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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.

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.

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.

The first purpose of the present study is to investigate the coverage of journal articles in Physics in various sources of altmetrics. Secondly, the study investigates the relationship between altmetrics and citations. Finally, the study also investigates whether the relationship between citations and altmetrics was stronger or weaker for those articles that had been mentioned at least once in the sources of altmetrics.

The journal articles in Physics having at least one author from an Indian Institution and published during 2014–2018 in sources of altmetrics have been investigated. Altmetric.com was used for collecting altmetrics data. Spearman’s rank correlation coefficient (ρ) has been used as the data found to be skewed.

The highest coverage was found on Twitter (22.68%), followed by Facebook (3.62%) and blogs (2.18%). The coverage in the rest of the sources was less than 1%. The average Twitter mentions for journal articles tweeted at least once was found to be 4 (3.99) and for Facebook mentions, it was found to be 1.48. Correlations between Twitter mentions–citations and Facebook mentions–citation were found to be statistically significant but low to weak positive.

The study concludes that due to the low coverage of journal articles, altmetrics should be used cautiously for research evaluation keeping in mind the disciplinary differences. The study also suggests that altmetrics can function as complementary to citation-based metrics.

The study is one of the first large scale altmetrics studies dealing with research in Physics. Also, Indian research has not been attended to in the altmetrics literature and the present study shall fill that void.

Owing to the worldwide outbreak of the SARS-CoV-2, social media conversations have increased. Given the increasing pressure from regulatory authorities and society, green accounting – as a dimension of sustainable development – remains the most discussed topic on most social media platforms. This study aims to incorporate a technological approach to green accounting and sustainability to enhance the innovation process inside and outside organizations.

This study uses the hermeneutic phenomenological technique to investigate Twitter content. Tweets were subjected to a manual coding process to analyze their content, including recent advancements, challenges, cross-country initiatives and promotion strategies in green accounting. Public perception of green accounting and the COP26 climate summit was also studied.

Tweeters view green accounting favorably; however, they are apprehensive about its implementation. Regarding the challenges in green accounting, “corporate green washing” was the most tweeted content. The UK was the top-rated nation with respect to green accounting development. Furthermore, the most discussed breakthrough was the application of artificial intelligence in the domain of green accounting functions. However, Twitter users were observed to have directed heavy criticism at the COP26 climate summit in Glasgow.

This study’s primary innovation is its integration of emerging technologies such as machine learning and data mining with social media platforms such as Twitter. Incorporating manual coding of tweets is a rigorous procedure that amplifies the strength of machine learning software’s auto-coding feature.

This paper studies, based on the theory of service-dominant logic, the effect of value co-creation practices (linking and materializing) on engagement dimensions (popularity, commitment and virality). The main objective is to analyze the influence of value co-creation practices on engagement at international trade shows organizer association on Twitter.

This paper studies the usage of Twitter by the Specialty Food Association, which organizes one of the top five foods and beverage international trade show in the United States. To achieve the research objective, the authors have analyzed 1,608 posts on Twitter from the Twitter account @Specialty_Food. A content analysis was performed using Krippendorff's (2004) recommendations, and the data were analyzed using regression analysis with optimal scaling and Kruskal–Wallis Test.

According to the results, some materializing practices influence popularity, commitment, virality and global engagement on Twitter. While the usage of some linking practices influences respectively commitment and popularity.

These results provide valuable information for business-to-business (B2B) contexts and answer a research gap reported in previous literature, which affirms that more research is needed about the relationship between service systems and engagement. From a general view, to generate more engagement on social media in B2B contexts, it is recommended to prioritize posts that incorporate live and online events based on collaborative and dynamic human interactions, following by business ideas and business cases.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.