CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter

Koylu, Caglar; Larson, Ryan; Dietrich, Bryce J.; Lee, Kang-Pyo

doi:10.1080/15230406.2018.1510343

Cited by 15 publications

(22 citation statements)

References 23 publications

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“…Prior studies relative to the utility of social media in understanding public opinions show temporal and spatial variations [ 21 – 24 ]. Through the investigation of the geography of Twitter topics in London, Lansley and Longley (2016) [ 23 ] found that topics and attitudes expressed through tweets varied substantially across places and were associated with the demographic and socio-economic characters of the users.…”

Section: Introductionmentioning

confidence: 99%

“…Through the investigation of the geography of Twitter topics in London, Lansley and Longley (2016) [ 23 ] found that topics and attitudes expressed through tweets varied substantially across places and were associated with the demographic and socio-economic characters of the users. Koylu et al (2018) [ 21 ] investigated the online public discourse and sentiment across space and time towards an immigration policy implemented in 2017, and their study manifested that such policy highlighted important partisan division within U.S. states. The opinion variations on political topics can be attributed to the inferred characteristics of online users.…”

Section: Introductionmentioning

confidence: 99%

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Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis

Erfani

Wang

et al. 2021

PLoS ONE

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Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a “supporting” or “opposing” class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis

Erfani

Wang

et al. 2021

PLoS ONE

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show abstract

“…Sentiment analysis (SA) is a technique of identifying and extracting the sentiment associated with a piece of text data. It is an area of computational linguistics and NLP that analyses subjective information such as opinions and emotions expressed in the text [93]. Two primary methods (lexicon-based and corpus-based) are available for conducting SA.…”

Section: Sentiment Analysismentioning

confidence: 99%

Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

Brzustewicz

Singh

2021

Energies

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By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA) algorithm for topic modeling and the Louvain algorithm for semantic network clustering. NRC emotion lexicon was used for sentiment analysis. The LDA model discovers six topics: organic food consumption, food waste, vegan food, sustainable tourism, sustainable transport, and sustainable energy consumption. While the Louvain algorithm detects four clusters—lifestyle and climate change, responsible consumption, energy consumption, and renewable energy, sentiment analysis results show more positive emotions among the users than the negative ones. The study contributes to existing literature by providing a fresh perspective on various interconnected topics of sustainable consumption that bring global consumption to a sustainable level.

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“…migración fue caracterizada -entre otros-por puntos de vista extremos y racistas (Nail, 2016). Otro ejemplo de escalada en el discurso fue el provocado por la orden ejecutiva del presidente Donald Trump en enero de 2017, que se proponía inhibir el ingreso al suelo estadounidense de ciudadanos de siete "países musulmanes"; dicho evento fue motivo de una importante polarización en el debate público (Koylu et al, 2018). En su conjunto, estos eventos se dan en contextos nacionales caracterizados tradicionalmente por la instrumentación de la retórica migratoria por fuertes grupos tanto anti como promigrantes (Ferra y Nguyen, 2017;Koylu et al, 2018).…”

Section: Conflictos Migratorios Detonadores Del Discurso Públicounclassified

“…15, 2021 e-ISSN 2594-0279 https://doi.org/10.33679/rmi.v1i1.2172 3 esfera pública, se asiste a una profunda mutación de los medios detonadores del debate público accesible, transnacional y articulado de la modalidad presencial a virtual (Bouvier, 2019;Paulussen y Harder 2014). En el marco de esta dinámica, las redes sociales lograron posicionarse rápidamente al centro del paisaje mediático, involucrándose en polémicas trascendentes como en el caso del debate migratorio (Ferra y Nguyen, 2017;Koylu, Larson, Dietrich y Lee, 2018;Nguyen 2016). Como tecnología mediática, Twitter logra destacar a nivel global por su especificidad volcada a la interacción instantánea con contenidos cortos que convierten a esta plataforma en la favorita de gobernantes y gobernados (Bruns y Stieglitz, 2014;Meraz y Papacharissi, 2013;Tandoc y Johnson, 2016).…”

Section: Introductionunclassified

Crisis de la caravana de migrantes: Algunas realidades sobre el discurso público en Twitter

Toudert

2021

Migr. Inter

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The study aims to exploreTwitter content tofind out the confrontational structure of onlinepublic discourseduring the migrant caravan crisisof2018. To carry out this approach, anexploratory quantitative method was chosen to analyzea representative universe of the messages published on the platform from January1 to February 15, 2019. The findings indicate —among others—that the public discourse on the caravan is transnational, widely stimulated by the media,and for the most part, expresses neutral sentiment. However, the articulation of the media landscape and the rhetorical structure of the migrationcrisis seem to exhibit similarities and differences between countries of receiving tradition and transit. For thelatter, it is suggested to extendthe research to othertechnological means involved in the construction/deconstruction of the migratory narrative.

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CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter

Cited by 15 publications

References 23 publications

Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis

Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis

Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

Crisis de la caravana de migrantes: Algunas realidades sobre el discurso público en Twitter

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