Online gaming involves large amount of people forming a large community of players who interact in real time. Toxic behavior in online chat is common and can harm players by deterring them. Thus, automated moderation is a necessity but difficult because game chat mixes domain-specific slang, deliberate obfuscation, informal “gamer” language , and tiny support for categories such as threats and extremism. This paper describes the TAGA (Token-Attribution Guided Attention) system submitted to the EEUCA 2026 Shared Task on Understanding Toxic Behavior in Gaming Communities. We propose TAGA, an architecture that employs a leave-one-out attribution method using the Detoxify toxicity scorer to compute per-token attribution scores across multiple toxicity dimensions, which are then projected into the learned attention biases that steer the model toward toxicity-indicative tokens. By preparing a five phase ablation study, we demonstrate that each component: domain-specific preprocessing, focal loss with label smoothing, attribution-guided attention pooling, and dual-model Detoxify features with strategic oversampling contributes to a cumulative gain in macro-F1 score points over the DeBERTa-v3-base baseline reported. The final system achieves a test macro-F1 score of 0.618 and, importantly, produces non-zero predictions for extreme data imbalance present in the dataset used in the shared task.

Memes have become ubiquitous on social media platforms blending text and imagery to express complex and culturally nuanced messages. While a high degree of automation in meme moderation has been achieved for high-resource languages, low-resource languages, such as Nepali, still remain largely neglected. In this paper, we describe our system submission to the CHiPSAL 2026 Shared Task on Multi-modal Hate and Sentiment Understanding in Low-Resource Nepali Memes, which features two main sub-tasks: (1) Detection of HateSpeech as binary classification and (2) Sentiment Analysis as multi-class classification in Nepali memes. We perform a comprehensive analysis of the following models: uni-modal textual models (mBERT, XLM-RoBERTa,MuRIL), uni-modal visual models (ResNet, ConvNeXt, ViT), nine different late-fusion multimodal models, and the vision-language foundation model, SigLIP. Among all models, the ViT model achieved the best macro F1-score(0.6278) for the hate speech detection task, while SigLIP achieved the best score (0.5481) for the sentiment analysis task. We hypothesize that the under-performance of fusion models may be attributed to OCR noise and inadequate low-resource textual representations that act as a bottleneck when paired with more advanced visual encoders. These results highlight the unique challenges of multimodal meme comprehension in low-resource contexts and underscores the requirement for culturally grounded, noise-robust approaches to content moderation in Nepali.

The discourse surrounding climate change on social media platforms has emerged as a significant avenue for understanding public sentiments, perspectives, and engagement with this critical global issue. The unavailability of publicly available datasets, coupled with ignoring the multi-aspect analysis of climate discourse on social media platforms, has underscored the necessity for further advancement in this area. To address this gap, in this paper, we present an extensive exploration of the intricate realm of climate change discourse on Twitter, leveraging a meticulously annotated ClimaConvo dataset comprising 15,309 tweets. Our annotations encompass a rich spectrum, including aspects like relevance, stance, hate speech, the direction of hate, and humor, offering a nuanced understanding of the discourse dynamics. We address the challenges inherent in dissecting online climate discussions and detail our comprehensive annotation methodology. In addition to annotations, we conduct benchmarking assessments across various algorithms for six tasks: relevance detection, stance detection, hate speech identification, direction and target, and humor analysis. This assessment enhances our grasp of sentiment fluctuations and linguistic subtleties within the discourse. Our analysis extends to exploratory data examination, unveiling tweet distribution patterns, stance prevalence, and hate speech trends. Employing sophisticated topic modeling techniques uncovers underlying thematic clusters, providing insights into the diverse narrative threads woven within the discourse. The findings present a valuable resource for researchers, policymakers, and communicators seeking to navigate the intricacies of climate change discussions. The dataset and resources for this paper are available at https://github.com/shucoll/ClimaConvo.