20 research outputs found
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Americans spend about a third of their time online, with many participating
in online conversations on social and political issues. We hypothesize that
social media arguments on such issues may be more engaging and persuasive than
traditional media summaries, and that particular types of people may be more or
less convinced by particular styles of argument, e.g. emotional arguments may
resonate with some personalities while factual arguments resonate with others.
We report a set of experiments testing at large scale how audience variables
interact with argument style to affect the persuasiveness of an argument, an
under-researched topic within natural language processing. We show that belief
change is affected by personality factors, with conscientious, open and
agreeable people being more convinced by emotional arguments.Comment: European Chapter of the Association for Computational Linguistics
(EACL 2017
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums. A
better understanding of such claims requires analyzing them from different
perspectives. Stance classification is a necessary step for inferring these
perspectives in terms of supporting or opposing the claim. In this work, we
present a neural network model for stance classification leveraging BERT
representations and augmenting them with a novel consistency constraint.
Experiments on the Perspectrum dataset, consisting of claims and users'
perspectives from various debate websites, demonstrate the effectiveness of our
approach over state-of-the-art baselines.Comment: Accepted at EMNLP 201
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance
classification task as a crucial first step towards detecting fake news. To
date, there is no in-depth analysis paper to critically discuss FNC-1's
experimental setup, reproduce the results, and draw conclusions for
next-generation stance classification methods. In this paper, we provide such
an in-depth analysis for the three top-performing systems. We first find that
FNC-1's proposed evaluation metric favors the majority class, which can be
easily classified, and thus overestimates the true discriminative power of the
methods. Therefore, we propose a new F1-based metric yielding a changed system
ranking. Next, we compare the features and architectures used, which leads to a
novel feature-rich stacked LSTM model that performs on par with the best
systems, but is superior in predicting minority classes. To understand the
methods' ability to generalize, we derive a new dataset and perform both
in-domain and cross-domain experiments. Our qualitative and quantitative study
helps interpreting the original FNC-1 scores and understand which features help
improving performance and why. Our new dataset and all source code used during
the reproduction study are publicly available for future research