Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation

Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions while maintaining high-quality requirements. Our methodology penalizes demographic disparities while preserving quality through intersectional criteria (e.g., race, country) and a customized fairness loss, in contrast to heuristic approaches. Evaluations using conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI) indicate a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility, indicating that diversity promotion does not compromise academic rigor and supports equity-focused peer review solutions.

From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review

Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: if a post-review recommender is trained with an explicit fairness regularizer, it should increase inclusion without degrading quality. To test this, we introduce Fair-PaperRec, a Multi-Layer Perceptron (MLP) with a differentiable fairness loss over intersectional attributes (e.g., race, country) that re-ranks papers after double-blind review. We first probe the hypothesis on synthetic datasets spanning high, moderate, and near-fair biases. Across multiple randomized runs, these controlled studies map where increasing the fairness weight strengthens macro/micro diversity while keeping utility approximately stable, demonstrating robustness and adaptability under varying disparity levels. We then carry the hypothesis into the original setting, conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI). In this real-world scenario, an appropriately tuned configuration of Fair-PaperRec achieves up to a 42.03% increase in underrepresented-group participation with at most a 3.16% change in overall utility relative to the historical selection. Taken together, the synthetic-to-original progression shows that fairness regularization can act as both an equity mechanism and a mild quality regularizer, especially in highly biased regimes. By first analyzing the behavior of the fairness parameters under controlled conditions and then validating them on real submissions, Fair-PaperRec offers a practical, equity-focused framework for post-review paper selection that preserves, and in some settings can even enhance, measured scholarly quality.

Verifying Rumors via Stance-Aware Structural Modeling

Verifying rumors on social media is critical for mitigating the spread of false information. The stances of conversation replies often provide important cues to determine a rumor's veracity. However, existing models struggle to jointly capture semantic content, stance information, and conversation strructure, especially under the sequence length constraints of transformer-based encoders. In this work, we propose a stance-aware structural modeling that encodes each post in a discourse with its stance signal and aggregates reply embedddings by stance category enabling a scalable and semantically enriched representation of the entire thread. To enhance structural awareness, we introduce stance distribution and hierarchical depth as covariates, capturing stance imbalance and the influence of reply depth. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms prior methods in the ability to predict truthfulness of a rumor. We also demonstrate that our model is versatile for early detection and cross-platfrom generalization.

Causally Fair Node Classification on Non-IID Graph Data

Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recently, a few works have extended fairness to graph data, such as social networks, but most of them neglect the causal relationships among data instances. This paper addresses the prevalent challenge in fairness-aware ML algorithms, which typically assume Independent and Identically Distributed (IID) data. We tackle the overlooked domain of non-IID, graph-based settings where data instances are interconnected, influencing the outcomes of fairness interventions. We base our research on the Network Structural Causal Model (NSCM) framework and posit two main assumptions: Decomposability and Graph Independence, which enable the computation of interventional distributions in non-IID settings using the $do$-calculus. Based on that, we develop the Message Passing Variational Autoencoder for Causal Inference (MPVA) to compute interventional distributions and facilitate causally fair node classification through estimated interventional distributions. Empirical evaluations on semi-synthetic and real-world datasets demonstrate that MPVA outperforms conventional methods by effectively approximating interventional distributions and mitigating bias. The implications of our findings underscore the potential of causality-based fairness in complex ML applications, setting the stage for further research into relaxing the initial assumptions to enhance model fairness.

Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis

Emotional support (ES) systems alleviate users' mental distress by generating strategic supportive dialogues based on diverse user situations. However, ES systems are limited in their ability to generate effective ES dialogues that include timely context and interpretability, hindering them from earning public trust. Driven by cognitive models, we propose Mind-to-Mind (Mind2), an ES framework that approaches interpretable ES context modeling for the ES dialogue generation task from a discourse analysis perspective. Specifically, we perform cognitive discourse analysis on ES dialogues according to our dynamic discourse context propagation window, which accommodates evolving context as the conversation between the ES system and user progresses. To enhance interpretability, Mind2 prioritizes details that reflect each speaker's belief about the other speaker with bidirectionality, integrating Theory-of-Mind, physiological expected utility, and cognitive rationality to extract cognitive knowledge from ES conversations. Experimental results support that Mind2 achieves competitive performance versus state-of-the-art ES systems while trained with only 10\% of the available training data.

Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection

Stance Detection (SD) on social media has emerged as a prominent area of interest with implications for social business and political applications thereby garnering escalating research attention within NLP. The inherent subtlety and complexity of texts procured from online platforms pose challenges for SD algorithms in accurately discerning the authors stance. Mostly the inclusion of sarcastic and figurative language drastically impacts the performance of SD models. This paper addresses this by employing sarcasm detection intermediate-task transfer learning tailored for SD. The proposed methodology involves the finetuning of BERT and RoBERTa and the concatenation of convolutional BiLSTM and dense layers. Rigorous experiments are conducted on publicly available datasets to evaluate our transfer-learning framework. The performance of the approach is assessed against various State-Of-The-Art baselines for SD providing empirical evidence of its effectiveness. Notably our model outperforms the best SOTA models even prior to sarcasm-detection pretraining. The integration of sarcasm knowledge into the model proves instrumental in mitigating misclassifications of sarcastic textual elements in SD. Our model accurately predicts 85% of texts that were previously misclassified by the model without sarcasm-detection pretraining thereby amplifying the average F1-score of the model. Our experiments also revealed that the success of the transfer-learning framework is contingent upon the correlation of lexical attributes between the intermediate task and the target task. This study represents the first exploration of sarcasm detection as an intermediate transfer-learning task in the context of SD and simultaneously uses the concatenation of BERT or RoBERTa with other deep-learning techniques establishing the proposed approach as a foundational baseline for future research endeavors in this domain.

Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities

Stance Detection (SD) has become a critical area of interest due to its applications in various contexts leading to increased research within NLP. Yet the subtlety and complexity of texts sourced from online platforms often containing sarcastic language pose significant challenges for SD algorithms in accurately determining the authors stance. This paper addresses this by employing sarcasm for SD. It also tackles the issue of insufficient annotated data for training SD models on new targets by conducting Cross-Target SD (CTSD). The proposed approach involves fine-tuning BERT and RoBERTa models followed by concatenating additional deep learning layers. The approach is assessed against various State-Of-The-Art baselines for SD demonstrating superior performance using publicly available datasets. Notably our model outperforms the best SOTA models on both in-domain SD and CTSD tasks even before the incorporation of sarcasm-detection pre-training. The integration of sarcasm knowledge into the model significantly reduces misclassifications of sarcastic text elements in SD allowing our model to accurately predict 85% of texts that were previously misclassified without sarcasm-detection pre-training on in-domain SD. This enhancement contributes to an increase in the models average macro F1-score. The CTSD task achieves performance comparable to that of the in-domain task despite using a zero-shot finetuning. We also reveal that the success of the transfer-learning framework relies on the correlation between the lexical attributes of sarcasm detection and SD. This study represents the first exploration of sarcasm detection as an intermediate transfer-learning task within the context of SD while also leveraging the concatenation of BERT or RoBERTa with other deep-learning techniques. The proposed approach establishes a foundational baseline for future research in this domain.

Boolean-aware Attention for Dense Retrieval

We present Boolean-aware attention, a novel attention mechanism that dynamically adjusts token focus based on Boolean operators (e.g., and, or, not). Our model employs specialized Boolean experts, each tailored to amplify or suppress attention for operator-specific contexts. A predefined gating mechanism activates the corresponding experts based on the detected Boolean type. Experiments on Boolean retrieval datasets demonstrate that integrating BoolAttn with BERT greatly enhances the model's capability to process Boolean queries.

Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis

Sentiment Analysis (SA) is instrumental in understanding peoples viewpoints facilitating social media monitoring recognizing products and brands and gauging customer satisfaction. Consequently SA has evolved into an active research domain within Natural Language Processing (NLP). Many approaches outlined in the literature devise intricate frameworks aimed at achieving high accuracy, focusing exclusively on either binary sentiment classification or fine-grained sentiment classification. In this paper our objective is to fine-tune the pre-trained BERT model with Bidirectional LSTM (BiLSTM) to enhance both binary and fine-grained SA specifically for movie reviews. Our approach involves conducting sentiment classification for each review followed by computing the overall sentiment polarity across all reviews. We present our findings on binary classification as well as fine-grained classification utilizing benchmark datasets. Additionally we implement and assess two accuracy improvement techniques Synthetic Minority Oversampling Technique (SMOTE) and NLP Augmenter (NLPAUG) to bolster the models generalization in fine-grained sentiment classification. Finally a heuristic algorithm is employed to calculate the overall polarity of predicted reviews from the BERT+BiLSTM output vector. Our approach performs comparably with state-of-the-art (SOTA) techniques in both classifications. For instance in binary classification we achieve 97.67% accuracy surpassing the leading SOTA model NB-weighted-BON+dv-cosine by 0.27% on the renowned IMDb dataset. Conversely for five-class classification on SST-5 while the top SOTA model RoBERTa+large+Self-explaining attains 55.5% accuracy our model achieves 59.48% accuracy surpassing the BERT-large baseline by 3.6%.

Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation

This paper presents a diffusion-based recommender system that incorporates classifier-free guidance. Most current recommender systems provide recommendations using conventional methods such as collaborative or content-based filtering. Diffusion is a new approach to generative AI that improves on previous generative AI approaches such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items. Although a few current recommender systems incorporate diffusion, they do not incorporate classifier-free guidance, a new innovation in diffusion models as a whole. In this paper, we present a diffusion recommender system that augments the underlying recommender system model for improved performance and also incorporates classifier-free guidance. Our findings show improvements over state-of-the-art recommender systems for most metrics for several recommendation tasks on a variety of datasets. In particular, our approach demonstrates the potential to provide better recommendations when data is sparse.