On Identifying Disaster-Related Tweets: Matching-Based or Learning-Based?
| Autor: | Sumeet Agrawal, Seon Ho Kim, Hien To, Cyrus Shahabi |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Matching (statistics) Situation awareness Computer science media_common.quotation_subject Population 0211 other engineering and technologies 02 engineering and technology Machine Learning (cs.LG) Computer Science - Information Retrieval 0202 electrical engineering electronic engineering information engineering Selection (linguistics) Quality (business) Social media education media_common 021110 strategic defence & security studies education.field_of_study Emergency management business.industry Sentiment analysis 020207 software engineering Data science Computer Science - Learning business Information Retrieval (cs.IR) |
| Zdroj: | BigMM |
| DOI: | 10.1109/bigmm.2017.82 |
| Popis: | Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach. |
| Databáze: | OpenAIRE |
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