  Finding attackable sentences in an argument is the first step toward
successful refutation in argumentation. We present a first large-scale analysis
of sentence attackability in online arguments. We analyze driving reasons for
attacks in argumentation and identify relevant characteristics of sentences. We
demonstrate that a sentence's attackability is associated with many of these
characteristics regarding the sentence's content, proposition types, and tone,
and that an external knowledge source can provide useful information about
attackability. Building on these findings, we demonstrate that machine learning
models can automatically detect attackable sentences in arguments,
significantly better than several baselines and comparably well to laypeople.
