skip to main content
10.1145/3568562.3568651acmotherconferencesArticle/Chapter ViewAbstractPublication PagessoictConference Proceedingsconference-collections
research-article

Methods of Calculating Usefulness Ratings of Behavioral Facilitation Tweets in Disaster Situations

Published: 01 December 2022 Publication History

Abstract

When a severe disaster strikes, people transmit various information about the disaster. This kind of information is circulated especially on Twitter, an extremely popular social networking service. Especially, information that encourages people to take some kind of action or not to take any action is called “behavioral facilitation information”, which is known to influence the behaviors of many people. However, the volume of such information is enormous. Information transmitted by numerous unspecified people is not always useful for various victims. As described herein, we propose methods for calculating the usefulness ratings of tweets that include behavioral facilitation information, and which are posted during earthquake disasters. Specifically, we present several methods for calculating the usefulness ratings of tweets as real numbers from 0 to 3 according to a four-step scale (not useful at all, not very useful, somewhat useful, very useful). Then we compare the methods in terms of accuracy. Based on that comparison, the most suitable method for earthquake disasters can be determined. According to an accuracy evaluation experiment using behavioral facilitation tweets actually posted when a certain earthquake occurred, the method using Bidirectional Encoder Representations from Transformers (BERT) achieved the best accuracy (average RMSE or Root-Mean-Square Error calculated using five-fold cross-validation) of 0.39, which is sufficiently high even for unknown data. This result implies that the method can achieve sufficiently high accuracy, even for unknown data.

