Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain)

[EN] This paper presents the big data architecture and work flow used to download georeferenced tweets, store them in a NoSQL database, analyse them using the Apache Spark framework, and visualize the results. The study covers a complete year (from December 10, 2016 to December 10, 2017) in the city of Valencia (Eastern Spain), which is considered to be the third most important in Spain, having a population of nearly 800,000 inhabitants and a size of 135 km(2). The concepts of a specific event map and a specific event map with positive or negative sentiment are developed to highlight the location of an event. This approach is undertaken by subtracting the heat map of a specific day from the mean daily heat map, which is obtained by taking into account the 365 days of the studied period. This paper demonstrates how the proposed analysis from tweets can be used to depict city events and discover their spatiotemporal characteristics. Finally, the combination of all daily specific events maps in a single map, leads to the conclusion that the city of Valencia city has appropriate urban infrastructures to support these events.The authors would like to thank the comments and suggestions of the anonymous reviewers and the editor, which have helped to improve the original version.Martín Furones, ÁE.; Anquela Julián, AB.; Cos-Gayón López, FJ. (2019). Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain). Cities. (86):37-50. https://doi.org/https://doi.org/10.1016/j.cities.2018.12.014S37508

Advantageous comparison: using twitter responses to understand similarities between cybercriminals (“Yahoo boys”) and politicians (“Yahoo men”)

This article is about the manifestations of similarities between two seemingly distinct groups: cybercriminals and politicians. Which linguistic strategies do Twitter users use to express their opinions on cybercriminals and politicians? The study undertakes a qualitative analysis of ‘engaged’ tweets of a Nigerian law enforcement agency. We analyzed and coded over 100,000 ‘engaged’ tweets based on a component of mechanisms of moral disengagement (i.e., advantageous comparison), a linguistic device. The results reveal how respondents defend the actions of online fraudsters (“the powerless group”) by strategically comparing them to the wrongful acts of corrupt politicians (“the powerful group”). Similarly, the results show how respondents positioned this linguistic strategy to compare “the powerless group” (online fraudsters) and “the powerful group” (politicians) in society. Indeed, tweet responses suggest that the Economic and Financial Crimes Commission (EFCC) generally looks downwards for culprits (i.e., online fraudsters) while ignoring fraudulent politicians. We conclude that the process by which some actions are interpreted as a crime compared to others is a moral enterprise

Twitter, sentimientos y precandidatos presidenciales. Comunicación en tiempos de paro nacional

In recent years, social networks have become a fundamental tool in electoral campaigns. Specifically, Twitter® has become a campaign communication channel of vital importance, since it allows to generate opinion, make proposals known and position candidates. This paper investigates how the eleven pre-candidates with the highest vote intention for the presidency of Colombia employed Twitter® in their communications during the National Strike of April 2021. In such communication between presidential pre-candidates and potential voters, the feelings generated in response to such interaction have an impact on emotions. Using R’s tidytext package, a total of 18,093 tweets were analyzed for the eleven verified accounts of the selected presidential pre-candidates on Twitter® and, specifically, 2,700 tweets that are related to the general climate of the national strike. This allowed us to establish the relevance of these topics in the communication of the presidential pre-candidates. In addition, we were able to understand which feelings and emotions these communications generated in potential voters.En los últimos años, las redes sociales se han convertido en una herramienta fundamental en las campañas electorales. Específicamente, Twitter® se ha vuelto un canal de comunicación de campaña de vital importancia, ya que permite generar opinión, dar a conocer propuestas y posicionar candidaturas. El presente trabajo investiga cómo las y los once precandidatos con mayor intención de voto a la presidencia de Colombia llevaron a cabo la comunicación en Twitter® durante el Paro Nacional de abril de 2021. En aquella comunicación entre las y los precandidatos a la presidencia y las y los potenciales votantes, los sentimientos que se generan como respuesta a esa interacción tienen un impacto sobre las emociones. Con el paquete tidytext de R se analizaron un total de 18.093 tuits para las once cuentas verificadas de las y los precandidatos a la presidencia seleccionadas en Twitter® y, específicamente, los 2.700 tuits que están relacionados con temas de coyuntura general sobre el paro nacional. Esto permitió establecer la relevancia de aquellos temas en la comunicación de las y los precandidatos a la presidencia. Además, se pudo entender qué sentimientos y emociones generaron esas comunicaciones en las y los potenciales votantes

Community stress and resilience during covid-19: Assessing the emotional profile of the City of Hamilton using a social media analysis

This study investigated stress and resilience at the neighbourhood level in Hamilton Ontario in pre- and peri-pandemic conditions using a social media analysis. Sentiment analysis of geo-located Twitter posts produced within Hamilton census tract boundaries was conducted using Stresscapes and EMOTIVE, validated software that extract and code emotional information from human language expressions about stress and hope (a proxy for stress), respectively. Baseline levels of both emotions were measured using aggregate scores at the census tract level in Hamilton from tweets produced during two pre-pandemic periods (March 2019 to July 2019; and August 2019 to February 2020), with a replication analysis corresponding to the first (March - July 2020), second (August 2020 - February 2021) and third (March 2021-July 2021) waves of the pandemic. The spatial distribution of stress and hope across the five time periods (pre- and peri-pandemic) was visualized using a geographic information system. Candidate explanatory variables (including COVID-19 cases count, visible minority status, educational attainment, household income, and household size) were examined for significant bivariate correlations with the change in stress emotions within neighbourhoods across pre- and peri-pandemic periods. Baseline hope was examined as an effect modifier of any significant relationships between explanatory variables and stress. Results suggest that variation between stress and hope emotions exist between Hamilton census tracts (n=30) over the five time periods. Among the explanatory variables, household size and household income displayed a strong bivariate correlation to stress; however, baseline hope did not modify the effect on stress of either variable