Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks
This work addresses the time-consuming task of extracting blame ties for social scientists studying crisis narratives, though it is incremental as it applies existing neural network methods to a new domain-specific dataset.
The authors tackled the problem of manually extracting blame ties from news articles by defining a new task, Blame Tie Extraction, and constructing a dataset from financial crisis news; their BiLSTM model achieved an F1 score of 70% on the test set, enabling more efficient analysis for social scientists.
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.