{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/who-blames-whom-in-a-crisis-detecting-blame","title":"Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks","arxiv_id":"1904.10637","date":"2019-04-24","proceeding":null,"authors":["Shuailong Liang","Olivia Nicol","Yue Zhang"],"abstract":"Blame games tend to follow major disruptions, be they financial crises,\nnatural disasters or terrorist attacks. To study how the blame game evolves and\nshapes the dominant crisis narratives is of great significance, as sense-making\nprocesses can affect regulatory outcomes, social hierarchies, and cultural\nnorms. However, it takes tremendous time and efforts for social scientists to\nmanually examine each relevant news article and extract the blame ties (A\nblames B). In this study, we define a new task, Blame Tie Extraction, and\nconstruct a new dataset related to the United States financial crisis\n(2007-2010) from The New York Times, The Wall Street Journal and USA Today. We\nbuild a Bi-directional Long Short-Term Memory (BiLSTM) network for contexts\nwhere the entities appear in and it learns to automatically extract such blame\nties at the document level. Leveraging the large unsupervised model such as\nGloVe and ELMo, our best model achieves an F1 score of 70% on the test set for\nblame tie extraction, making it a useful tool for social scientists to extract\nblame ties more efficiently.","url_abs":"http://arxiv.org/abs/1904.10637v1","url_pdf":"http://arxiv.org/pdf/1904.10637v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"who-blames-whom-in-a-crisis-detecting-blame","repo_url":"https://github.com/Shuailong/BlamePipeline","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"elmo","method_name":"ELMo"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.10637","atlas_url":"https://app.syntology.ai/?focus=1904.10637","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}