A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages.

2015, Proceedings of the 24th international conference companion on World Wide Web

Dataset description

A dataset of 5,642 manually annotated tweets in the Italian language. The tweets are related to 4 different natural disasters occurred in Italy between 2009 and 2014. For each tweet is reported:

• tweet ID;
• text;
• source;
• author’s screen name;
• author’s ID;
• latitude and longitude (if available);
• time;
• disaster ID (see below);
• class.

Tweets have been manually annotated by humans and divided among 3 classes according to the information they convey:

• damage class: tweets related to the disaster and carrying information about damages to the infrastructures or on the population;
• no damage class: tweets related to the disaster but not carrying relevant information for the assessment of damages;
• not relevant class: tweets collected while building the dataset, but not related to any disaster (noise).

Dataset Statistics

This dataset comprises Twitter chatter about 4 different natural disasters occurred in Italy, as reported in the following table.

Source (Citation)

S. Cresci, M. Tesconi, A. Cimino and F. Dell’Orletta A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages. Proceedings of the 24th international conference companion on World Wide Web. ACM, 2015.

BibTex

@inproceedings{cresci2015swdm,
    title={A Linguistically-driven Approach to Cross-Event Damage Assessment of Natural Disasters from Social Media Messages},
    author={Cresci, Stefano and Tesconi, Maurizio and Cimino, Andrea and Dell’Orletta, Felice},
    booktitle={Proceedings of the 24th international conference companion on World Wide Web},
    year={2015},
    organization={ACM}
}