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Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter / Caglar Koylu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 805 - 832 Titre : Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur Année de publication : 2019 Article en page(s) : pp 805 - 832 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] comportement [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] traitement interactif [Termes descripteurs IGN] Twitter Résumé : (Auteur) Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events. Numéro de notice : A2019-217 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458987 date de publication en ligne : 25/04/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458987 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92689 [article]

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CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter / Caglar Koylu in Cartography and Geographic Information Science, Vol 46 n° 1 (January 2019)  

Public [article]  inCartography and Geographic Information Science > Vol 46 n° 1 (January 2019) . - pp 57 - 71 Titre : CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur ; Ryan Larson, Auteur ; Bryce J. Dietrich, Auteur ; Kang-Pyo Lee, Auteur Année de publication : 2019 Article en page(s) : pp 57 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse du discours [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] cartogramme [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] corpus [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] sentiment [Termes descripteurs IGN] Twitter [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Assessing the impact of events on the evolution of online public discourse is challenging due to the lack of data prior to the event and appropriate methodologies for capturing the progression of tenor of public discourse, both in terms of their tone and topic. In this article, we introduce a geovisual analytics framework, CarSenToGram, which integrates topic modeling and sentiment analysis with cartograms to identify the changing dynamics of public discourse on a particular topic across space and time. The main novelty of CarSenToGram is coupling comprehensible spatiotemporal overviews of the overall distribution, topical and sentiment patterns with increasing levels of information supported by zoom and filter, and details-on-demand interactions. To demonstrate the utility of CarSenToGram, in this article, we analyze tweets related to immigration the month before and after the 27 January 2017 travel ban in order to reveal insights into one of the defining moments of President Trump’s first year in office. Not only do we find that the travel ban influenced online public discourse and sentiment on immigration, but it also highlighted important partisan divisions within the US. Numéro de notice : A2019-012 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1510343 date de publication en ligne : 18/09/2018 En ligne : https://doi.org/10.1080/15230406.2018.1510343 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91661 [article]

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