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              <text>An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media</text>
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              <text>CNN; Detection; Hate speech; Hybrid model; LSTM</text>
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              <text>Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanfords GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Nath N.; George J.P.; Kesan A.; Rodrigues A.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-462, pp. 347-355.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2022-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-19-2211-4_30" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-2211-4_30&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135007967&amp;amp;doi=10.1007%2F978-981-19-2211-4_30&amp;amp;partnerID=40&amp;amp;md5=13972f303d79c8e87bb7e04c29d9b447" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135007967&amp;amp;doi=10.1007%2f978-981-19-2211-4_30&amp;amp;partnerID=40&amp;amp;md5=13972f303d79c8e87bb7e04c29d9b447&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 23673370; ISBN: 978-981192210-7</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Nath N., CHRIST (Deemed to be University), Bengaluru, India; George J.P., CHRIST (Deemed to be University), Bengaluru, India; Kesan A., CHRIST (Deemed to be University), Bengaluru, India; Rodrigues A., CHRIST (Deemed to be University), Bengaluru, India</text>
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