An attention-enhanced few-shot model for event detection in online social networks
Abstract The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. Howe...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97970-9 |
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| author | Sielvie Sharma Tanvir Ahmad Niyaz Ahmad Wani Naveed Ahmad Sadique Ahmad |
| author_facet | Sielvie Sharma Tanvir Ahmad Niyaz Ahmad Wani Naveed Ahmad Sadique Ahmad |
| author_sort | Sielvie Sharma |
| collection | DOAJ |
| description | Abstract The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. However, such research tasks require large scale labelled social network data, and annotating such vast amounts of data is nearly impractical and often considered unfeasible. In addition, established literature encounters significant hurdles when it comes to detect unseen or new events, even after acquiring a substantial amount of training data. This marks a new era, emphasizing the importance of leveraging minimal data and embracing broader generalizations, much like human understanding of information. To this end, we proposed AttendFew, a model to detect events in X (formely X) with limited data (Few-shot learning) which detects events while mitigating data dependency, even for unseen events, aligning with the current need for less data and greater adaptability. The proposed method encodes the posts (also known as “posts”) with BERTweet and Graph Attention Networks to capture both contextual and structural aspects of social network data and further, stacked attention will be applied to make the model attend local and global context effectively and enhances the ability to understand complex data of social network. Furthermore, the amalgamation of the feature score and Multi-layer Perceptron (MLP) facilitates class matching, aligning the derived features with their associated classifications. This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. This study represents an attempt to utilize a few-shot learning model in addressing the challenges posed by the sparsity and dynamism of online social networks for event detection. |
| format | Article |
| id | doaj-art-404b2a7d90d44519bbb92a98c80c683a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-404b2a7d90d44519bbb92a98c80c683a2025-08-20T02:55:21ZengNature PortfolioScientific Reports2045-23222025-04-0115111610.1038/s41598-025-97970-9An attention-enhanced few-shot model for event detection in online social networksSielvie Sharma0Tanvir Ahmad1Niyaz Ahmad Wani2Naveed Ahmad3Sadique Ahmad4School of Computer Science Engineering and Technology, Bennett UniversityDepartment of Computer Engineering, Jamia Millia IslamiaSchool of Computer Science and Engineering, IILM UniversityCollege of Computer and Information Science, Prince Sultan UniversityEIAS: Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan UniversityAbstract The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. However, such research tasks require large scale labelled social network data, and annotating such vast amounts of data is nearly impractical and often considered unfeasible. In addition, established literature encounters significant hurdles when it comes to detect unseen or new events, even after acquiring a substantial amount of training data. This marks a new era, emphasizing the importance of leveraging minimal data and embracing broader generalizations, much like human understanding of information. To this end, we proposed AttendFew, a model to detect events in X (formely X) with limited data (Few-shot learning) which detects events while mitigating data dependency, even for unseen events, aligning with the current need for less data and greater adaptability. The proposed method encodes the posts (also known as “posts”) with BERTweet and Graph Attention Networks to capture both contextual and structural aspects of social network data and further, stacked attention will be applied to make the model attend local and global context effectively and enhances the ability to understand complex data of social network. Furthermore, the amalgamation of the feature score and Multi-layer Perceptron (MLP) facilitates class matching, aligning the derived features with their associated classifications. This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. This study represents an attempt to utilize a few-shot learning model in addressing the challenges posed by the sparsity and dynamism of online social networks for event detection.https://doi.org/10.1038/s41598-025-97970-9Online social network analysisEvent detectionFew-shot learningBERTweetGAT |
| spellingShingle | Sielvie Sharma Tanvir Ahmad Niyaz Ahmad Wani Naveed Ahmad Sadique Ahmad An attention-enhanced few-shot model for event detection in online social networks Scientific Reports Online social network analysis Event detection Few-shot learning BERTweet GAT |
| title | An attention-enhanced few-shot model for event detection in online social networks |
| title_full | An attention-enhanced few-shot model for event detection in online social networks |
| title_fullStr | An attention-enhanced few-shot model for event detection in online social networks |
| title_full_unstemmed | An attention-enhanced few-shot model for event detection in online social networks |
| title_short | An attention-enhanced few-shot model for event detection in online social networks |
| title_sort | attention enhanced few shot model for event detection in online social networks |
| topic | Online social network analysis Event detection Few-shot learning BERTweet GAT |
| url | https://doi.org/10.1038/s41598-025-97970-9 |
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