Spatial Classifications

The linkages between geography and Twitter have been addressed in part by an ongoing interest in social sensing systems and situational awareness (SA) efforts. Many researchers have flocked to build more precise social-sensing systems for identifying distress amid natural

disasters. These often involve the identification of geographic cues (places) in relation to an event, usually a drastic and unusual one. In 2015, disaster relief-related work represented 27% of reviewed studies using spatiotemporal elements of Twitter (Steiger, de Albuquerque, and Zipf 2015, 816). Tweets have been a primary focus in the field of SA, a body of literature which “assists in positing helpful processes and strategies for those seeking awareness in emergency situations” (Vieweg et al. 2010, 1079), with a hefty amount of literature on crisis events: a subfield of SA known as ‘crisis informatics’ (Starbird and Palen 2012; Palen and Anderson 2016). Just as Rogers noted how it became of value in Twitter research to qualify reliable accounts “from the ground and from online for event-following” (2013, 5), studies with a prime focus on SA during crisis events have fostered several classifications of their own, albeit driven primarily by a focus on the informational credibility of tweets (i.e. the extent to which they reflect real-world occurrences). It is especially here where the concept of AGI fits, since social media is valued for its in-situ, observational quality. Some researchers have preferred to stick to geotagged tweets for SA research (Crooks et al. 2013), yet identification of locative expressions and toponyms (i.e. geoparsing) on Twitter has gained more attention for informing relief efforts and mapping the development of disaster events (Gelernter and Mushegian 2011; Andrea, Stuart, and Laurissa 2013; Panteras et al. 2015). The most insightful studies here have been small-data, analytic approaches that disregarded geotagged material completely (Starbird and Palen 2010, 2012; Vieweg et al. 2010; Truelove, Vasardani, and Winter 2015; Palen and Anderson 2016).

Vieweg et al. (2010) provided much-needed insight on the differing temporal behaviour of tweets depending on the event, as well as the need to identify users who provide useful

39 information. Their much-cited piece was an in-depth analysis of thousands of tweets from two natural disasters from 2009. They observed differences in how users revealed places in tweet content and how spatial features were referred to implicitly as the event carried on in time. After an exhaustive qualitative content analysis which resulted in several thematic and

communicational categories (e.g. weather, advice, evacuation information), they found

statistically significant differences between their distribution in both the flood and the wildfire corpora. A temporal analysis of these tweets also revealed how the two crises had different visibility in both their buildup and aftermath. In the end, they found that it was most effective to identify “high-yield Twitterers” (p. 1086) who consistently provide curated and informative content. Other studies have since shown interest in identifying high-quality users based on geo- and topical-relevance to protests (Kumar et al. 2013).

Starbird and Palen (2010) found that crowdsourced intelligence of Twitter was useful in identifying high-quality tweets, and shed light on the kind of information shared at different geographic scales during an event. Using the same dataset as Vieweg et al., they suggested that retweets could be used as a noise-filtering mechanism for identifying informative and relevant tweets during emergencies, yet they also pointed to how local and international users retweeted differently, with more abstracted content being spread abroad and locals retweeting more detailed info relevant to on-the-ground, localized awareness and response. In both cases, tweets that were retrieved from the chosen queries were tagged manually as on- or off-topic (indeed, there was no location-filter, so any mention of red river anywhere else in the world would have been captured by these data harvests and cleaned thereafter). In 2012, they followed up with a qualitative analysis of retweeting behaviour during the 2011 ‘Arab Spring’ in Egypt (Starbird and Palen 2012). They divided users who retweeted by their self-reported profile location to gauge how certain messages were disseminated internationally or locally. They found a distinction between local users who were sometimes virally retweeted by more internationally-based users who contributed consistently to more popular and diverse feeds. They also explored how certain messages or memes were more likely to be carried internationally and out of more restricted, local twitterspheres. This all points to how various versions of an event propagate differently across spheres of users.

More recently, Truelove, Vasardani and Winter (2015) used the query “bushfire” and developed a grounded framework for coding tweet content based on the credibility of their

40 content in their reflection of real-world events (i.e. degree to which they reported actual physical happenings). They differentiated between witness, impact and relayed accounts to characterize the ways in which tweets reported Bushfires in rural Australia at different levels of lived or mediated observation, indicated both through coordinate metadata and place names contained in content. Their inquiry aimed to develop a theoretical framework on which more automated data mining methods for place descriptions could enable an extraction of event information.

It is hoped that insights from SA having to do with ‘crisis informatics’ could contribute to more consistent and revelatory, local forms of ‘urban informatics’ (Zimmerman et al. 2016, 1). The literature on SA is relevant for its astute breakdown of Twitter’s spatial dimensions and brings us a step closer to an exploration of how tweets have been regarded for how they reveal place. Yet they only hint at the different ways in which tweets may be revelatory of places. These studies generally have an interest in evaluating the geographical or informational relevance of users to specific events to better retrieve actionable information from microblogs, as

demonstrated by a focus on ‘high-yield twitterers’ and on-the-ground witness accounts, drawing a line between uninformative, self-interested tweets and more informative ones (Vieweg et al. 2010; Truelove, Vasardani and Winter 2015). All of them conclude that users that are physically closer to the event provide more detailed and useful information. Yet there tends to be more effort made in breaking down the informative tweets by their level of credibility, as defined by how reflective they are of direct or mediated experience. This literature suggests that the

revelation of toponyms and locative expressions in Twitter content may be highly dependent on real-world occurrences either in mediated form (e.g. the news and ‘relayed impact accounts’), or directly. Before exploring how place is revealed in Twitter content more closely through its content, we will do a review of which kinds of geographic indicators exist on Twitter and how they have been employed in research.