How can we manage Offensive Text in Social Media - A Text Classification Approach using LSTM-BOOST

Wadud, Md. Anwar Hussen; Kabir, Md. Mohsin; Mridha, M. F.; Ali, Mohsan; Hamid, Md. Abdul; Monowar, Muhammad Mostafa

doi:10.1016/j.jjimei.2022.100095

Cited by 41 publications

(13 citation statements)

References 43 publications

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“…By combining contextual embeddings from advanced models such as BERT with LSTM layers, a balance is achieved between model performance and generalization, which emphasizes the current research focus on developing more robust and adaptable text classification systems [34]. Figure 1 illustrates the concept of unrolling a loop in the context of RNNs [35], showcasing their efficiency in modeling sequential data, including textual content, with promising results; however, RNNs face challenges when long-term memory is crucial for problem-solving, such as in scenarios requiring the prediction of words in lengthy sentences where key information begins to fade as the sequence progresses. This situation can lead to a significant increase in the gap between necessary information and its point of use.…”

Section: Nlp Models: Rnn and Lstmmentioning

confidence: 99%

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

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As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

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Section: Nlp Models: Rnn and Lstmmentioning

confidence: 99%

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Alshattnawi,

Shatnawi,

AlSobeh

et al. 2024

Applied Sciences

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“…Adaptive boosting [16] (AdaBoost) is a common type of integrated learning algorithm boosting class, first applied to classification problems and gradually used in regression tasks as the algorithm evolves [17]. AdaBoost's key feature is its adaptability.…”

Section: Adaptive Enhancement Algorithmmentioning

confidence: 99%

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Wang,

Xu,

et al. 2024

Mathematics

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The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

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“…Despite these significant strides, the literature collectively points to persistent challenges in balancing high detection accuracy with context sensitivity, especially in linguistically diverse online environments. The nuances of language, everevolving use of lexicon, and cultural variations continue to complicate the landscape of offensive content detection [50]. This research gap necessitates continued exploration into advanced model architectures, like the proposed HDLA, that promise enhanced performance by synergizing various aspects of deep learning technology.…”

Section: E Deep Learning In Offensive Language Detectionmentioning

confidence: 99%

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Kazbekova,

Ismagulova,

Kemelbekova

et al. 2023

IJACSA

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In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.

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How can we manage Offensive Text in Social Media - A Text Classification Approach using LSTM-BOOST

Cited by 41 publications

References 43 publications

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection

Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

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