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The purpose of this paper is to explore the effect of Elon Musk’s Twitter bio change on January 29, 2021 on the discourse around Bitcoin (BTC) on Twitter and to understand how these changes relate to the changes in Bitcoin price around that time.

This study implements sentiment analysis and text mining on Twitter data to explore changes in public sentiments toward Bitcoin after Elon Musk’s Twitter bio change. Furthermore, it uses Bitcoin price data obtained from the Binance exchange to understand its relation with Twitter discussion.

Elon Musk’s bio change on Twitter on January 29 increased the tweet volume mentioning Bitcoin. This increase in tweets had a strong positive correlation with Bitcoin price and preceded the rise in Bitcoin price. Although the bio change had an apparent effect on the tweet volume, there was no considerable effect on the tweet sentiments, indicating that tweet sentiment is a poor predictor of Bitcoin price.

This paper proposes an understanding of how social media influencers, like Elon Musk, affect the discourse around Bitcoin and can, in turn, have an impact on Bitcoin price.

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.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

This study aims to examine the impact of signaling through social media (SM) on funding achieved by start-ups.

This study follows a causal research design and is based on unique data set compiled from Crunchbase-Pro and Twitter. The sample size is 1,672 Indian start-ups. Heckman’s model and ordinary least squares regression is used to test the hypothesis.

Devising a thoughtful SM strategy, should be an integral part of the overall strategy of the start-ups looking out for funds. LinkedIn presence is in itself a positive signal. Active usage of Twitter and feedback from other Twitter users has a positive impact on funds raised by the start-up. Posting retweets and repetitive usage of URLs and media is not a predictor of funds raised by the start-up.

An early-stage strategy on SM adoption, especially Twitter can play an important role in attracting interest and attention of stakeholders. To capitalize SM, entrepreneurs should maintain an active SM account of the start-up.

India has emerged as one of the start-up hubs of the world. However, there is a dearth of literature on SM usage by start-ups in India. To the best of the authors’ knowledge, this study is first of its kind and establishes the results empirically based on more than 100k tweets for a large pool of Indian start-ups.

Anthropomorphized brands have been widely used as marketing communication tools to engage consumers on social media, especially on Twitter. Guided by the social exchange theory (SET) and the dialogic theory, this study aims to investigate how anthropomorphized brands leverage different communication strategies on Twitter and how these strategies are related to consumer engagement.

Supervised machine learning was used to identify the communication strategies (i.e. message types and dialogic principles) of 125,887 tweets from 21 brand characters. Some statistical analyses (e.g. frequency analysis, Chi-square analysis and Poisson regression analysis) were performed to explore the relationships between communication strategies and consumer engagement (i.e. retweets and replies).

The majority of anthropomorphized brands’ tweets belonged to the socioemotional category and the most adopted dialogic principles were generation of return visits and conservation of visitors. Consumers engaged more with socioemotional tweets as well as the tweets that adopted the principles of dialogic loop and conservation of visitors. There were clear relationships between message types and dialogic principles in anthropomorphized brands’ tweets, and certain dialogic principles were found to effectively improve consumer engagement with certain message types.

To the best of the authors’ knowledge, this study is the first to investigate the communication strategies of anthropomorphized brand characters on Twitter using computational research methods. It not only provides brand managers a systematic review of how current anthropomorphized brands communicate with consumers on Twitter and what strategies work more effectively to trigger consumer engagement but also contributes to theory building in brand management by integrating the SET and the dialogic theory in brand anthropomorphism research.

Supply chains are under increased scrutiny as consumers have become aware of the dark side of downstream production practices. Many articles and exposés have led consumers to be more conscious of purchasing products from companies who source materials in a socially responsible and ethical manner. As a result, business-to-business (B2B) and business-to-consumers (B2C) companies are under increased pressure to source raw materials in a transparent and ethical way. Because of the associated costs, companies then look to benefit from increased brand equity by promoting to consumers how ethical their products are. The purpose of this study is to look at the case of the food industry to analyze sustainability messaging on Twitter in both B2B and B2C companies to determine which of the dimensions of sustainability (people, profit, planet) are being emphasized.

In this study, two published dictionaries were combined to capture the three dimensions of “sustainability,” and these scales were then used to analyze Twitter posts. This study created a unique software package to classify, mine, collect and analyze Twitter data. This study used these tools to analyze 246,386 Twitter posts within a sample of 39 leading B2C and B2B food companies over a 10-year period (2012–2021) to explore brand messaging and engagement rate.

This research revealed several interesting results. These include how B2B companies have emphasized the employee (people) dimensions of sustainability, while B2C companies have had a more balanced approach that overall has prioritized the economic dimension (profit) of sustainability. The data on audience engagement revealed a mismatch between the types of sustainability messaging B2B companies and B2C companies in the food industry are posting and what engages audiences.

This study fills several gaps, including analyzing how B2B and B2C companies use sustainability language in their social media brand management, as well as looking at which dimensions of sustainability they emphasize and which ones engage audiences the most. This research is also novel in combining multiple existing scales under one project to analyze the triple bottom line in the analysis of qualitative texts.

The study aims to access, monitor and visualize the scientific progress of Twitter-based research through a bibliometric analysis of scientific publications.

The data was retrieved from 2006 to February 23, 2022 using the Web of Science, a leading indexing and abstracting database. In response to the authors’ query, 6,193 items with 101,037 citations, an average citation of 16.31 and an h index of 126 were received. The “Biblioshiny” extension of the “Bibliometrics” package (www.bibliometrix.org) of R software was used to evaluate and visualize the data.

The present study highlighted the scientific progress of the field evolved over a period of time. The obtained results uncovered the publication trends, productive countries and their collaboration pattern, active authors who nurture the field by making their contribution, prolific source titles adopted by authors to publish the literature on the topic, most productive language in which literature was written, productive institutions, funding agencies that sponsor the research, influential articles, prominent keywords used in publications were also identified which will aid scientists in identifying research gaps in a particular area.

This study comprehensively illustrates the research status of Twitter-related research by conducting a bibliometric analysis. The study’s findings can assist relevant researchers in understanding the research trend, seeking scientific collaborators and funding for their research. Further, the study will act as a ready reference tool for the scientific community to identify research gaps, select research topics and appropriate platforms for submitting their scholarly endeavors.

Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.

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.