Analysis of Behavioral Facilitation Information During Disasters Based on Reader Attributes and Personality Traits

Akiyo Nadamoto, Kosuke Wakasugi, Yu Suzuki, Tadahiko Kumamoto

Abstract


During disasters, a large volume of messages are posted on social networking services (SNS). Some of these messages include ``behavioral facilitation information,'' which either encourages or discourages specific actions. However, the interpretation of such information is contingent upon the personality traits of the individuals affected. In this study, we hypothesize that victims' personality traits influence their perception of behavioral facilitation information, and we analyze the characteristics of these differences. Focusing on typhoons, we propose a method for extracting behavioral facilitation information from posts on X (formerly Twitter) during typhoon-related disasters. The extracted behavioral facilitation information is then classified into four content-based categories: ``suggest,'' ``inhibition,'' ``encouragement,'' and ``wish.'' Furthermore, we categorize individual personality traits into five dimensions (the Big Five), and also consider their age and sex.
We then analyze how the perception of each type of behavioral facilitation information varies according to these traits. Our analysis reveals that during disasters, the interpretation of behavioral facilitation information exhibits distinct and consistent patterns depending on the personality traits of the victims.


Full Text:

PDF

References


Brett D. M. Peary, Rajib Shaw, and Yukiko Takeuchi (2012). Utilization of Social Media in the East Japan Earthquake and Tsunami and its Effectiveness. Journal of Natural Disaster Science, 34(1), 3–18.

https://doi.org/10.2328/jnds.34.3

Keiichi Mizuka, Yu Suzuki, and Akiyo Nadamoto (2019). A Behavioral Facilitation Tweet Detection Method. Proc. IEEE International Conference on Big Data and Smart Computing (BigComp 2019), 1–4.

https://doi.org/10.1109/BIGCOMP.2019.8679261

Yoshiki Yoneda, Yu Suzuki, and Akiyo Nadamoto (2019). Detection of Behavioral Facilitation information in Disaster Situation. Proc. 21st International Conference on Information Integration and Web-based Applications & Services (iiWAS2019), 255–259.

https://doi.org/10.1145/3366030.3366083

F. Yamamoto, Y. Suzuki, and A. Nadamoto (2021). Extraction and Analysis of Regionally Specific Behavioral Facilitation Information in the Event of a Large-scale Disaster. Proc. IEEE/WIC/ACM International Conference on Web Intelligence, 538–543.

https://doi.org/10.1145/3486622.3493942

Xiaodong Ning, Lina Yao, Boualem Benatallah, Yihong Zhang, Quan Z. Sheng, and Salil S. Kanhere (2019). Source-Aware Crisis-Relevant Tweet Identification and Key Information Summarization. ACM Transactions on Internet Technology (TOIT), 19(3), Article 41.

https://doi.org/10.1145/3230710

Udit Paul, Alexander Ermakov, Michael Nekrasov, Vivek Adarsh, and Elizabeth Belding (2020). #Outage: Detecting Power and Communication Outages from Social Networks. Proc. The Web Conference 2020, 1819–1829.

https://doi.org/10.1145/3366423.3380246

M. Yasin Kabir, Sergey Gruzdev, and Sanjay Madria (2020). STIMULATE: A System for Real-time Information Acquisition and Learning for Disaster Management. Proc. 21st IEEE International Conference on Mobile Data Management (MDM), 186–193.

https://doi.org/10.1109/MDM48529.2020.00044

Ankit Gupta, Fatemeh Mohajeri, and Babak Mirbaha (2021). Studying the Role of Personality Traits on the Evacuation Choice Behavior Pattern in Urban Road Network in Different Severity Scales of Natural Disaster. Advances in Civil Engineering, Article ID 6615445, 16 pages.

https://doi.org/10.1155/2021/6615445

Kamol Chandra Roy, Samiul Hasan, Arif Mohaimin Sadri, and Manuel Cebrian (2020). Understanding the Efficiency of Social Media Based Crisis Communication during Hurricane Sandy. International Journal of Information Management, 52, 102060.

https://doi.org/10.1016/j.ijinfomgt.2020.102060

Lu Zhou, Wenbo Wang, and Keke Chen (2016). Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones. Proc. 25th International Conference on World Wide Web (WWW 2016), 603–612.

https://doi.org/10.1145/2872427.2883080

David Valle-Cruz, Asdrúbal López-Chau, and Rodrigo Sandoval-Almazán (2020). Impression Analysis of Trending Topics in Twitter with Classification Algorithms. Proc. International Conference on Theory and Practice of Electronic Governance (ICEGOV 2020), 430–441.

https://doi.org/10.1145/3428502.3428532

Sanetoshi Yamada, Keisuke Utsu, and Osamu Uchida (2019). An Analysis of Tweets Posted During 2018 Western Japan Heavy Rain Disaster. Proc. IEEE International Conference on Big Data and Smart Computing (BigComp 2019), 1–8.

https://doi.org/10.1109/BIGCOMP.2019.8679163

Shuji Nishikawa, Osamu Uchida, and Keisuke Utsu (2019). Analysis of Rescue Request Tweets in the 2018 Japan Floods. Proc. International Conference on Information Technology and Computer Communications (ITCC 2019), 29–36.

https://doi.org/10.1145/3343147.3343156

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

https://doi.org/10.48550/arXiv.1907.11692

Kosuke Wakasugi, Futo Yamamoto, Yu Suzuki, and Akiyo Nadamoto (2023). Feature Analysis of Regional Behavioral Facilitation Information Based on Source Location and Target People in Disaster. Big Data Analytics and Knowledge Discovery: 25th International Conference, DaWaK 2023, 224–232.

https://doi.org/10.1007/978-3-031-40378-6_18

Diederik P. Kingma and Jimmy Ba (2014). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980.

https://doi.org/10.48550/arXiv.1412.6980

Futo Yamamoto, Tadahiko Kumamoto, and Akiyo Nadamoto (2022). Analysis of Behavioral Facilitation Tweets Considering the Emotion of Disaster Victims. Proc. 15th IEEE International Conference on Social Computing and Networking (SocialCom 2022), 251–257.

https://doi.org/10.1109/SocialCom55681.2022.00047

Jason Wei, Kai Zou, Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (2019). EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. Proc. 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 6382–6388.

https://doi.org/10.18653/v1/D19-1670

Atsushi Oshio, Shingo Abe, and Pino Cutrone (2012). Development, Reliability, and Validity of the Japanese Version of Ten Item Personality Inventory (TIPI-J). The Japanese Journal of Personality, 21(1), 40–52.

https://doi.org/10.2132/personality.21.40




DOI: https://doi.org/10.31449/inf.v49i3.10525

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.