Mapping Social Media Texts as the Basis of Place-Making Process

PurposeThis work aims to examine the communication on Twitter of the most responsible companies in Spain to identify the topics covered on corporate social responsibility (CSR) from the perspective of happiness and social marketing. In addition, the profiles of the messages that show an association with the impact of the messages have been identified.Design/methodology/approachAn empirical analysis of the Twitter posts of Spain's ten most responsible companies has been carried out. The methodology of this work combines data mining techniques, sentiment analysis and content analysis, both from a quantitative and qualitative approach.FindingsThe results show that most brand tweets do not deal with CSR-related topics. The topics they address the most are those related to sports and the weather. From the perspective of social marketing, conversational-type tweets are the most published and have achieved the most significant reaction from the public. In addition, four messages' profiles have been identified based on the company and the emotional connotation associated with the impact, giving rise to more outstanding promotion of social causes.Originality/valueOur main contribution to this work has been to value positive communication and social marketing to promote better CSR on Twitter. In this sense, it has been verified that there is a relationship between the public's reaction, the affective connotation and the company that issues the messages.

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“…In total, 2,050 tweets have been recovered. The tool to collect the tweets is Twitonomy (Alkadri et al. , 2015).…”

Section: Methodsmentioning

confidence: 99%

Exploring social responsibility, social marketing and happiness using artificial intelligence, automated text analysis and correspondence analysis

Coronil

Montero

Sánchez

et al. 2023

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“…Once the sampling elements were selected, all content published by the official accounts of the thirteen companies in 2020 was extracted from the Twitter platform. Data extraction, following the procedure recommended by other authors [45][46][47], was performed through the Twitter API by using the service provider Twitonomy. This process yielded 23,670 publications.…”

Section: Sample Design and Data Extractionmentioning

confidence: 99%

How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry

Matosas-López

Romero-Ania

2021

JTAER

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The objective of this research is to identify to what extent volumes, components, time slots, and publication topics improve customer engagement with Spanish automotive brands through social networks. The study considers thirteen brands and the total number of publications created by them in 2020 (23,670 publications) on the social network Twitter. Applying machine learning algorithms followed by multiple linear regression techniques, the authors examine how the variables previously mentioned affect a customer engagement indicator developed for this purpose. The results reveal that while publication components (links, mentions, and hashtags) and the publication time slot do not affect customer engagement, the volume of retweets made by the brand and publications on customer experience topics (without a direct commercial purpose) significantly improve the customer engagement indicator. The authors conclude that customer engagement in social networks can only be improved by conducting exhaustive analyses of activity data for these platforms. However, such analyses must not be done via generic multisector analyses, which only generate superficial and inapplicable knowledge, but rather through detailed studies for each sector.

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“…Once the sample was selected, the researchers extracted from the Twitter platform, all the content published by the official accounts of the ten organizations over a one-year period. Following the procedure of previous studies [23,35], the data were extracted through Twitter's API using the service provider Twitonomy. This process led to the gathering of 21,771 publications, in addition to the recognition obtained by each of them in terms of retweets and favorites.…”

Section: Sample Design and Data Extractionmentioning

confidence: 99%

The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

Matosas-López¹,

Romero-Ania²

2020

Applied Sciences

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The objective of this work is to detect the variables that allow organizations to manage their social network services efficiently. The study, applying machine learning algorithms and multiple linear regressions, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The authors examine (I) the characteristics of the content (publication volumes, publication components, and publication moments) and (II) the message of the content (publication topics). The research considers 21,771 publications and thirty-nine variables. The results show that the recognition obtained through retweets and favorites is conditioned both by the characteristics of the content and by the message of the content. The recognition through retweets improves when the organization uses links, hashtags, and topics related to gender equality, whereas the recognition through favorites increases when the organization uses original tweets, publications between 8:00 and 10:00 a.m. and, again, gender equality related topics. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.

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Mapping Social Media Texts as the Basis of Place-Making Process

Cited by 7 publications

References 1 publication

Exploring social responsibility, social marketing and happiness using artificial intelligence, automated text analysis and correspondence analysis

Exploring social responsibility, social marketing and happiness using artificial intelligence, automated text analysis and correspondence analysis

How to Improve Customer Engagement in Social Networks: A Study of Spanish Brands in the Automotive Industry

The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

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