Sophisticated Machine Learning Approaches in Cybersecurity and Social Media

Current Trends in Cybersecurity and Social Media Analysis

The recent advancements in cybersecurity and social media analysis are significantly shifting towards leveraging advanced machine learning and natural language processing techniques to tackle complex issues such as disinformation, hate speech, and cyberbullying. The field is witnessing a surge in the development of models that integrate large language models (LLMs) with graph neural networks (GNNs) to enhance the accuracy of identifying key actors in underground forums and disinformation campaigns. Additionally, there is a growing emphasis on multi-task learning architectures that incorporate user-based information to improve hate speech detection, reflecting a move towards more personalized and context-aware solutions.

In the realm of social media, sentiment analysis and deep learning are being increasingly utilized to detect and mitigate cyberbullying, with models employing LSTM and BERT embeddings showing promising results. Furthermore, the detection of harmful content on social media platforms is being addressed through innovative architectures that combine deep neural networks with network immunization algorithms to effectively block toxic nodes and curb the spread of harmful content.

Noteworthy developments include the use of LLMs fine-tuned for domain-specific adaptation in identifying key hackers, and the integration of sentiment analysis with Transformer-based models to significantly improve hate speech detection. Additionally, the exploration of social bots through feature-based approaches is providing new insights into improving bot detection in social networks, while the application of LLMs like GPT-3.5 Turbo to combat hate speech on platforms like Twitter is demonstrating robust performance in classification tasks.

These advancements collectively underscore a trend towards more sophisticated, context-aware, and multi-faceted approaches in addressing the multifaceted challenges posed by cybersecurity threats and the proliferation of harmful content on social media.

Sources

EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums

Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy

Bridging Nodes and Narrative Flows: Identifying Intervention Targets for Disinformation on Telegram

Sentiment Analysis of Cyberbullying Data in Social Media

StopHC: A Harmful Content Detection and Mitigation Architecture for Social Media Platforms

Characteristics of Political Misinformation Over the Past Decade

VocalTweets: Investigating Social Media Offensive Language Among Nigerian Musicians

Exploring social bots: A feature-based approach to improve bot detection in social networks

A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information

1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of Language, Hate Speech, and Targets using LLMs

HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X

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