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Using Deep Learning for Temporal Forecasting of User Activity on Social Media: Challenges and Limitations

Published: 20 April 2020 Publication History

Abstract

The recent advances in neural network-based machine learning algorithms promise a revolution in prediction-based tasks in a variety of domains. Of these, forecasting user activity in social media is particularly relevant for problems such as modeling and predicting information diffusion and designing intervention techniques to mitigate disinformation campaigns. Social media seems an ideal context for applying neural network techniques, as they provide large datasets and challenging prediction objectives. Yet, our experiments find a number of limitations in the power of deep neural networks and traditional machine learning approaches in predicting user activity on social media platforms. These limitations are related to dataset characteristics due to temporal aspects of user behavior. This work describes the challenges we encountered while attempting to forecast user activity on two popular social interaction sites: Twitter and GitHub.

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  • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
  • (2023)VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318058610:4(1479-1490)Online publication date: Aug-2023
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          cover image ACM Conferences
          WWW '20: Companion Proceedings of the Web Conference 2020
          April 2020
          854 pages
          ISBN:9781450370240
          DOI:10.1145/3366424
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          Published: 20 April 2020

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          Cited By

          View all
          • (2025)Navigating user engagement and cultural transitions in entertainment technology and social media based on activity managementEntertainment Computing10.1016/j.entcom.2024.10079152(100791)Online publication date: Jan-2025
          • (2023)Modeling information diffusion in social media: data-driven observationsFrontiers in Big Data10.3389/fdata.2023.11351916Online publication date: 17-May-2023
          • (2023)VAM: An End-to-End Simulator for Time Series Regression and Temporal Link Prediction in Social Media NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.318058610:4(1479-1490)Online publication date: Aug-2023
          • (2023)Using SMOTE-based Data Augmentation for Social Media Time Series Prediction2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394544(893-898)Online publication date: 1-Oct-2023
          • (2022)Simulating New and Old Twitter User Activity with XGBoost and Probabilistic Hybrid Models2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00026(132-139)Online publication date: Dec-2022
          • (2022)Social media activity forecasting with exogenous and endogenous signalsSocial Network Analysis and Mining10.1007/s13278-022-00927-312:1Online publication date: 8-Aug-2022

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