Mapping (Dis-)Information Flow about the MH17 Plane Crash
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Mapping (Dis-)Information Flow about the MH17 Plane Crash. / Hartmann, Mareike ; Golovchenko, Yevgeniy; Augenstein, Isabelle.
2019. 45-55 Paper presented at Natural Language Processing for Internet Freedom, Hong Kong.Research output: Contribution to conference › Paper › Research
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TY - CONF
T1 - Mapping (Dis-)Information Flow about the MH17 Plane Crash
AU - Hartmann, Mareike
AU - Golovchenko, Yevgeniy
AU - Augenstein, Isabelle
N1 - Conference code: EMNLP-IJCNLP 2019
PY - 2019
Y1 - 2019
N2 - Digital media enables not only fast sharingof information, but also disinformation. Oneprominent case of an event leading to circu-lation of disinformation on social media isthe MH17 plane crash. Studies analysing thespread of information about this event on Twit-ter have focused on small, manually anno-tated datasets, or used proxys for data anno-tation. In this work, we examine to what ex-tent text classifiers can be used to label datafor subsequent content analysis, in particularwe focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17plane crash. Even though we find that a neuralclassifier improves over a hashtag based base-line, labeling pro-Russian and pro-Ukrainiancontent with high precision remains a chal-lenging problem. We provide an error analysisunderlining the difficulty of the task and iden-tify factors that might help improve classifica-tion in future work. Finally, we show how theclassifier can facilitate the annotation task forhuman annotators
AB - Digital media enables not only fast sharingof information, but also disinformation. Oneprominent case of an event leading to circu-lation of disinformation on social media isthe MH17 plane crash. Studies analysing thespread of information about this event on Twit-ter have focused on small, manually anno-tated datasets, or used proxys for data anno-tation. In this work, we examine to what ex-tent text classifiers can be used to label datafor subsequent content analysis, in particularwe focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17plane crash. Even though we find that a neuralclassifier improves over a hashtag based base-line, labeling pro-Russian and pro-Ukrainiancontent with high precision remains a chal-lenging problem. We provide an error analysisunderlining the difficulty of the task and iden-tify factors that might help improve classifica-tion in future work. Finally, we show how theclassifier can facilitate the annotation task forhuman annotators
UR - https://www.aclweb.org/anthology/D19-50.pdf#page=55
M3 - Paper
SP - 45
EP - 55
T2 - Natural Language Processing for Internet Freedom
Y2 - 4 November 2019
ER -
ID: 234936965