  We address rumor detection by learning to differentiate between the
community's response to real and fake claims in microblogs. Existing
state-of-the-art models are based on tree models that model conversational
trees. However, in social media, a user posting a reply might be replying to
the entire thread rather than to a specific user. We propose a post-level
attention model (PLAN) to model long distance interactions between tweets with
the multi-head attention mechanism in a transformer network. We investigated
variants of this model: (1) a structure aware self-attention model (StA-PLAN)
that incorporates tree structure information in the transformer network, and
(2) a hierarchical token and post-level attention model (StA-HiTPLAN) that
learns a sentence representation with token-level self-attention. To the best
of our knowledge, we are the first to evaluate our models on two rumor
detection data sets: the PHEME data set as well as the Twitter15 and Twitter16
data sets. We show that our best models outperform current state-of-the-art
models for both data sets. Moreover, the attention mechanism allows us to
explain rumor detection predictions at both token-level and post-level.
