  We address a challenging problem of identifying main sources of hate speech
on Twitter. On one hand, we carefully annotate a large set of tweets for hate
speech, and deploy advanced deep learning to produce high quality hate speech
classification models. On the other hand, we create retweet networks, detect
communities and monitor their evolution through time. This combined approach is
applied to three years of Slovenian Twitter data. We report a number of
interesting results. Hate speech is dominated by offensive tweets, related to
political and ideological issues. The share of unacceptable tweets is
moderately increasing with time, from the initial 20% to 30% by the end of
2020. Unacceptable tweets are retweeted significantly more often than
acceptable tweets. About 60% of unacceptable tweets are produced by a single
right-wing community of only moderate size. Institutional Twitter accounts and
media accounts post significantly less unacceptable tweets than individual
accounts. In fact, the main sources of unacceptable tweets are anonymous
accounts, and accounts that were suspended or closed during the years
2018-2020.
