Twitter is currently one of the most popular platforms for disseminating information about events happening around the world. Especially but not only for emergency events, it is crucial to know when and where the events are taking place. Unfortunately, identifying the geo-location of an event discussed in Twitter is a very challenging task mainly due to the brevity of the messages (i.e., tweets) and their subjective nature. In the literature, some efforts have been made to address this task, but they are characterized by substantial limitations such as the use of exclusively text analysis techniques, or the need for keywords or possible candidate locations. This paper proposes a new process for automatic event geo-localization which relies on both textual and spatial/temporal use of content posted on Twitter without using some prior knowledge about the event to be located. As shown by experimental results, our proposal achieves a good accuracy rate and outperforms two well-known baseline approaches related to the geo-location of events in Twitter.

Automatic Event Geo-Location in Twitter

Acampora G.;Anastasio P.;Risi M.
;
Tortora G.;
2020

Abstract

Twitter is currently one of the most popular platforms for disseminating information about events happening around the world. Especially but not only for emergency events, it is crucial to know when and where the events are taking place. Unfortunately, identifying the geo-location of an event discussed in Twitter is a very challenging task mainly due to the brevity of the messages (i.e., tweets) and their subjective nature. In the literature, some efforts have been made to address this task, but they are characterized by substantial limitations such as the use of exclusively text analysis techniques, or the need for keywords or possible candidate locations. This paper proposes a new process for automatic event geo-localization which relies on both textual and spatial/temporal use of content posted on Twitter without using some prior knowledge about the event to be located. As shown by experimental results, our proposal achieves a good accuracy rate and outperforms two well-known baseline approaches related to the geo-location of events in Twitter.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4755532
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