MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.
By John Cadigan, Dayne Freitag, Eric Yeh
arXiv:2610.05275v2 Announce Type: replace
Abstract: A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statemen...
By Sandrine Chausson, Bj\"orn Ross
arXiv:2609.06126v1 Announce Type: new
Abstract: We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different...
By Ruibiao Fu, Di Tang, Yunlong Yang, Ran Wang, Sicheng Lu, Peixuan Wu, Xiaoyu Fan, Jiacheng Ma, HaoZhe Luo, Yang Xiao
The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents. Existing methods for detecting LLM-generated text mainly focus on document-level classification and cannot identify which parts of the text are generated by LLMs.
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
By Lior Biton, Oren Tsur
The paper introduces a modular data‑science pipeline that estimates public sentiment toward individuals using fragmented, unstructured open‑source intelligence. The pipeline combines web search, text extraction, relevance filtering, tokenisation, co‑reference resolution, and sentiment analysis to produce auditable person‑level sentiment distributions. By comparing AFINN, VADER, and the domain‑specific MINOS algorithm, the authors show that MINOS best distinguishes positive, ambiguous, and negative reputational cases, and they apply the method to the UK Honours system to support transparent, reproducible, human‑in‑the‑loop sentiment assessment for high‑stakes decisions.
By Francesca von Braun-Bates, Sunreeta Sen, Indraayudh Talukdar, Anirban Lahiri
arXiv:2607. 10312v1 Announce Type: cross Abstract: The rapid proliferation of online polarization threatens social cohesion, necessitating robust automated detection systems that operate effectively across diverse linguistic contexts.
By Muhammad Abdullahi Said
The paper presents a SciBERT-based method for automatically classifying scientific papers into four telescope-related categories—science, instrumentation, mention, and not telescope—within strict 512-token limits. Despite truncation challenges, the approach achieved a macro F1 score of 0.89, topping the WASP-2025 leaderboard. The authors analyze truncation effects, compare chunking and long-context models, and offer insights into efficient scientific text curation.
By Madhusudhana Naidu
EnsembleLink is a record‑linkage method that achieves high accuracy without requiring any training labels. It uses pre‑trained language models to capture semantic relationships, enabling it to match records across diverse domains such as city names, person names, organizations, multilingual political parties, and bibliographic entries. The approach runs locally on open‑source models, avoiding external API calls, and completes typical linkage tasks in minutes.
By Noah Dasanaike
arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.
By Yangjun Lu, Hongyi Zhou, Fabian Spill, Kai Ye, Chengchun Shi, Jin Zhu
The paper presents a method for building a network map of the artworld using tweets from a manually selected set of exemplar actors and NLP techniques. It identifies significant artworld actors, even those without Twitter accounts, and groups them into communities via the Louvain method while assessing their authority with HITS centrality. The resulting network demonstrates that starting from a small set of exemplars can uncover new actors and yield data that would otherwise be difficult to gather.
By Amalia Foka
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
By Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey