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Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

The purpose of this study is to investigate how libraries use Twitter as a social media platform and examine the tweets they post, including multimedia content such as images and video clips. The study also aims to analyse the relationship between post types and user engagement and evaluate the effects of post features, such as multimedia content, on user engagement.

The methodology of the study involved three phases. In Phase 1, a review of related literature was conducted to develop a holistic approach for the study. In Phase 2, official Twitter handles of selected libraries were identified and verified for authenticity using various methods, including cross-checking with library websites. During Phase 3, data was collected from the Twitter handles. The data was then tabulated and interpreted to achieve the set objectives of the study.

The paper examined the tweets posted by select libraries on Twitter and their impact on user engagement. The study found that most tweets were related to library resources/collection and announcements, followed by events hosted by libraries. Emotionally inspiring posts and daily facts were also commonly posted. The findings also showed that including images in tweets resulted in higher levels of user engagement than video clips did. The study suggests that incorporating images fosters engagement and boosts retweets, while watching a video takes more effort and time.

The practical implications of the study can provide insights into the tweets that generate user engagement, which can help libraries tailor their social media strategies to attract and retain more followers. The paper can help libraries measure the success of their social media activities by evaluating user engagement metrics.

The originality/ value of the study lies in its examination of how libraries use Twitter as a social media platform, including the tweets they post and the impact of multimedia content on user engagement. While previous studies have examined the use of social media by libraries, this study focuses specifically on Twitter and provides a detailed analysis of the tweets that generate user engagement.

In recent decades, higher education institutions (HEIs) have increasingly adopted marketing-oriented approaches. While the adoption of marketing was slower in Europe and Spain, it has become a vital tool for HEIs, both to stay competitive in a changing socio-economic context and to face the challenges posed by the transition to the University 2.0 model. This study aims to analyse the historical evolution of communication techniques used by universities, bringing into focus the relevance of social networks in the most recent decades.

This research methodology consists of two components. Firstly, a comprehensive analysis of the available data is conducted to investigate the earliest marketing and communication actions involving universities, as well as their evolution over time, contextualizing this within the significant shifts in the social, political and technological background. Secondly, a specific focus is placed on the contribution of social media, particularly Twitter, as a powerful tool in creating a university brand and effectively promoting educational institutions, especially during the last stage of this historical evolution. To identify and analyse these trends, Natural Language Processing is used, specifically by leveraging topic modelling techniques.

The results of this analysis offer insights into the evolution of marketing communication applied by Spanish universities and show the increasing importance of social networks and the use of specific topics and contents to enhance their impact on engagement.

This study contributes to the literature by using a novel methodological approach to the research on the historical development of communication in universities in Spain, providing guidance to manage their social media strategy to differentiate themselves, increase engagement and foster brand loyalty.

This study explored how the use of different social media is related to subjective well-being among university students during the COVID-19 pandemic in Japan.

We surveyed 1,681 university students in the Kanto region of Japan in May 2021 to investigate how social media use relates to subjective well-being. We also examined the effects of self-consciousness and friendship, self-presentation desire, generalized trust, online communication skills, depression tendency and social support from others.

The responses revealed 15 possible patterns of social media usage on four widely used social media in Japan (LINE, Twitter, Instagram and Facebook). We selected users with the top five patterns for further statistical analyses: LINE/Twitter/Instagram/Facebook, LINE/Twitter/Instagram, LINE/Twitter, LINE/Instagram and LINE only. Overall, self-establishment as a factor of self-consciousness and friendship, and social support from others had positive effects on the improvement of subjective well-being, whereas depression tendency had negative effects on their subjective well-being regardless of their usage patterns, of which the results of social support from others and depression tendency were consistent with the results of previous studies. Regarding other factors, they had different effects on subjective well-being due to different patterns. Effects on subjective well-being from self-indeterminate and self-independency as factors of self-consciousness and friendship, praise acquisition, self-appeal and topic avoidance as factors of self-presentation desire were observed.

This is among the earliest studies on the relationship between young generations’ social media use and subjective well-being through social media usage patterns during the COVID-19 pandemic in Japan.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

This chapter seeks to understand the intersection between eGovernment, social media, and digital inequalities by examining the disparate flow of information during the COVID-19 pandemic. Developed economies are increasingly transitioning to digital interfaces for information dissemination and provision of services. The authors explore the potential of, and challenges facing eGovernment by looking at the use of social media during the COVID-19 pandemic. This chapter employs a case study approach to probe the dynamics of government-initiated efforts at information dissemination through the Center for Disease Control and Prevention’s (CDC) website and social media account on Twitter. The analysis in this chapter uses NodeXL to examine communication roles played by government and non-governmental actors within this slice of the Twittersphere centered around CDC@gov. As the findings demonstrate, non-governmental actors played key roles in the dissemination of public health messaging. The authors analyze these data with an eye to the potential of social media for public health communication and extrapolate that understanding to the use of digital access and social media for the provision of accurate, official information in other circumstances. While the COVID-19 pandemic was a global health crisis, individuals and households face individual or local crises every day. This angle of vision allows the chapter to conclude with recommendations pertaining to government-led information dissemination for the public good during crisis and non-crisis situations alike. In the concluding section, the authors probe the degree to which eGovernment can also address digital inequalities including connectivity, device, and literacy gaps. The authors offer solutions needed for eGovernment initiatives in light of challenges posed by digital inequalities to ensure that digital information sharing and services are accessible to all.

Given the historical legacy of policing Black bodies, this research focuses on the structures of anti-Blackness within school policing and the strategies students of Color activists use as they work to defund or abolish police departments in the Los Angeles Unified School District (LAUSD).

Specifically, this article looks to Twitter as a counter-storytelling space for students of Color activists to organize and build movements to end anti-Black school policing. Through the frameworks of critical race theory (CRT) and Black critical theory (BlackCrit), this research applies inductive coding to analyze 42 Twitter posts from three students of Color-led organizations based in Los Angeles.

This document analysis presents four themes, which describe four dominant strategies students of Color activists use in their campaigns to defund or abolish school police in the LAUSD: (1) centering Blackness and Black student experiences, (2) making demands for the elimination of funding and support for school police, (3) calling for a shift in funding to support Black students and (4) employing multiple tactics concurrently.

These findings demonstrate the importance of developing and centering a critical understanding of anti-Blackness to achieve racial and educational justice within social movements.

Moreover, the demands of students of Color activists reflect visions of public schools free from anti-Black school policing.

This 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.

An 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.

The 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.

Our 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.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.

Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.

Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.

This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.