TY - GEN
T1 - Developing and evaluating annotation procedures for twitter data during hazard events
AU - Stowe, Kevin
AU - Palmer, Martha
AU - Anderson, Jennings
AU - Palen, Leysia
AU - Anderson, Kenneth M.
AU - Kogan, Marina
AU - Morss, Rebecca
AU - Demuth, Julie
AU - Lazrus, Heather
N1 - Publisher Copyright:
Copyright © LAW-MWE-CxG 2018 - Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions, Proceedings of the Workshop.All rights reserved.
PY - 2018
Y1 - 2018
N2 - When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others’ actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user’s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.
AB - When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others’ actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user’s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.
UR - https://www.scopus.com/pages/publications/85058441775
M3 - Conference contribution
AN - SCOPUS:85058441775
T3 - LAW-MWE-CxG 2018 - Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions, Proceedings of the Workshop
SP - 133
EP - 143
BT - LAW-MWE-CxG 2018 - Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions, LAW-MWECxG 2018, in conjunction with the 27th International Conference on Computational Linguistics, COLING 2018
Y2 - 25 August 2018 through 26 August 2018
ER -