Searching for Structure in Unfalsifiable Claims
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Searching for Structure in Unfalsifiable Claims. / Christensen, Peter Ebert; Warburg, Frederik ; Jia, Menglin.
arxiv.org, 2022.Research output: Working paper › Preprint › Research
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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