Searching for Structure in Unfalsifiable Claims

Publikation: Working paper › Preprint › Forskning

Standard

Searching for Structure in Unfalsifiable Claims. / Christensen, Peter Ebert; Warburg, Frederik ; Jia, Menglin.

arxiv.org, 2022.

Publikation: Working paper › Preprint › Forskning

Harvard

Christensen, PE, Warburg, F & Jia, M 2022 'Searching for Structure in Unfalsifiable Claims' arxiv.org. <https://arxiv.org/abs/2209.00495>

APA

Christensen, P. E., Warburg, F., & Jia, M. (2022). Searching for Structure in Unfalsifiable Claims. arxiv.org. https://arxiv.org/abs/2209.00495

Vancouver

Christensen PE, Warburg F, Jia M. Searching for Structure in Unfalsifiable Claims. arxiv.org. 2022.

Author

Christensen, Peter Ebert ; Warburg, Frederik ; Jia, Menglin. / Searching for Structure in Unfalsifiable Claims. arxiv.org, 2022.

Bibtex

@techreport{6e1ee192c8f445b9b747c0183dc94fa6,
title = "Searching for Structure in Unfalsifiable Claims",
abstract = "Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfalsifiable nature makes them ill-suited to fact-checking pipelines. In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic. Understanding and visualizing these narratives can facilitate more informed debates on social media. As a first step towards systematically identifying the underlying narratives on social media, we introduce PAPYER, a fine-grained dataset of online comments related to hygiene in public restrooms, which contains a multitude of unfalsifiable claims. We present a human-in-the-loop pipeline that uses a combination of machine and human kernels to discover the prevailing narratives and show that this pipeline outperforms recent large transformer models and state-of-the-art unsupervised topic models.",
author = "Christensen, {Peter Ebert} and Frederik Warburg and Menglin Jia",
year = "2022",
language = "English",
publisher = "arxiv.org",
type = "WorkingPaper",
institution = "arxiv.org",

}

RIS

TY - UNPB

T1 - Searching for Structure in Unfalsifiable Claims

AU - Christensen, Peter Ebert

AU - Warburg, Frederik

AU - Jia, Menglin

PY - 2022

Y1 - 2022

N2 - Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfalsifiable nature makes them ill-suited to fact-checking pipelines. In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic. Understanding and visualizing these narratives can facilitate more informed debates on social media. As a first step towards systematically identifying the underlying narratives on social media, we introduce PAPYER, a fine-grained dataset of online comments related to hygiene in public restrooms, which contains a multitude of unfalsifiable claims. We present a human-in-the-loop pipeline that uses a combination of machine and human kernels to discover the prevailing narratives and show that this pipeline outperforms recent large transformer models and state-of-the-art unsupervised topic models.

AB - Social media platforms give rise to an abundance of posts and comments on every topic imaginable. Many of these posts express opinions on various aspects of society, but their unfalsifiable nature makes them ill-suited to fact-checking pipelines. In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic. Understanding and visualizing these narratives can facilitate more informed debates on social media. As a first step towards systematically identifying the underlying narratives on social media, we introduce PAPYER, a fine-grained dataset of online comments related to hygiene in public restrooms, which contains a multitude of unfalsifiable claims. We present a human-in-the-loop pipeline that uses a combination of machine and human kernels to discover the prevailing narratives and show that this pipeline outperforms recent large transformer models and state-of-the-art unsupervised topic models.

M3 - Preprint

BT - Searching for Structure in Unfalsifiable Claims

PB - arxiv.org

ER -

ID: 384581811