Text Analytics for Sports Fan Engagement in Social Media

Digital communications have greatly increased the engagements between customers and businesses of all varieties, to include university athletic programs and their fans. Interaction and engagement through social media content plays a critical role in developing the relationship between fans and their favorite teams [1] and boosting brand popularity [2]. In this study we reviewed the existing literature pertaining to the use of sentiment analysis and content categorization for university sports fan engagements. Dozens of sources were examined and their methodologies explored. This study seeks to demonstrate that the use of text mining and sentiment analysis can provide significant time savings to the athletics departments for the betterment of their data understanding. In turn this process will yield improved fan engagement of a growing fan base without increased personnel hours being expended. Using the textual data gathered from Basketball Season Ticket Holder Survey Results at a major Midwestern university in the United States, multiple analytical models were created, using several different text mining packages, each one seeking to classify the polarity of the comments being examined. The study explored the possibility of classifying comments as positive or negative at the sentence level or by combining several statements originative from a single comment. Statements were further categorized according to the subject matter of the comment. Inconsistencies were found between what these models determined and what a basic understanding of English suggests is actually true. Through tweaking of models and usage of more effective text mining algorithms performance improved. Ultimately, it was determined that text mining and sentiment analysis models would be capable of performing the necessary analysis. Implications for research and practice are discussed

Everyday the Same Picture: Popularity and Content Diversity

Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than $40K$ followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about $2M$ likes and $190K$ comments, made by approximately $340K$ and $65K$ users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents

Uncovering the popularity mechanisms for Facebook applications

Understanding the popularity dynamics of online application(App) is significant for the online social systems. In this paper, by dividing the Facebook Apps into different groups in terms of their popularities, we empirically investigate the popularity dynamics for different kinds of Facebook Apps. Then, taking into account the influence of cumulative and recent popularities on the user choice, we present a model to regenerate the growth of popularity for different App groups. The experimental results of 917 Facebook Apps show that as the popularities of Facebook Apps increase, the recent popularity plays more important role. Specifically, the recent popularity plays more important role in regenerating the popularity dynamics for more popular Apps, and the cumulative popularity plays more important role for unpopular Apps. We also conduct temporal analysis on the growth characteristic of individual App by comparing the increment at each time with the average of historical records. The results show that the growth of more popular App tends to fluctuate more greatly. Our work may shed some lights for deeply understanding the popularity mechanism for online applications

Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Marketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.Abdulaziz Elwalda and Mohammed Alsagga

The effect of social networks on the development of gastronomy – the way forward to the development of gastronomy tourism in Serbia

Purpose: To evaluate some of the current discussions about the possible impacts of social networks on the development of gastronomy in the Republic of Serbia. There could be either positive and/or negative impacts and this viewpoint provides some reflection on what the future might hold for some if not many tourism destinations in Serbia and the region when the tourism industry restarts after the pandemic of Covid-19 virus. Design/methodology/approach: The research was conducted in December 2021, on a total of 244 respondents in three cities in Serbia. SPSS software was used, version 26.00, and the obtained data were analyzed by descriptive statistics. Then, to determine the structure of the questionnaire and the percentage of variance, an exploratory factor analysis was performed together with a higher order factor analysis, in order to obtain the desired number of factors. Subsequently, the authors used multiple regression analysis to confirm the significance of the predictors. The goal of the research was to determine whether, and to what extent, social networks can predict the choice of restaurants and gastronomic offers in Serbia. Serbian gastronomy has a great influence on the development of tourism, so this research has a wide scientific and practical contribution. Findings: This paper provides a context and viewpoint on the possible implications of impacts of social networks on the development of gastronomy in the Republic of Serbia in the future. It has been proven that social networks can have an impact on the development of gastronomy and tourism itself. Research limitations/implications: To examine the impact of social networks on the development of gastronomy, the authors conducted a survey online due to the current Covid-19 pandemic. The limitation of this research was precisely that the authors did not have the opportunity to conduct the research live due to the Covid-19 pandemic. It is recommended that such surveys be conducted live in direct contact with respondents in the future in order to obtain a larger sample with fully completed questionnaires. Practical implications: The importance of social networks is increasingly a topic of study of world research, especially when it comes to gastronomy, which is becoming increasingly important as an activity in the tourism industry. The results indicate that the greatest importance in predicting the choice of restaurants and gastronomic offers has social networks and marketing. The importance of the work is reflected in the recognition of the importance of social networks, in order to better place Serbian gastronomy. Social implications: This paper offers a synthesis of views that fosters an understanding of the possibility of impacts of social networks on the development of gastronomy in the Republic of Serbia before and after the Covid-19 pandemic. Originality/value: The viewpoint proffered in this paper provides scope for a rapid evaluation of the current status of gastronomy tourism in Serbia which can help practitioners and researchers in the faster and better development of gastronomy and tourism

