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| Titel |
Multilingual Analysis of Twitter News in Support of Mass Emergency Events |
| VerfasserIn |
A. Zielinski, U. Bügel, L. Middleton, S. E. Middleton, L. Tokarchuk, K. Watson, F. Chaves |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250065867
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| Zusammenfassung |
| Social media are increasingly becoming an additional source of information for event-based
early warning systems in the sense that they can help to detect natural crises and support
crisis management during or after disasters.
Within the European FP7 TRIDEC project we study the problem of analyzing multilingual
twitter feeds for emergency events. Specifically, we consider tsunami and earthquakes, as one
possible originating cause of tsunami, and propose to analyze twitter messages for capturing
testified information at affected points of interest in order to obtain a better picture of the
actual situation. For tsunami, these could be the so called Forecast Points, i.e. agreed-upon
points chosen by the Regional Tsunami Warning Centers (RTWC) and the potentially
affected countries, which must be considered when calculating expected tsunami
arrival times. Generally, local civil protection authorities and the population are
likely to respond in their native languages. Therefore, the present work focuses on
English as "lingua franca" and on under-resourced Mediterranean languages in
endangered zones, particularly in Turkey, Greece, and Romania. We investigated ten
earthquake events and defined four language-specific classifiers that can be used to detect
natural crisis events by filtering out irrelevant messages that do not relate to the
event.
Preliminary results indicate that such a filter has the potential to support earthquake detection
and could be integrated into seismographic sensor networks. One hindrance in our study is
the lack of geo-located data for asserting the geographical origin of the tweets and thus to be
able to observe correlations of events across languages. One way to overcome this deficit
consists in identifying geographic names contained in tweets that correspond to or which are
located in the vicinity of specific points-of-interest such as the forecast points of the tsunami
scenario. We also intend to use twitter analysis for situation picture assessment, e.g. for
planning relief actions.
At present, a multilingual corpus of Twitter messages related to crises is being assembled,
and domain-specific language resources such as multilingual terminology lists and
language-specific Natural Language Processing (NLP) tools are being built up to help cross
the language barrier. The final goal is to extend this work to the main languages spoken
around the Mediterranean and to classify and extract relevant information from tweets,
translating the main keywords into English. |
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