and tourism products. • A large focus of research on this should consider, particularly, how consumers are impacted by ‘fake news’. • Important consumer issues such as ethical and security implications should also be considered.

Quantity or quality? The professionalization of social media communication in Portugal and Germany: A comparison

As the worldwide growth of social media usage and institutionalization by organizations rise worldwide little is known about the degree of professionalization that has occurred. By comparing data between two asynchronous countries this research project offers insights into its strategic usage as well as discovering an interesting dynamic between activity and professionalization.