Aug 2026· Knowledge and Information Systems· Vol 68· 0 citations· 62 references
TL;DR
Results demonstrate that jointly incorporating structural, semantic, and sentiment information yields more coherent, semantically aligned communities than structure-only baselines while maintaining practical scalability.
The proposed model, Crossbred Analogous Tree-Knowledge Deep (CAT-KD), is composed of five key components for comprehensive sentiment analysis and achieves an accuracy of 94.83% and an F1-score of 94.12%, outperforming baseline models with improvements of up to 0.62% in accuracy and 2.46% in F1-score on Twitter datasets.
. Social media sentiment analysis faces a persistent aggregation problem: lexicon-based and transformer-based models often produce inconsistent outputs for the same short, informal, and stylistically heterogeneous texts. This paper introduces ADRTW (Adaptive Dynamic Reliability-Trig-gered Weighting), an interpretable sentiment fusion framework that combines heterogeneous sentiment estimators using rule-guided reliability weights derived from textual cues, inter-model disagreement, and consistency patterns [5, 8]. The framework is evaluated on a Reddit dataset containing 1,577 posts, 354,050 comments, and 187,666 authors collected between 2017 and 2025, together with a controlled synthetic benchmark for aggregation comparison. The results show that ADRTW remains competitive with static averaging in controlled settings while preserving context-sensitive local variation in large-scale discourse analysis. Beyond sentiment fusion, the ADRTW-derived signal supports complementary analyses of online discussions, including temporal trend inspection, toxicity-aware interpretation, and participation-based clustering. Overall, the proposed framework provides a transparent and reusable basis for examining emotional dynamics in social media discourse.
Aniko Apro, L. Sasi· Annales Mathematicae et Info...· 0 citations
This study proposes a novel framework for identifying the dominant domain of influence of social media actors on Twitter under conditions of uncertainty. Existing influence analysis approaches rely on network topology, popularity indicators, or content analysis separately and often fail to explicitly manage uncertainty and conflicting evidence. To address this research gap, the proposed framework combines Latent Dirichlet Allocation (LDA), Natural Language Processing (NLP), Belief Function Theory and Dempster’s rule of combination within a unified evidential reasoning framework. First, NLP preprocessing techniques including tokenization, stop-word removal, lemmatization and text normalization are applied to transform raw tweets into a structured textual corpus. LDA is then employed to extract latent thematic topics and estimate topic distributions. Subsequently, thematic relevance and engagement indicators are transformed into belief mass functions. These heterogeneous sources of evidence are fused through Dempster–Shafer theory and pignistic probabilities are computed to identify the dominant influence domain. Experimental validation conducted on two influential public figures from distinct domains demonstrates that the proposed framework successfully identifies dominant influence domains while explicitly accounting for uncertain evidence. The results show strong consistency between thematic relevance and audience engagement indicators, confirming the robustness of the proposed evidential fusion strategy. The main novelty of this work lies in transforming topic-modeling outputs into belief mass functions and integrating them with engagement-based evidence through Dempster’s combination rule. The proposed framework provides a rigorous, interpretable and uncertainty-aware solution for social media influence-domain identification and offers practical applications in influencer marketing, digital communication and reputation management.
Fatima-Zahrae Sifi, Wafae Sabbar, A. Mzabi· Journal of Intelligent Decis...· 0 citations
Influencer-centered communities on social media are connected through overlapping audiences and shared topical attention, yet their directed sentiment dependencies remain difficult to observe directly. We reconstruct inter-community influence networks from sentiment time series on Weibo, a major Chinese microblogging platform. We track several prominent technology bloggers over six months, aggregating comment-level sentiment into collective sentiment trajectories for each blogger’s commenting audience. These audiences are treated as macro-agents, an aggregation justified by shared content exposure and homophily-driven sentiment alignment. Using opinion dynamics models as network inference tools, we estimate directed influence graphs capturing how collective sentiment in one community shapes that in others. The inferred networks exhibit pronounced sparsity and clear structural asymmetry. Some communities act as hubs whose sentiment radiates outward, while others are largely internally driven, reacting to their blogger’s posts and weakly coupled to peer communities. These patterns remain consistent across model variants, and the learned models demonstrate stable two-period-ahead predictive performance on held-out data. Our findings show that collective online sentiment is well described by structured inter-community averaging, yielding interpretable, empirically grounded influence graphs. The proposed framework provides an interpretable approach for mapping inter-community sentiment dependencies from social media time series.
Yulong He, A. Proskurnikov, Artem Sedakov· Social Network Analysis and...· 3 citations
Political discussions on social media have become an important source of information for understanding public opinion and information dissemination. However, most existing political topic classification methods rely primarily on textual features and tend to overlook structural and temporal relationships between users. In this study, we propose a Multi-View Graph Attention Network (MV-GAT) that improves political topic classification by integrating three complementary graph representations: a semantic content graph, a user interaction graph, and a temporal propagation graph. We collected a dataset containing 15,131 Indonesian tweets from Twitter(X), of which 1,677 tweets were manually labeled as political or apolitical, and the remaining tweets were kept as unlabeled nodes to maintain the graph structure. Each graph view was independently constructed and aligned using tweet_id before being processed by the proposed MV-GAT model. The model was trained using weighted cross-entropy loss with an attention-based fusion mechanism to automatically learn the contribution of each graph view. Experimental results showed that the proposed method achieved an accuracy of 84.23%, a macro F1 score of 83.04%, and an F1 score of 78.54% in political topic classification. Attention analysis revealed that the semantic content graph contributed most significantly to the classification process, while the interaction graph and time graph provided complementary structural information. Furthermore, post-classification graph analysis revealed relationship patterns among users and the propagation of political information within the Twitter network. These results demonstrate that integrating multiple graph views improves both the classification performance and interpretability of political topic analysis on social media.
Dhea Sila Mukti, Rizal Tjut Adek, Cut Agusniar· Brilliance Research of Artif...· 0 citations
This work proposes the SENSE model, a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction, and employs a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction.
Ying-Jie Chen, Xiang Li, Dong Chen et al.· 0 citations
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