{"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/on-identifying-disaster-related-tweets","title":"On Identifying Disaster-Related Tweets: Matching-based or Learning-based?","arxiv_id":"1705.02009","date":"2017-05-04","proceeding":null,"authors":["Hien To","Sumeet Agrawal","Seon Ho Kim","Cyrus Shahabi"],"abstract":"Social media such as tweets are emerging as platforms contributing to\nsituational awareness during disasters. Information shared on Twitter by both\naffected population (e.g., requesting assistance, warning) and those outside\nthe impact zone (e.g., providing assistance) would help first responders,\ndecision makers, and the public to understand the situation first-hand.\nEffective use of such information requires timely selection and analysis of\ntweets that are relevant to a particular disaster. Even though abundant tweets\nare promising as a data source, it is challenging to automatically identify\nrelevant messages since tweet are short and unstructured, resulting to\nunsatisfactory classification performance of conventional learning-based\napproaches. Thus, we propose a simple yet effective algorithm to identify\nrelevant messages based on matching keywords and hashtags, and provide a\ncomparison between matching-based and learning-based approaches. To evaluate\nthe two approaches, we put them into a framework specifically proposed for\nanalyzing disaster-related tweets. Analysis results on eleven datasets with\nvarious disaster types show that our technique provides relevant tweets of\nhigher quality and more interpretable results of sentiment analysis tasks when\ncompared to learning approach.","url_abs":"http://arxiv.org/abs/1705.02009v1","url_pdf":"http://arxiv.org/pdf/1705.02009v1.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":"on-identifying-disaster-related-tweets","repo_url":"https://github.com/infolab-usc/bdr-tweet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}