Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly. We introduce UNRESTSENT200K, a Bangla crisis...
arXiv:2607. 14957v1 Announce Type: new Abstract: Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage.
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
arXiv:2608.21385v1 Announce Type: cross Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose b...
arXiv:2602. 13452v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication.
arXiv:2607. 02734v1 Announce Type: cross Abstract: Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation.
arXiv:2609.35821v2 Announce Type: replace Abstract: Disaster social sensing converts public social-media posts into evidence for situational awareness and humanitarian response, but plausible LLM-gen...
arXiv:2610.09464v1 Announce Type: new Abstract: Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences. While Bangla n...
The paper introduces BenCoDiC, a large Bengali cyberbullying corpus focused on political news discourse. It contains 50,250 textual instances from 100 YouTube videos, preserving caption–comment–reply relationships and organized via a nine‑dimensional star schema. A two‑tier human annotation process achieved high reliability (Fleiss' κ = 0.86, Krippendorff's α = 0.86), and transformer experiments show that adding conversational context modestly improves classification over isolated text.
arXiv:2610.20370v1 Announce Type: new Abstract: Hate speech has become increasingly prevalent on social media, raising concerns about its detection, interpretation, and moderation. Persian, a relativ...
BanglishDepYT is a native Bengali depression detection dataset comprising 191,000 unlabeled YouTube comments and 1,000 bilingual‑annotated samples. The authors introduce Emotion‑Preserving Hybrid preprocessing (EPH), which retains emoticons, Banglish tokens, negations, and emotional elongation, improving SVM F1 from 0.7062 to 0.8570. Domain‑adaptive pretraining of SahajBERT on in‑domain comments yields the best performance (F1 = 0.8161, MCC = 0.6272), and LLM‑assisted annotation enables pseudo‑labeling that further boosts AUC from 0.8499 to 0.8731.