• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3211-3229
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• 2022

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

• Asian Journal for Public Opinion Research
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• v.1 no.4
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• pp.249-267
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• 2014

Social media is being increasingly utilized in disaster relief work to identify safety issues, locate displaced-victims, and seek or provide support for those who need help. The presence of social media in disaster management has changed significantly in recent years, as it was not prevalent in the 2008 Wenchuan earthquake, but had become a powerful force in the 2013 Ya'an earthquake. This paper discusses the development of social media in disaster management via making a comparison between how it functioned in the two earthquakes. It examines the following aspects: who are the stakeholders that use social media in the earthquake management; how do they adopt this means in response to the earthquakes; and what are the outcomes of adopting social media with regards to public engagement and collaboration in an emergency event. As Sina Weibo acts as the equivalent of China's Twitter, the methodology relies on an analysis of posts in Weibo. The outcomes primarily show that: (1) authorities, celebrities and the public actively adopted social media for the purpose of information dissemination and resource mobilization; and (2) social media users are both content consumers and content generators in the times of earthquakes. The study concludes that social media as a backchannel communication tool is helpful for government institutions, corporations, and nonprofit organizations to build relationships with their stakeholders in the disaster management cycle. The result will interest academics and emergency management practitioners who are concerned with improving disaster communication.