← Back to all news
arXiv Computation and Language By Theodora Moldovan, Georgios Panayiotou, Davide Vega

Les Mis\'erables Dialogue: A film-based network representation of social communication

Read the original on arXiv Computation and Language →

The paper presents a new dataset called the Les Misérables Dialogue temporal text network, created from the 1998 film adaptation of the novel. This network explicitly records speakers, addressees, other recipients, the content of each utterance, and its timing. Additionally, the authors generate multilayer network representations based on ten social dimensions, and provide a general methodology for constructing similar communication datasets from narrative sources.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Machine Learning
Aug 4

Network Information Enhances Unreliable News Domain Detection

arXiv:2608. 02399v1 Announce Type: cross Abstract: Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag.

By Raphaela Ke{\ss}ler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo
rag
More like this →
arXiv AI
Jul 3

Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas

arXiv:2607. 02504v1 Announce Type: cross Abstract: Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \textbf{speaker recognition}, the task of accurately attributing each spoken utterance to its respective character.

By Yuxuan Li, Lingxi Xie, Xinyue Huo, Jihao Qiu, Jiacheng Shao, Pengfei Chen, Jiannan Ge, Kaiwen Duan, Qi Tian
llmsmultimodalbenchmarks
More like this →
arXiv Computation and Language
Sep 16

Zero-shot narrative detection in social messaging

The paper explores how large language models can detect hidden narratives in social messages without training data. By feeding the models human-written narrative descriptions, performance improves markedly, while automatically generated descriptions or few-shot examples can hurt accuracy. Ensemble techniques, especially majority voting, further boost robustness, and larger models show the best results with less sensitivity to prompts.

By Jes\'us M. Fraile-Hern\'andez, Anselmo Pe\~nas, Patrick Giedemann
llms
More like this →
arXiv Computation and Language
Sep 11

Characterizing Narrative Content in Web-scale LLM Pretraining Data

The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.

By Teagan Johnson, Elliott Ash, Andrew Piper, Maria Antoniak
llms
More like this →