Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking.

Media profiling offers valuable insights to enhance the objectivity and reliability of news coverage by providing comprehensive analysis, but the diversity in languages posed significant challenges to our identification of factuality and political bias of non-English sources. The limitation of existing media analysis research is its concentration on a singular high-resource language, and it hardly extends to languages beyond English. To address this, we introduce xMP, a dataset for zero-shot cross-lingual media profiling tasks. xMP’s cross-lingual test set encompasses 34 non-English languages and 18 language families, extending media profiling beyond English resources and allowing us to assess cross-lingual media profiling model performance. Additionally, we propose a method, named R-KAT, to enhance the model’s zero-shot cross-lingual transfer learning capability by building virtual multilingual embedding. Our experiments illustrate that our method improves the transferability of models in cross-lingual media profiling tasks. Additionally, we further discuss the performance of our method for different target languages. Our dataset and code are publicly available.

Social bots are automated programs designed to spread rumors and misinformation, posing significant threats to online security. Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots: a higher number of connected components weakens their collaborative capabilities, thereby reducing their proportion within the overall network. However, current social bot detection methods still make limited use of topological features. Furthermore, both graph neural network (GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations, and existing studies lack an effective fusion of multi-scale information. To address these issues, this paper proposes a topology-aware multi-scale social bot detection method, which jointly learns local and global representations through a co-training mechanism. At the local level, topological features are effectively embedded into node representations, enhancing expressiveness while alleviating the over-smoothing problem in GNNs. At the global level, a clustering attention mechanism is introduced to learn global node representations, mitigating the over-globalization problem. Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches. Our code is publicly available at (accessed on 27 October 2025).

Large Language Model (LLM)-driven social simulation offers a controllable approach for analyzing crisis responses, but existing work on online crises often emphasizes either user engagement prediction or opinion and eWOM evolution. This separation is insufficient for online firestorms, where crisis impact emerges from the coupling between what users express and how participation expands over time. In such events, responsibility attribution, KOL influence, electronic Word of Mouth (eWOM), and user engagement jointly shape collective reactions. To address this gap, we introduce the Crisis Response Interaction Simulation Pipeline (CRISP), a training-free LLM-driven multi-agent framework for online firestorm simulation. CRISP integrates an eWOM Perception module for responsibility attribution and attitude formation with an Engagement Mechanism for predicting participation under evolving KOL influence. Experiments on four heterogeneous Weibo online firestorms across beverage, automobile manufacturing, food service, and education domains show that CRISP reproduces major eWOM and engagement trajectories across different activity scales and interaction structures. Counterfactual interventions on emotional composition and responsibility attribution further produce directionally consistent responses, suggesting mechanism-level validity beyond trajectory fitting. These findings indicate that CRISP provides a framework for analyzing online firestorm evolution and evaluating crisis communication strategies in controllable simulation environments.

Social bots threaten the integrity of online social platforms by spreading misinformation, manipulating public opinion, and carrying out fraudulent activities. Detecting social bots is therefore not just a classification problem: looking ahead, a detector can also act as a discriminative component in agent-based social network generation, supplying realism-assessment signals for a “Generate–Detect–Explain–Refine” loop. Existing graph-based detection methods rely mainly on social relation graphs and do not explicitly treat behavioral similarity as a separate parallel graph branch. This paper proposes a dual-branch graph neural network for Twitter bot detection in which a heterogeneous social relation graph is accompanied by a sparse, conditionally active behavior-similarity auxiliary view; the paper’s main contributions are a competitive detector and a diagnostic protocol for attributing and repairing the value of that auxiliary view. The relation branch uses a Relational Graph Convolution Network to process a heterogeneous social relation graph, while the auxiliary branch applies a Graph Attention Network to a behavior similarity graph that links users with similar follower–following ratios, tweet type distributions, and post influence metrics. Experiments on TwiBot-22 show that the proposed model reaches 89.48 ± 0.07% accuracy and 78.11 ± 0.14% macro-F1 over five random seeds at a per-seed validation-calibrated decision threshold (89.92 ± 0.07% accuracy and 77.28 ± 0.31% macro-F1 at the default 0.5 threshold). Per-class and threshold analyses further show that the default operating point is precision-oriented on the minority bot class (bot recall 54.83 ± 1.60%), while operating near the validation-calibrated threshold (τ = 0.40) raises bot recall to 64.12 ± 1.31%. Ablation studies show that the semantic text embedding provides the largest single performance contribution and that the relation branch supplies the bulk of the graph signal. A degree-stratified analysis further shows that the behavior branch is functionally active on its ≈1% covered users—2.9× more prediction changes and 4.0× larger probability shifts than on uncovered nodes—where a validation-only subgroup threshold lifts macro-F1 from 51.85 to 67.17 without retraining, so that it can be turned into a deployable calibrated component.