The paper introduces a pipeline and conversational system that processes 22,788 YouTube transcript and comment chunks from 309 North American cities to analyze public discourse on urbanism. It combines geographic entity resolution, topic modeling, sentiment analysis, and Retrieval-Augmented Generation (RAG), and reports empirical findings on model performance, such as a Twitter-tuned RoBERTa classifier outperforming VADER and dense retrieval surpassing TF‑IDF. The study also evaluates groundedness metrics, noting limitations of BERTScore and ROUGE‑1 for short user-generated text.
arXiv:2609.27059v1 Announce Type: new Abstract: We introduce the Illinois Social Attitudes Aggregate Corpus (ISAAC), an open, modular, and accessible corpus of 527 million+ English-language Reddit po...
arXiv:2608. 07208v1 Announce Type: cross Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities.
arXiv:2608.22859v1 Announce Type: cross Abstract: RAG systems are increasingly used to summarize what large collections of documents say. A user asks "What do people think about X?" and receives an a...
The paper presents a knowledge graph built from Harvard Dataverse’s public data, linking 102,650 datasets to 215,985 nodes and 528,003 edges that include keywords, publications, subjects, journals, and locations. About 43,991 datasets contain geospatial metadata, and 7,654 are identified as policy‑relevant, with elections and legislatures forming the largest cluster. The authors highlight the challenge of place resolution—disconnected nodes representing the same location—and propose the graph as a testbed for AI‑driven metadata enrichment and entity resolution, noting a bias toward American city‑level data.
arXiv:2508. 13187v4 Announce Type: replace-cross Abstract: Homelessness is a persistent social challenge, impacting millions worldwide.
The paper introduces a diagnostic tool for distinguishing the use of misogynistic slurs from their mention in counter‑speech within code‑mixed Hinglish. It identifies evaluation artifacts in existing corpora, releases a 416‑item minimal‑pair contrast set that decorrelates slur presence and gendered register from labels, and proposes a pair‑consistency metric to assess model performance. Experiments show that even strong baselines struggle to consistently label counter‑speech pairs, while a large language model achieves perfect scores, indicating the benchmark measures genuine capability rather than exploitation of artifacts.
arXiv:2608.30614v1 Announce Type: new Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at m...
The paper investigates how semantic distance and ambiguity contribute to joke humor by revisiting and extending metrics from prior work. It introduces a new symmetry metric—measuring how close the ambiguous element Z is to both X and Y—and evaluates it using two embedding models on three joke datasets, including expanded versions with paired ambiguous sentences. Although models based on these metrics performed poorly in predicting humor ratings, the symmetry metric consistently correlated with higher-rated jokes, hinting it captures a key, though not sole, property of humor.
arXiv:2510. 18908v2 Announce Type: replace-cross Abstract: Social media platforms such as Twitter (now X) provide rich data for analyzing public discourse, especially during crises such as the COVID-19 pandemic.
TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.
arXiv:2605. 05103v3 Announce Type: replace-cross Abstract: We introduce the \textbf{Concept Field} of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences.
arXiv:2606. 07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift.