Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
This work addresses the need for targeted interventions against fake news spread by distinguishing malicious from non-malicious intents, though it is incremental as it builds on existing DRL and LSTM methods.
The paper tackled the problem of classifying the intent behind fake news spreaders on social media by proposing a deep reinforcement learning framework that optimizes tweet representation through word removal, achieving 95% multi-class accuracy while reducing the number of selected words.
Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy.