  The (((echo))) symbol -- triple parenthesis surrounding a name, made it to
mainstream social networks in early 2016, with the intensification of the U.S.
Presidential race. It was used by members of the alt-right, white supremacists
and internet trolls to tag people of Jewish heritage -- a modern incarnation of
the infamous yellow badge (Judenstern) used in Nazi-Germany. Tracking this
trending meme, its meaning, and its function has proved elusive for its
semantic ambiguity (e.g., a symbol for a virtual hug).
  In this paper we report of the construction of an appropriate dataset
allowing the reconstruction of networks of racist communities and the way they
are embedded in the broader community. We combine natural language processing
and structural network analysis to study communities promoting hate. In order
to overcome dog-whistling and linguistic ambiguity, we propose a multi-modal
neural architecture based on a BERT transformer and a BiLSTM network on the
tweet level, while also taking into account the users ego-network and meta
features. Our multi-modal neural architecture outperforms a set of strong
baselines. We further show how the the use of language and network structure in
tandem allows the detection of the leaders of the hate communities. We further
study the ``intersectionality'' of hate and show that the antisemitic echo
correlates with hate speech that targets other minority and protected groups.
Finally, we analyze the role IRA trolls assumed in this network as part of the
Russian interference campaign. Our findings allow a better understanding of
recent manifestations of racism and the dynamics that facilitate it.
