Abusive language detection systems play a significant role in addressing cyberbullying. However, conventional detection systems primarily focus on the textual pattern of messages for their prediction, overlooking the reality that the same message can usually provoke different consequences for different users. In other words, personalised predictions are essential but currently absent. To address this limitation, this paper considers the rationality of introducing a set of psychological features to personalise abusive language detection tasks. The foundation of our work lies in the Antecedents, Behaviours, Consequences model (ABC model), asserting that an individual’s response to a trigger message is impacted not only by the trigger itself but also by their personal beliefs and attitudes. We develop a novel data preparation framework and construct a new abusive language dataset, incorporating psychological features from 505 online users. Logistic regression analysis illustrates that Irrationality and Self-down features positively correlate with the abusive class, while Rationality features exhibit a negative correlation. These results are supported by established psychological findings. Furthermore, a preliminary evaluation showed that our proposed psychological features improve a CNN-based detection system’s Macro and Weighted F1 scores by 4%–5% points when making personalised predictions. These results collectively make for an empirical case that underscores the importance of considering users’ psychological features in abusive language detection. Crucially, these findings also pave the way for developing personalised prediction systems. • Psychological features improve the detection system’s F1 scores by 4%–5% points. • Psychological theory and features are transferable to detection systems studies. • Due to a self-selection bias, online and ordinary users are substantially different. • A new abusive language dataset incorporates psychological features from 505 users.

Optimal window sizing in time series forecasting emerges as a pivotal factor for enhancing predictive accuracy, particularly in the volatile cryptocurrency market. While traditional models often rely on static window sizes, resulting in compromised forecasting performance, this research explores optimal window configurations across various market volatilities. By employing a hybrid Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) model, the study systematically identifies the most effective window sizes for high, medium, and low volatility conditions. Results demonstrate that smaller windows are preferable in highly volatile environments to capture rapid market shifts, whereas larger windows are more suitable for stable conditions to incorporate a broader historical context. By identifying the predetermined optimal window sizes for each volatility segment, this study offers valuable insights for researchers aiming to enhance the adaptability and efficacy of predictive models. These results are especially useful for exploring dynamic window sizing techniques across various domains, particularly in fields where data volatility significantly impacts model performance.

Recent methods have adapted the well-established AGM and belief base frameworks for belief change to cover belief revision in logic programs. In this study here, we present two new sets of belief change operators for logic programs. They focus on preserving the explicit relationships expressed in the rules of a program, a feature that is missing in purely semantic approaches that consider programs only in their entirety. In particular, operators of the latter class fail to satisfy preservation and support, two important properties for belief change in logic programs required to ensure intuitive results. We address this shortcoming of existing approaches by introducing partial meet and ensconcement constructions for logic program belief change, which allow us to define syntax-preserving operators for satisfying preservation and support. Our work is novel in that our constructions not only preserve more information from a logic program during a change operation than existing ones, but they also facilitate natural definitions of contraction operators, the first in the field to the best of our knowledge. To evaluate the rationality of our operators, we translate the revision and contraction postulates from the AGM and belief base frameworks to the logic programming setting. We show that our operators fully comply with the belief base framework and formally state the interdefinability between our operators. We further compare our approach to two state-of-the-art logic program revision methods and demonstrate that our operators address the shortcomings of one and generalise the other method.

The recent years have seen several proposals aimed at placing the revision of logic programs within the belief change frameworks established for classical logic. A crucial challenge of this task lies in the nonmonotonicity of standard logic programming semantics. Existing approaches have thus used the monotonic characterisation via SE-models to develop semantic revision operators, which however neglect any syntactic information, or reverted to a syntax-oriented belief base approach altogether. In this paper, we bridge the gap between semantic and syntactic techniques by adapting the idea of a partial meet construction from classical belief change. This type of construction allows us to define new model-based operators for revising as well as contracting logic programs that preserve the syntactic structure of the programs involved. We demonstrate the rationality of our operators by testing them against the classic AGM or alternative belief change postulates adapted to the logic programming setting. We further present an algorithm that reduces the partial meet revision or contraction of a logic program to performing revision or contraction only on the relevant subsets of that program.