A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

Abstract

Since its inception, Twitter has played a major role in real-world events-especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users' profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location with much larger distance errors or at a city-level resolution at best.

Event classification and location prediction from tweets during disasters

“…Some studies like Zhang et al (2015) and Laylavi et al (2016b) showed more interest in user profiles to better understand the origin of tweets. Zhang et al (2015) detected burst words from micro-blogging text streams using term co-occurrence information and user social relation information.…”

“…The future popularity of an event was also predicted using the historical popularity of an event data. Laylavi et al (2016b) introduced a multi-elemental location inference method to predict the location of tweets by exploiting the textual content, user profile location and place labeling. Three granularity levels of location name classes were defined to look up the location references from the location associated elements.…”

Location reference identification from tweets during emergencies: A deep learning approach

“…Several examples were seen when the news was first reported on Twitter, such as an airplane crash over the Hudson River in New York in the year 2009 (Sakaki et al, 2013), the death of former British Prime Minister Margaret Thatcher in April 2013 1 , and the explosions at the Boston Marathon 2013 1 . In recent years, Twitter has been used extensively in the course of natural and human-made disasters such as earthquakes, floods, fire, terrorist attacks, civil unrest, and so on (Alexander, 2014;Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Luna & Pennock, 2018;Mejri et al, 2017;Mendoza et al, 2010;Sakaki et al, 2013;Singh et al, 2017;Yuan & Liu, 2018). The government and non-government agencies use Twitter in case of crisis so that different rescue operations can leap into action, disseminate information to the wider audience, and recognize floor reality (Imran et al, 2014a(Imran et al, , 2015Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Rossi et al, 2018;Sakaki et al, 2013;Zhou et al, 2017).…”

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

“…Social media data are limited however, because they cannot reflect completed user activities in the real world. We can only observe the activities that an individual shares on social media [7][8][9][10]. In this paper, we focus on developing new ways for delimitating trade areas using social media in ways that overcome the partial and incomplete qualities of this rich data source.…”