Transfer Learning for Harmful Content Detection
This book provides an in-depth exploration of the effectiveness of transfer learning approaches in detecting deceptive content (i.e., fake news) and inappropriate content (i.e., hate speech). The author first addresses the issue of insufficient labeled data by reusing knowledge gained from other nat...
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| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Editie: | 1st ed. 2025. |
| Reeks: | T-Labs Series in Telecommunication Services,
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| Online toegang: | https://doi.org/10.1007/978-3-032-00850-3 |
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| 100 | 1 | |a Mohtaj, Salar. |e author. |0 (orcid)0000-0002-0032-3833 |1 https://orcid.org/0000-0002-0032-3833 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Transfer Learning for Harmful Content Detection |h [electronic resource] / |c by Salar Mohtaj. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XVI, 105 p. 12 illus.). |b 9 illus. in color. | ||
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| 505 | 0 | |a Introduction -- Background -- The Impact of Pre-processing on Fake News Detection -- Transfer Learning for Harmful Content Detection -- Sentiment Analysis and Fake News Detection -- Outlook -- Conclusion. | |
| 520 | |a This book provides an in-depth exploration of the effectiveness of transfer learning approaches in detecting deceptive content (i.e., fake news) and inappropriate content (i.e., hate speech). The author first addresses the issue of insufficient labeled data by reusing knowledge gained from other natural language processing (NLP) tasks, such as language modeling. He goes on to observe the connection between harmful content and emotional signals in text after emotional cues were integrated into the classification models to evaluate their impact on model performance. Additionally, since pre-processing plays an essential role in NLP tasks by enriching raw data—especially critical for tasks with limited data, such as fake news detection—the book analyzes various pre-processing strategies in a transfer learning context to enhance the detection of fake stories online. Optimal settings for transferring knowledge from pre-trained models across subtasks, including claim extraction and check-worthiness assessment, are also investigated. The author shows that the findings indicate that incorporating these features into check-worthy claim models can improve overall model performance, though integrating emotional signals did not significantly affect classifier results. Finally, the experiments highlight the importance of pre-processing for enhancing input text, particularly in social media contexts where content is often ambiguous and lacks context, leading to notable performance improvements. Explores the effectiveness of transfer learning approaches in detecting deceptive and inappropriate content; Analyzes pre-processing strategies in a transfer learning context to enhance the detection of fake stories online; Investigates optimal settings for transferring knowledge from pre-trained models across subtasks. | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
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| 950 | |a Focus Bundle Electrical and Electronic Engineering (SpringerNature-85403) | ||