The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Improvised Marketing Interventions in Social Media

Online virality has attracted the attention of academics and marketers who want to identify the characteristics of online content that promote sharing. This article adds to this body of research by examining the phenomenon of improvised marketing interventions (IMIs)—social media actions that are composed and executed in real time proximal to an external event. Using the concept of quick wit, and theorizing that the effect of IMIs is furthered by humor and timeliness or unanticipation, the authors find evidence of these effects on both virality and firm value across five multimethod studies, including quasiexperiments, experiments, and archival data analysis. These findings point to the potential of IMIs in social media and to the features that firms should proactively focus on managing in order to reap the observed online sharing and firm value benefits

The Impact of Terrorism on Consumer Sentiment: Evidence from Twitter Data

This paper investigates the effects that 12 different terrorist attacks had on consumer sentiment, using data scraped from Twitter to determine a broad based emotional stance. This was inspired by my previous research that worked to determine the impact that terrorism had on the stock market. The goal of this research is to determine a more qualitative impact of terrorist attacks. I utilized Rapid Miner for data processing, and the Global Terrorism Database as my data source. The project began by examining the rise and fall of keywords and hashtags through sentiment analysis to measure reaction over time. The text was then clustered into keyword families to determine the key conversation topics. Finally, the text was analyzed to determine the evolution of polarity (the transition from negative to positive sentiment over time). The results from this analysis concluded that most of the messages were overwhelmingly negative, but shifted more towards a neutral stance as time passed after the attack. The number of tweets per day actually increased after the attack, with most tweets occurring in the early morning hours, and declining around 5 pm. An analysis of the clusters reveals a relationship between different keywords, with political keywords (i.e. Trump, Violence, Terror) often forming the strongest cluster. Consumer sentiment seemed to neutralize over time, suggesting the possibility of desensitization or a numbing effect

What drives cryptocurrency value? A volatility and predictability analysis

Mestrado em Decisão Económica e EmpresarialEsta tese descreve como as moedas digitais se tornaram no novo fenómeno nos mercados financeiros e como a mais popular das moedas digitais - Bitcoin - originou perguntas cruciais sobre o seu valor e como ao mesmo tempo as suas séries financeiras criaram uma oportunidade para estudar várias dinâmicas sobre o preço, que tipicamente estão fortemente ligadas a movimentos especulativos e sem análise fundamental. Com a utilização de um modelo GARCH(1,1) sobre dados diários e centrando-se em dois fenómenos recentes - moedas digitais, nomeadamente Bitcoin e conteúdo web oriundo do Google Trends, Wikipedia e Twitter - verificámos que os retornos da Bitcoin são fortemente impulsionados pela sua popularidade. Assim, analisando este relacionamento e modelando a existência de variâncias condicionais heterocedásticas demonstramos que o conteúdo proveniente de motores de busca e redes sociais e a flutuação nos preços Bitcoin estão intensamente ligados e que esta relação exibe alguma previsibilidade.This thesis describes how digital currencies have rose as a new interesting phenomenon in the financial markets and how the most popular of the digital currencies - BitCoin - have risen crucial questions about their exchange rates and also represents a field to study the dynamics of this market, which is strongly connected with speculative traders with no fundamentals as there is no fundamental value to the currency. Using a GARCH(1,1) model on daily data and focusing on two emerging phenomena of recent years - digital currencies, particularly Bitcoin, and web content provided by search queries on Google Trends and Wikipedia and tweets from Twitter - we discover that Bitcoin returns are driven primarily by its popularity. Thus, we analyze their relationship, the existence of volatility clustering and demonstrate that the web content and Bitcoin prices are connected and they exhibit some predictable power

Social Media Perceptions of 51% Attacks on Proof-of-Work Cryptocurrencies: A Natural Language Processing Approach

This work is the first study on the effects of 51% attacks on proof-of-work (PoW) cryptocurrencies as expressed in the sentiments and emotions of social media users. Our goals are to design the methodologies for the study including data collection, conduct volumetric and temporal analyses of the data, and profile the sentiments and emotions that emerge from the data. As a first step, we have identified 31 events of 51% attacks on various PoW cryptocurrencies. We have designed the methodologies and gathered Twitter data on the events as well as benchmark data during normal times for comparison. We have defined parameters for profiling the datasets based on their sentiments and emotions. We have studied the variation of these sentiment and emotion profiles when a cryptocurrency is under attack and the benchmark otherwise, between multiple attack events of the same cryptocurrency, and between different cryptocurrencies. Our results confirm some expected overall behaviour and reactions while providing nuanced insights that may not be obvious or may even be considered surprising. Our code and datasets are publicly accessible

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi

What Information Propagates among the Public when an Initial Coin Offering (ICO) is Initiated? A theory-driven approach

Since the popularity of blockchain-based cryptocurrency investments has increased among the public, people have directly purchased cryptocurrency through the cryptocurrency market or joined initial coin offering (ICO) projects. This research explores what informational cues are captured before, during, and after ICO projects that can be considered as signals and a fulfillment of information asymmetry. We adopted two theoretical underpinnings to achieve our research goal - agency and signaling theory. Using information from Twitter, we selected the best-performing ICO project based on the highest return on investment (ROI). Then, we extracted 5,085 tweets related to the selected ICO project. Tweets are categorized by pre-ICO, during and post-ICO, by topic, and dispersion. Analyzing the tweets, we found multiple categories of informational cues for each ICO project. Implications and limitations are discussed

An analysis of the user occupational class through Twitter content

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications