Abstract
Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments’ needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens’ transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens’ concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment.
Highlights
Engaging citizens and other stakeholders is considered as an essential step in government decision-making [1]
A more generic approach is used here to build a local lexicon from local news, municipal reports, and articles based on the wlAoidcmaelolpryelagnuennsienergdiccatopfnpterxotaidicshftihmseuneseetderdihcoe.rfeTlothocaiblsluyillsedpxaeiclcoifocicanlrleeissxoiuctohrcneefsnr(oeum.gs.l,eoocdanltaonlseowginys,pmtruauitnniticnoipgaedlvarteaaplsouertasts,t,eeatcnt.d)h. e relevance of the text messagaretisclebsabsaesdedoon tahelawnidgeulyaugseedmtfo-iddfemlientrgic.aTphpisrloexaiccohn tihs athtenisufsoedunasdintpoutbteo eevffaelucattievtehefor identifying relevant short rteelexvtanmceeosfstahegteesxtfmroesmsagseos cbiaaseldmoneda liaang[u6a2g]e. modeling approach that is found to be effective for identifying relevant short text messages from social media [62]
We evaluate the relevance of geosocial media messages based on the language model
Summary
Engaging citizens and other stakeholders is considered as an essential step in government decision-making [1]. Social media often contain information about public opinions and perceptions that is comparable to public comments collected through traditional public participation approaches [6]. Unlike citizen surveys or interviews, social media data are the outputs of users’ communication These data are unstructured, vary in quality, and often of unknown relevance to local governments’ need [13]. Further complications arise from the fact that only a small portion of social media are tagged with explicit geographic coordinates and that these data vary widely in their geographic representativeness within and across urban areas [14] Their effectiveness to supporting public engagement needs to be examined critically through empirical studies [6]. We conclude the paper with suggestions for future research opportunities (Section 5)
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