2 research outputs found
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
This is the proposal for RumourEval-2019, which will run in early 2019 as
part of that year's SemEval event. Since the first RumourEval shared task in
2017, interest in automated claim validation has greatly increased, as the
dangers of "fake news" have become a mainstream concern. Yet automated support
for rumour checking remains in its infancy. For this reason, it is important
that a shared task in this area continues to provide a focus for effort, which
is likely to increase. We therefore propose a continuation in which the
veracity of further rumours is determined, and as previously, supportive of
this goal, tweets discussing them are classified according to the stance they
take regarding the rumour. Scope is extended compared with the first
RumourEval, in that the dataset is substantially expanded to include Reddit as
well as Twitter data, and additional languages are also included
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
Automatically verifying rumorous information has become an important and
challenging task in natural language processing and social media analytics.
Previous studies reveal that people's stances towards rumorous messages can
provide indicative clues for identifying the veracity of rumors, and thus
determining the stances of public reactions is a crucial preceding step for
rumor veracity prediction. In this paper, we propose a hierarchical multi-task
learning framework for jointly predicting rumor stance and veracity on Twitter,
which consists of two components. The bottom component of our framework
classifies the stances of tweets in a conversation discussing a rumor via
modeling the structural property based on a novel graph convolutional network.
The top component predicts the rumor veracity by exploiting the temporal
dynamics of stance evolution. Experimental results on two benchmark datasets
show that our method outperforms previous methods in both rumor stance
classification and veracity prediction.Comment: EMNLP-IJCNLP 201