Abstract
Reddit has emerged as a leading platform for microblogging data collection, providing valuable insights into patterns and knowledge discovery. However, the process of gathering and preparing this data presents significant challenges, particularly when it comes to ensuring its accuracy. Existing methodologies often yield an abundance of irrelevant posts, and datasets for relevance prediction on Reddit are rare. To overcome these obstacles, we propose a new semi-supervised framework that filters Reddit posts based on their topic relevance. Our approach combines annotated data from Twitter with weak labels generated from Wikipedia pages associated with relevant subreddits to automatically label Reddit posts. To enhance the model’s generalization performance, we utilize a domain adversarial adaptation network to bridge the distribution gap between Twitter and Reddit data. Our novel framework achieves an accuracy of 73% and an F1 score of 0.77, which is a significant improvement of 20% compared to baseline models. Additionally, we address important research questions regarding the effectiveness of automatic labeling, the use of weakly labeled data, the contextual requirements for training domain adaptation models, and the optimal weak labeling method.
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Acknowledgements
Research was sponsored by the DEVCOM Analysis Center and was accomplished under Cooperative Agreement Number W911NF-22-2-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Also supported, in part by the Temple University office of the Vice President for Research 2022 Catalytic Collaborative Research Initiative Program, AI & ML Focus Area.
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Gupta, S., Alshehri, J., Hai, A.A., Otudi, H., Obradovic, Z. (2024). From Tweets to Reddit: Leveraging Semi-supervised Domain Adaptation for Improving Data Filtering. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-63223-5_22
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