Clustering Document Parts: Detecting and Characterizing Influence
  Campaigns From Documents
Zhengxiang Wang Owen Rambow
We propose a novel clustering pipeline to detect and characterize influence
campaigns from documents. This approach clusters parts of document, detects
clusters that likely reflect an influence campaign, and then identifies
documents linked to an influence campaign via their association with the
high-influence clusters. Our approach outperforms both the direct
document-level classification and the direct document-level clustering approach
in predicting if a document is part of an influence campaign. We propose
various novel techniques to enhance our pipeline, including using an existing
event factuality prediction system to obtain document parts, and aggregating
multiple clustering experiments to improve the performance of both cluster and
document classification. Classifying documents on the top of clustering not
only accurately extracts the parts of the documents that are relevant to
influence campaigns, but also capture influence campaigns as a coordinated and
holistic phenomenon. Our approach makes possible more fine-grained and
interpretable characterizations of influence campaigns from documents.