Identifying the antecedents of posts’ popularity on Facebook fan pages

Informed by the related theories of agenda-setting and framing, the purpose of this paper is to identify the antecedents of posts’ popularity on Facebook Fan Pages. Posts’ popularity is conceptualized as the volumes of Likes, Comments and Shares attracted by the entries. Building on prior studies, the paper proposes a conceptual framework that identifies four categories of antecedents—presentation, brand awareness, engagement and temporal—that could be related to posts’ popularity on Facebook Fan Pages. The framework was validated by drawing 10,000 posts from 50 Facebook Fan Pages. The posts were measured in terms of the dimensions of the four categories. Hierarchical regression was used for analysis with volumes of Likes, Comments and Shares as the three separate dependent variables. Several dimensions from all the categories of antecedents were found to have a significant bearing on Likes, Comments and Shares. In particular, the presentation category emerged as being the most important in promoting posts’ popularity. The findings have implications for social media brand managers

MANAGING BRANDS’ POPULARITY ON FACEBOOK: POST TIME, CONTENT, AND BRAND COMMUNICATION STRATEGIES

Introduction: A brand’s popularity on social media affects its customers’ purchasing intention and purchasing decision. Background Problem: A review of the literature shows that a brand’s popularity on social media has a secure connection with its content and the time information about it is posted; allegedly the brand’s interactions are also influential. Indicators of its popularity include the number of likes, shares, comments, and views for it. Novelty: Previous brand popularity studies were limited to the features of likes, comments, and shares as a function of the content and time, and OLS was commonly used. However, this study adds the views feature and the function of the administrator’s comments to complete the gap. GLS is used as the method of analysis. Research Method: Data are collected through the observation of six top international food and beverage products’ categories on the Facebook fan page. The data were analyzed using the Seemingly Unrelated Regression (SUR), and the Mann-Whitney and Kruskal-Wallis methods. Findings: The study’s findings shows that video and the day to post have a significant influence and increase the number of likes, shares, comments, and views. A caption only shows significance to increase the number of likes and shares. The hour has a significant effect on comments and shares. The time of posting indicates that posting on weekdays and during busy periods is more effective for increasing the popularity of brands. The administrator’s comments significantly influence the increase in the number of comments and views, while two-way communication is more significant for increasing a brand’s popularity. Conclusion: These findings provide a deeper insight to help managers to improve their brand’s popularity on social media by exploring how brands manage their fan pages

Mining and modelling temporal dynamics of followers' engagement on online social networks

A relevant fraction of human interactions occurs on online social networks. In this context, the freshness of content plays an important role, with content popularity rapidly vanishing over time. We therefore investigate how influencers' generated content (i.e., posts) attracts interactions, measured by the number of likes or reactions. We analyse the activity of influencers and followers over more than 5 years, focusing on two popular social networks: Facebook and Instagram, including more than 13 billion interactions and about 4 million posts. We investigate the influencers' and followers' behaviour over time, characterising the arrival process of interactions during the lifetime of posts, which are typically short-lived. After finding the factors playing a crucial role in the post popularity dynamics, we propose an analytical model for the user interactions. We tune the parameters of the model based on the past behaviour observed for each given influencer, discovering that fitted parameters are pretty similar across different influencers and social networks. We validate our model using experimental data and effectively apply the model to perform early prediction of post popularity, showing considerable improvements over a simpler baseline

Consumers’ Sentiments and Popularity of Brand Posts in Social Media: The Moderating Role of Up-votes

User-generated contents (UGC) on online social media plays an important role in the branding and marketing of firms’ products and services. In this study, we examine the effect of consumers’ sentiments embedded in UGC on the popularity of brand posts. We retrieved real-world data from a social media platform and utilized a rigorous data analysis method that exploited state-of-the-art semi-supervised sentiment analysis technique. Our empirical findings confirm that positive and negative sentiments are associated with post popularity to some extent. Also, the customers’ up-votes for negative comments somehow moderates the effect of negative comments on post popularity. To the best of our knowledge, this is the first study that demonstrates the specific role of up-votes in enhancing the popularity of brand posts on online social media. Our findings provide a promising theoretical contribution to the literature. The managerial implication is that firms can apply our findings to develop more effective strategies for marketing through social media brand communities