Social network analysis using deep learning: applications and schemes

Abbas, Ash Mohammad

doi:10.1007/s13278-021-00799-z

Cited by 21 publications

(4 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, according to [19], more than 41 articles have used contentbased characteristics to identify cyberbullying, indicating that this sort of information is critical for the job. However, semantic features generated from topic design model [20], word embeddings, and knowledge discovery [21] are increasingly being integrated with content-based attributes. In recent years, several experiments have been undertaken on the contribution of machine learning algorithms to social network data analysis.…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

show abstract

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The authors in [20] proposed a deep learning approach using LSTM for detecting hate speech on Twitter, demonstrating superior performance compared to shallow learning techniques. On the other hand, [21] used BiLSTM models for the same task, illustrating the effectiveness of bidirectional RNNs in capturing the context and semantics of text. Additionally, [22] used both LSTM and BiLSTM models to detect hate speech on Twitter, finding that the BiLSTM model outperformed its unidirectional counterpart.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Toktarova,

Syrlybay,

Myrzakhmetova

et al. 2023

IJACSA

View full text Add to dashboard Cite

Hate speech on social media platforms like Twitter is a growing concern that poses challenges to maintaining a healthy online environment and fostering constructive communication. Effective detection and monitoring of hate speech are crucial for mitigating its adverse impact on individuals and communities. In this paper, we propose a comprehensive approach for hate speech detection on Twitter using both traditional machine learning and deep learning techniques. Our research encompasses a thorough comparison of these techniques to determine their effectiveness in identifying hate speech on Twitter. We construct a robust dataset, gathered from diverse sources and annotated by experts, to ensure the reliability of our models. The dataset consists of tweets labeled as hate speech, offensive language, or neutral, providing a more nuanced representation of online discourse. We evaluate the performance of LSTM, BiLSTM, and CNN models against traditional shallow learning methods to establish a baseline for comparison. Our findings reveal that deep learning techniques outperform shallow learning methods, with BiLSTM emerging as the most accurate model for hate speech detection. The BiLSTM model demonstrates improved sensitivity to context, semantic nuances, and sequential patterns in tweets, making it adept at capturing the intricate nature of hate speech. Furthermore, we explore the integration of word embeddings, such as Word2Vec and GloVe, to enhance the performance of our models. The incorporation of these embeddings significantly improves the models' ability to discern between hate speech and other forms of online communication. This paper presents a comprehensive analysis of various machine learning methods for hate speech detection on Twitter, ultimately demonstrating the superiority of deep learning techniques, particularly BiLSTM, in addressing this critical issue. Our findings pave the way for further research into advanced methods of tackling hate speech and facilitating healthier online interactions.

show abstract

“…As the field progresses, deep reinforcement learning (DRL) [ 23 ] has introduced a novel paradigm in influence maximization, employing iterative learning strategies to optimize the identification of key influencers. Despite the promise of DRL in revolutionizing influence strategies, it encounters notable hurdles, including computational complexity and the extensive data requirements for model training.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

Bendahman,

Lotfi

2024

Entropy

View full text Add to dashboard Cite

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node’s immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network’s broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible–infected–recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model’s progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics.

show abstract

Social network analysis using deep learning: applications and schemes

Cited by 21 publications

References 95 publications

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

Contact Info

Product

Resources

About