The Competitive Influence Maximization (CIM) problem involves multiple
entities competing for influence in online social networks (OSNs). While Deep
Reinforcement Learning (DRL) has shown promise, existing methods often assume
users’ opinions are binary and ignore their behavior and prior knowledge. We
propose DRIM, a multi-dimensional uncertainty-aware DRL-based CIM framework
that leverages Subjective Logic (SL) to model uncertainty in user opinions,
preferences, and DRL decision-making. DRIM introduces an Uncertainty-based
Opinion Model (UOM) for a more realistic representation of user uncertainty and
optimizes seed selection for propagating true information while countering
false information. In addition, it quantifies uncertainty in balancing
exploration and exploitation. Results show that UOM significantly enhances true
information spread and maintains influence against advanced false information
strategies. DRIM-based CIM schemes outperform state-of-the-art methods by up to
57% and 88% in influence while being up to 48% and 77% faster. Sensitivity
analysis indicates that higher network observability and greater information
propagation boost performance, while high network activity mitigates the effect
of users’ initial biases.

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