  Online firestorms on Twitter are seemingly arbitrarily occurring outrages
towards people, companies, media campaigns and politicians. Moral outrages can
create an excessive collective aggressiveness against one single argument, one
single word, or one action of a person resulting in hateful speech. With a
collective "against the others" the negative dynamics often start. Using data
from Twitter, we explored the starting points of several firestorm outbreaks.
As a social media platform with hundreds of millions of users interacting in
real-time on topics and events all over the world, Twitter serves as a social
sensor for online discussions and is known for quick and often emotional
disputes. The main question we pose in this article, is whether we can detect
the outbreak of a firestorm. Given 21 online firestorms on Twitter, the key
questions regarding the anomaly detection are: 1) How can we detect the
changing point? 2) How can we distinguish the features that cause a moral
outrage? In this paper we examine these challenges developing a method to
detect the point of change systematically spotting on linguistic cues of
tweets. We are able to detect outbreaks of firestorms early and precisely only
by applying linguistic cues. The results of our work can help detect negative
dynamics and may have the potential for individuals, companies, and governments
to mitigate hate in social media networks.