References

[1]
Krishna Kanth A, Abirami S, Chitra P, and Gayathri Sowmya G. 2019. Real Time Twitter Based Disaster Response System for Indian Scenarios. In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). 82–86. https://doi.org/10.1109/HiPCW.2019.00029
[2]
Masayuki Asahara. 2018. NWJC2Vec: Word embedding dataset from ‘NINJAL Web Japanese Corpus’. Terminology: International Journal of Theoretical and Applied Issues in Specialized Communication 24, 2 (Feb. 2018), 7–25.
[3]
Zahra Ashktorab, Christopher Brown, Manojit Nandi, and Aron Culotta. 2014. Tweedr: Mining twitter to inform disaster response. In ISCRAM. Citeseer, 269–272.
[4]
Moumita Basu, Anurag Shandilya, Kripabandhu Ghosh, and Saptarshi Ghosh. 2018. Automatic matching of resource needs and availabilities in microblogs for post-disaster relief. In Companion Proceedings of the The Web Conference 2018. 25–26.
[5]
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the association for computational linguistics 5 (2017), 135–146.
[6]
Mark A Cameron, Robert Power, Bella Robinson, and Jie Yin. 2012. Emergency situation awareness from twitter for crisis management. In Proceedings of the 21st international conference on world wide web. 695–698.
[7]
Wang Gao, Lin Li, Xun Zhu, and Yuwei Wang. 2020. Detecting Disaster-Related Tweets Via Multimodal Adversarial Neural Network. IEEE MultiMedia 27, 4 (2020), 28–37.
[8]
M Yasin Kabir, Sergey Gruzdev, and Sanjay Madria. 2020. STIMULATE: A System for Real-time Information Acquisition and Learning for Disaster Management. In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 186–193.
[9]
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT. 4171–4186.
[10]
Dheeraj Kumar and Satish V Ukkusuri. 2018. Utilizing geo-tagged tweets to understand evacuation dynamics during emergencies: A case study of Hurricane Sandy. In Companion Proceedings of the The Web Conference 2018. 1613–1620.
[11]
Keiichi Mizuka, Yu Suzuki, and Akiyo Nadamoto. 2019. A behavioral facilitation tweet detection method. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 1–4.
[12]
Hajime Morita, Daisuke Kawahara, and Sadao Kurohashi. 2015. Morphological analysis for unsegmented languages using recurrent neural network language model. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2292–2297.
[13]
Anastasia Moumtzidou, Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2018. Flood relevance estimation from visual and textual content in social media streams. In Companion Proceedings of the The Web Conference 2018. 1621–1627.
[14]
Akiyo Nadamoto, Mai Miyabe, and Eiji Aramaki. 2013. Analysis of microblog rumors and correction texts for disaster situations. In Proceedings of International Conference on Information Integration and Web-based Applications & Services. 44–52.
[15]
Thi Huyen Nguyen and Koustav Rudra. 2022. Towards an Interpretable Approach to Classify and Summarize Crisis Events from Microblogs. In Proceedings of the ACM Web Conference 2022. 3641–3650.
[16]
Shuji Nishikawa, Osamu Uchida, and Keisuke Utsu. 2019. Analysis of rescue request tweets in the 2018 Japan floods. In Proceedings of the 2019 International Conference on Information Technology and Computer Communications. 29–36.
[17]
Udit Paul, Alexander Ermakov, Michael Nekrasov, Vivek Adarsh, and Elizabeth Belding. 2020. # Outage: Detecting Power and Communication Outages from Social Networks. In Proceedings of The Web Conference 2020. 1819–1829.
[18]
Jishnu Ray Chowdhury, Cornelia Caragea, and Doina Caragea. 2019. Keyphrase extraction from disaster-related tweets. In The world wide web conference. 1555–1566.
[19]
Koustav Rudra, Subham Ghosh, Niloy Ganguly, Pawan Goyal, and Saptarshi Ghosh. 2015. Extracting situational information from microblogs during disaster events: a classification-summarization approach. In Proceedings of the 24th ACM international on conference on information and knowledge management. 583–592.
[20]
Takeshi Sakaki, Fujio Toriumi, Kosuke Shinoda, Kazuhiro Kazama, Satoshi Kurihara, Itsuki Noda, and Yutaka Matsuo. 2013. Regional analysis of user interactions on social media in times of disaster. In Proceedings of the 22nd International Conference on World Wide Web. 235–236.
[21]
Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45, 11 (1997), 2673–2681.
[22]
Yu Suzuki, Yoshiki Yoneda, and Akiyo Nadamoto. 2021. Analysis of Behavioral Facilitation Tweets for Large-Scale Natural Disasters Dataset Using Machine Learning. In International Conference on Database and Expert Systems Applications. Springer, 161–169.
[23]
Hien To, Sumeet Agrawal, Seon Ho Kim, and Cyrus Shahabi. 2017. On identifying disaster-related tweets: Matching-based or learning-based?. In 2017 IEEE third international conference on multimedia big data (BigMM). IEEE, 330–337.
[24]
Sanetoshi Yamada, Keisuke Utsu, and Osamu Uchida. 2019. An analysis of tweets posted during 2018 Western Japan heavy rain disaster. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 1–8.
[25]
Yoshiki Yoneda, Yu Suzuki, and Akiyo Nadamoto. 2019. Detection of Behavioral Facilitation information in Disaster Situation. In Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. 255–259.
[26]
Akio Yuzawa, Hiroyoshi Ichikawa, and Aki Kobayashi. 2018. Extracting Tweets Related to Disaster Information by Using Multiple Co-occurrence Relation of Words. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 321–326.

Cited By

View all
  • (2024)A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster AnalyticsSustainability10.3390/su1607274216:7(2742)Online publication date: 26-Mar-2024
  • (2024)A systematic review on the dimensions of open-source disaster intelligence using GPTJournal of Economy and Technology10.1016/j.ject.2024.03.0042(62-78)Online publication date: Nov-2024

Index Terms

  1. Methods of Calculating Usefulness Ratings of Behavioral Facilitation Tweets in Disaster Situations

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      SoICT '22: Proceedings of the 11th International Symposium on Information and Communication Technology
      December 2022
      474 pages
      ISBN:9781450397254
      DOI:10.1145/3568562
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 01 December 2022

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. BERT
      2. BLSTM
      3. Behavioral Facilitation Information
      4. Disaster Assistance
      5. RoBERTa
      6. Twitter

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      SoICT 2022

      Acceptance Rates

      Overall Acceptance Rate 147 of 318 submissions, 46%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)24
      • Downloads (Last 6 weeks)2
      Reflects downloads up to 23 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster AnalyticsSustainability10.3390/su1607274216:7(2742)Online publication date: 26-Mar-2024
      • (2024)A systematic review on the dimensions of open-source disaster intelligence using GPTJournal of Economy and Technology10.1016/j.ject.2024.03.0042(62-78)Online publication date: Nov-2024

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media