Mapping (Dis-)Information Flow about the MH17 Plane Crash

Abstract

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators.

A Survey on Stance Detection for Mis- and Disinformation Identification

“…Dungs et al (2018) continued this line of research, but focused on the effectiveness of stance for predicting rumour veracity. Hartmann et al (2019) explored the flow of (dis-)information on Twitter after the MH17 Plane Crash.…”

Russia’s rising military and communication power: From Chechnya to Crimea

“…The Bellingcat investigation into the MH17 disaster used open-source material to conclude that Russian-backed separatists in Eastern Ukraine were responsible for the shooting down of the Malaysian Airlines passenger plane in July 2014 (Bellingcat, 2020). Hartmann et al’s (2019) work on Twitter and disinformation in the aftermath of that disaster illustrates the difficulties of controlling narratives on social media.…”