TY - GEN
T1 - Classifying types of ethos support and attack
AU - Duthie, Rory
AU - Budzynska, Katarzyna
PY - 2018
Y1 - 2018
N2 - Endorsing the character of allies and destroying credibility of opponents is a powerful tactic for persuading others, impacting how we see politicians and how we vote in elections, for example. Our previous work demonstrated that ethos supports and attacks use different language, we hypothesise that further distinctions should be made in order to better understand and implement ethotic strategies which people use in real-life communication. In this paper, we use the Aristotelian concept of elements of ethos: practical wisdom, moral virtue and goodwill, to determine specific grounds on which speakers can be endorsed and criticised. We propose a classification of types of ethos supports and attacks which is empirically derived from our corpus. The manual classification obtains a reliable Cohen's kappa κ = 0.52 and weighted κ = 0.7. Finally, we develop a pipeline to classify ethos supports and attacks into their types depending on whether endorsement or criticism is grounded in wisdom, virtue or goodwill. The automatic classification obtains a solid improvement of macro-averaged F1-score over the baseline of 10%, 25%, 9% for one vs all classification, and 16%, 18%, 10% for pairwise classification.
AB - Endorsing the character of allies and destroying credibility of opponents is a powerful tactic for persuading others, impacting how we see politicians and how we vote in elections, for example. Our previous work demonstrated that ethos supports and attacks use different language, we hypothesise that further distinctions should be made in order to better understand and implement ethotic strategies which people use in real-life communication. In this paper, we use the Aristotelian concept of elements of ethos: practical wisdom, moral virtue and goodwill, to determine specific grounds on which speakers can be endorsed and criticised. We propose a classification of types of ethos supports and attacks which is empirically derived from our corpus. The manual classification obtains a reliable Cohen's kappa κ = 0.52 and weighted κ = 0.7. Finally, we develop a pipeline to classify ethos supports and attacks into their types depending on whether endorsement or criticism is grounded in wisdom, virtue or goodwill. The automatic classification obtains a solid improvement of macro-averaged F1-score over the baseline of 10%, 25%, 9% for one vs all classification, and 16%, 18%, 10% for pairwise classification.
KW - Corpus Analysis
KW - Elements of Ethos
KW - Ethos Mining
KW - Ethos-Logos
KW - Wisdom-Virtue-Goodwill
UR - http://www.scopus.com/inward/record.url?scp=85053920931&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-906-5-161
DO - 10.3233/978-1-61499-906-5-161
M3 - Conference contribution
AN - SCOPUS:85053920931
VL - 305
T3 - Frontiers in Artificial Intelligence and Applications
SP - 161
EP - 168
BT - Computational Models of Argument - Proceedings of COMMA 2018
A2 - Modgil, Sanjay
A2 - Budzynska, Katarzyna
A2 - Lawrence, John
A2 - Budzynska, Katarzyna
PB - IOS Press
T2 - 7th International Conference on Computational Models of Argument, COMMA 2018
Y2 - 12 September 2018 through 14 September 2018
ER -