  Blame games tend to follow major disruptions, be they financial crises,
natural disasters or terrorist attacks. To study how the blame game evolves and
shapes the dominant crisis narratives is of great significance, as sense-making
processes can affect regulatory outcomes, social hierarchies, and cultural
norms. However, it takes tremendous time and efforts for social scientists to
manually examine each relevant news article and extract the blame ties (A
blames B). In this study, we define a new task, Blame Tie Extraction, and
construct a new dataset related to the United States financial crisis
(2007-2010) from The New York Times, The Wall Street Journal and USA Today. We
build a Bi-directional Long Short-Term Memory (BiLSTM) network for contexts
where the entities appear in and it learns to automatically extract such blame
ties at the document level. Leveraging the large unsupervised model such as
GloVe and ELMo, our best model achieves an F1 score of 70% on the test set for
blame tie extraction, making it a useful tool for social scientists to extract
blame ties more efficiently.
