New Framework Improves Uncertainty Estimation in Social Bot Detection

Global: New Framework Improves Uncertainty Estimation in Social Bot Detection

Researchers have introduced a novel Uncertainty Estimation for Social Bot Detection (UESBD) framework in a paper posted to arXiv in March 2025, aiming to quantify predictive uncertainty in bot detection models and address generalization challenges caused by distribution shifts.

Background

Social bot detection is considered essential for mitigating misinformation, online manipulation, and coordinated inauthentic behavior. Existing neural network‑based detectors often achieve high accuracy on benchmark datasets but tend to produce overconfident predictions when encountering out‑of‑distribution accounts, limiting their real‑world reliability.

Proposed Framework

The UESBD framework incorporates Robust Multi‑modal Neural Processes (RMNP) to enhance resilience against modality inconsistencies that bots may exploit for camouflage. RMNP first employs modality‑specific encoders to learn unimodal representations before applying attentive neural processes that encode each modality’s latent variables as Gaussian distributions.

Key Components

An evidential gating network is introduced to explicitly model the reliability of each modality, counteracting attempts by bots to borrow human‑like features that could create conflicting signals. The joint latent distribution is then derived using a generalized product of experts, which weights each modality according to its estimated reliability during fusion.

Prediction Mechanism

Final classifications are generated by drawing Monte Carlo samples from the joint latent distribution and passing them through a decoder, thereby producing both class predictions and associated uncertainty estimates.

Experimental Evaluation

Experiments conducted on three real‑world benchmark datasets demonstrate that RMNP improves both classification performance and uncertainty estimation compared with prior approaches, while maintaining robustness in scenarios where modalities provide contradictory information.

Implications and Future Directions

The findings suggest that incorporating uncertainty quantification and modality reliability assessment can lead to more trustworthy social bot detection systems, particularly in dynamic online environments. Researchers indicate plans to explore additional modalities and real‑time deployment strategies in subsequent work.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.