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The ravage of malicious bots in online social networks has profoundly affected normal users. In this article, we propose a novel integrated framework to detect social bots. Specifically, social bot detection is performed from two dimensions: binary-class and fine-grained detection. Moreover, 35 features from three views are extracted to detect social bots, including eight newly defined features. Then, a category balancing based on resampling technology is designed to balance the training data. Finally, a divide-and-conquer strategy is integrated into Random Forest, and the interference of noise in the training process is reduced. Feature effectiveness evaluation found that extracting features from multi-views can describe bots more comprehensively. It is also noted from the category imbalance test that the balanced data set can prevent the detection result from tilting. Comparative experiments show that the integrated framework is more effective than the baseline both in social bot detection and the type detection of bots. [ABSTRACT FROM AUTHOR]

Dunhuang is a unique art treasure and a world heritage site. In order to organise and manage Dunhuang cultural heritage resources, this article studies the classification of Dunhuang murals in different dynasties, and explores the topic distribution characteristics and evolution rules of them. First, image features are extracted through scale-invariant feature transform (SIFT) and Canny and scale-invariant feature transform (CSIFT), a visual dictionary is generated through the k -means clustering algorithm, and the term frequency–inverse document frequency (TF-IDF) vector is calculated and combined with the colour feature vector extracted via hue, saturation and value (HSV). Second, Dunhuang mural images are collected and the support vector machine (SVM) classifier is built. Finally, the knowledge graph-based topic maps are constructed, and graph theory is introduced to analyse the topic distribution and evolution of Dunhuang murals in different dynasties. The results show that the Dunhuang murals of different dynasties can be effectively classified through the bag of words, HSV and support vector machine (BOW_HSV_SVM) based on their visual features. Through topic maps, the topic distribution characteristics and evolution rules of Dunhuang murals with the dynasties are revealed. [ABSTRACT FROM AUTHOR]

Murals are important resources for carrying cultural heritage, historical evidence and artistic memory. The sentiment of a mural is the transmission of its inner thoughts, closely related to the region and dynasty to which the mural belongs. To explore the sentiment evolution patterns of temple murals, we construct a spatio-temporal evolution analysis framework based on sentiment recognition. This framework mainly includes feature extraction, sentiment recognition and sentiment evolution analysis. First, we extract the colour features, local features, global semantic features, patch features and structure relations to represent the visual features of temple murals. Second, the semantics of spatio-temporal attributes and titles of murals are extracted through the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) to enhance the feature discrimination for sentiment recognition. Third, we introduce the SMOTE (Synthetic Minority Oversampling Technique) to reduce the influence of imbalanced data and select RF (random forest) as the optimal classifier. The F1 score of the fine-grained sentiment recognition model is up to 81.37%. Finally, we collect the temple murals and reveal the characteristics and patterns of sentiment evolution from the spatial, temporal and spatio-temporal perspectives. [ABSTRACT FROM AUTHOR]

This study aims to construct a comprehensive feature system for identifying artificial intelligence–generated content (AIGC) in online Q&A communities, thus uncovering the key factors and mechanisms influencing the identification of AIGC. First, based on the theory of systemic functional linguistics (SFL) and information quality (IQ), this article extracts vocabulary, content, structure, and emotional features from the text, and identifies the AIGC through nine mainstream machine learning algorithms. Subsequently, three widely used resampling strategies are exploited to address the category imbalance problem. The grid search optimisation algorithm fine-tunes different combinations of parameters to improve the performance of the identification classifier. Finally, SHAP values are introduced to evaluate and elucidate the global feature importance and feature influence mechanism. A Chinese corpus from the Zhihu Q&A community is constructed to verify the validity of these methods. The experimental results show that the eXtreme Gradient Boosting (XGBoost) model optimised with hybrid sampling and grid search parameters exhibits excellent performance in identifying AI-generated text, which achieves an F1-score of 0.9935, an improvement of 0.11 percentage points over the original model. In addition, all four dimensions of features constructed in this article contribute to AI-generated text identification, and the results of feature interpretability analysis show the greatest impact of features that focus on content readability. The study facilitates the identification and labelling of AIGC in online Q&A communities, thereby enhancing transparency and accountability of information shared online. [ABSTRACT FROM AUTHOR]