376 research outputs found
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also
disinformation. One prominent case of an event leading to circulation of
disinformation on social media is the MH17 plane crash. Studies analysing the
spread of information about this event on Twitter have focused on small,
manually annotated datasets, or used proxys for data annotation. In this work,
we examine to what extent text classifiers can be used to label data for
subsequent content analysis, in particular we focus on predicting pro-Russian
and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though
we find that a neural classifier improves over a hashtag based baseline,
labeling pro-Russian and pro-Ukrainian content with high precision remains a
challenging problem. We provide an error analysis underlining the difficulty of
the task and identify factors that might help improve classification in future
work. Finally, we show how the classifier can facilitate the annotation task
for human annotators
Multi-Task Learning Improves Performance In Deep Argument Mining Models
The successful analysis of argumentative techniques from user-generated text
is central to many downstream tasks such as political and market analysis.
Recent argument mining tools use state-of-the-art deep learning methods to
extract and annotate argumentative techniques from various online text corpora,
however each task is treated as separate and different bespoke models are
fine-tuned for each dataset. We show that different argument mining tasks share
common semantic and logical structure by implementing a multi-task approach to
argument mining that achieves better performance than state-of-the-art methods
for the same problems. Our model builds a shared representation of the input
text that is common to all tasks and exploits similarities between tasks in
order to further boost performance via parameter-sharing. Our results are
important for argument mining as they show that different tasks share
substantial similarities and suggest a holistic approach to the extraction of
argumentative techniques from text
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
We present the shared task on Fine-Grained Propaganda Detection, which was
organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two
subtasks. FLC is a fragment-level task that asks for the identification of
propagandist text fragments in a news article and also for the prediction of
the specific propaganda technique used in each such fragment (18-way
classification task). SLC is a sentence-level binary classification task asking
to detect the sentences that contain propaganda. A total of 12 teams submitted
systems for the FLC task, 25 teams did so for the SLC task, and 14 teams
eventually submitted a system description paper. For both subtasks, most
systems managed to beat the baseline by a sizable margin. The leaderboard and
the data from the competition are available at
http://propaganda.qcri.org/nlp4if-shared-task/.Comment: propaganda, disinformation, fake news. arXiv admin note: text overlap
with arXiv:1910.0251
- …