Dealing With Statistical Significance in Big Data: The Social Media Value of Game Outcomes in Professional Football

2021 ◽  
pp. 1-12
Author(s):  
Daniel Weimar ◽  
Brian P. Soebbing ◽  
Pamela Wicker
Keyword(s):  
Social Media ◽  
Big Data ◽  
Fixed Effects ◽  
Statistical Significance ◽  
Dominance Analysis ◽  
Football Clubs ◽  
The Social ◽  
Sport Teams ◽  
Fan Engagement ◽  
Media Data

The identification of relevant effects is challenging in Big Data because larger samples are more likely to yield statistically significant effects. Professional sport teams attempting to identify the core drivers behind their follower numbers on social media also face this challenge. The purposes of this study are to examine the effects of game outcomes on the change rate of followers using big social media data and to assess the relative impact of determinants using dominance analysis. The authors collected data of 644 first division football clubs from Facebook (n = 297,042), Twitter (n = 292,186), and Instagram (n = 312,710) over a 19-month period. Our fixed-effects regressions returned significant findings for game outcomes. Therefore, the authors extracted the relative importance of wins, draws, and losses through dominance analysis, indicating that a victory yielded the highest increase in followers. For practitioners, the findings present opportunities to develop fan engagement, increase the number of followers, and enter new markets.

scholarly journals Human Behavior Analysis Using Intelligent Big Data Analytics

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak ◽  
Mohammad Dahman Alshehri ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Human Behavior ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Apache Spark ◽  
Social Media Data ◽  
The Social ◽  
Media Data

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted.

scholarly journals A three-layer model on users’ interests mining

2017 ◽  
Vol 44 (1) ◽  
pp. 136-144 ◽  
Author(s):  
Renfeng Yang ◽  
Wenbo Xie ◽  
Duanbing Chen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Layer Model ◽  
Short Term ◽  
Social Media Data ◽  
Sina Weibo ◽  
Hit Rate ◽  
The Social ◽  
Media Data

With the advent of big data era, social media plays an important role in many areas such as security and finance. Researchers pay more attention on mining users’ interests through the social media data. A three-layer model (TLM) based on keyword extracting is proposed to mine users’ interests, which includes candidate words extracting, semantic structures analysing and interest words ranking. The TLM mainly focuses on both self-importance and semantic-importance of interest words. In addition, the TLM also considers the interest drifting to track long-term and short-term interests of users. Experiments conducted on 10 SINA Weibo datasets show that TLM is more efficient than existing methods to identify users’ interests based on hit rate.

Professional Team Sport and Twitter: Gratifications Sought and Obtained by Followers

2014 ◽  
Vol 7 (2) ◽  
pp. 188-213 ◽  
Author(s):  
Chris Gibbs ◽  
Norm O’Reilly ◽  
Michelle Brunette
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Professional Sport ◽  
Sport Communication ◽  
The Social ◽  
Sport Teams ◽  
Twitter Users ◽  
Media Experience ◽  
Fan Engagement ◽  
Depth Interviews

Without exception, all professional sport teams in North America use social media to communicate with fans. Sport communication professionals use Twitter as one of the strategic tools of engagement, yet there remains a lack of understanding about how users are motivated and gratified in their Twitter use. Drawing on a specific sample from the Twitter followers of the Canadian Football League, the researchers used semistructured in-depth interviews, content analysis, and an online survey to seek an understanding of what motivates and satisfies Twitter followers of professional sport teams, measured through the gratifications sought and the fulfillment of these motives through the perceived gratifications obtained. The results add to the sport communications literature by finding 4 primary gratifications sought by Twitter users: interaction, promotion, live game updates, and news. Professional sport teams can improve strategic fan engagement by better understanding how Twitter followers use and seek gratification in the social-media experience.

scholarly journals Network based model of social media big data predicts contagious disease diffusion

2017 ◽  
Vol 45 (3) ◽  
pp. 110-120 ◽  
Author(s):  
Lauren S. Elkin ◽  
Kamil Topal ◽  
Gurkan Bebek
Keyword(s):  
Social Media ◽  
Big Data ◽  
Disease Outbreaks ◽  
Social Media Data ◽  
Content Type ◽  
Contagious Diseases ◽  
The Social ◽  
The Usa ◽  
Disease Diffusion ◽  
Media Data

Purpose Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks. Design/methodology/approach In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data. Findings The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets. Originality/value First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

The Effectiveness of Big Data in Social Networks

2015 ◽  
pp. 362-381
Author(s):  
Khine Khine Nyunt ◽  
Noor Zaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
User Generated Content ◽  
Web Based ◽  
The Social ◽  
Internet Application ◽  
Media Data ◽  
Media Mining

In this chapter, we will discuss how “big data” is effective in “Social Networks” which will bring huge opportunities but difficulties though challenges yet ahead to the communities. Firstly, Social Media is a strategy for broadcasting, while Social Networking is a tool and a utility for connecting with others. For this perspective, we will introduce the characteristic and fundamental models of social networks and discuss the existing security & privacy for the user awareness of social networks in part I. Secondly, the technological built web based internet application of social media with Web2.0 application have transformed users to allow creation and exchange of user-generated content which play a role in big data of unstructured contents as well as structured contents. Subsequently, we will introduce the characteristic and landscaping of the big data in part II. Finally, we will discuss the algorithms for marketing and social media mining which play a role how big data fit into the social media data.

The Effectiveness of Big Data in Social Networks

2018 ◽  
pp. 863-882
Author(s):  
Khine Khine Nyunt ◽  
Noor Zaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
User Generated Content ◽  
Web Based ◽  
The Social ◽  
Internet Application ◽  
Media Data ◽  
Media Mining

In this chapter, we will discuss how “big data” is effective in “Social Networks” which will bring huge opportunities but difficulties though challenges yet ahead to the communities. Firstly, Social Media is a strategy for broadcasting, while Social Networking is a tool and a utility for connecting with others. For this perspective, we will introduce the characteristic and fundamental models of social networks and discuss the existing security & privacy for the user awareness of social networks in part I. Secondly, the technological built web based internet application of social media with Web2.0 application have transformed users to allow creation and exchange of user-generated content which play a role in big data of unstructured contents as well as structured contents. Subsequently, we will introduce the characteristic and landscaping of the big data in part II. Finally, we will discuss the algorithms for marketing and social media mining which play a role how big data fit into the social media data.

Brand-Image Communication Through Social Media: The Case of European Professional Football Clubs

2018 ◽  
Vol 11 (3) ◽  
pp. 319-338 ◽  
Author(s):  
Daniel Maderer ◽  
Petros Parganas ◽  
Christos Anagnostopoulos
Keyword(s):  
Social Media ◽  
Brand Management ◽  
Brand Image ◽  
Professional Football ◽  
Fan Identification ◽  
Football Clubs ◽  
Sport Teams ◽  
Social Media Platforms ◽  
Brand Attributes ◽  
Fan Engagement

Social-media platforms have become an important tool for sport marketers to communicate their brand image and engage with fans. This study analyzed 1,115 Facebook posts and 16,308 tweets from 10 of the most valuable European professional football clubs to identify the range of brand associations communicated and the level of online fan engagement. Statistical analysis captured correlations between and among selected brand attributes, time periods of posts (in and off-season), and levels of fan engagement. On both Facebook and Twitter, football clubs posted more frequently during the season, while content associated with product-related attributes was the focus of such communication. Product-related content was found to generate higher levels of online fan engagement. The study extends the literature on sport teams’ brand management through social media and offers practical recommendations on how to enhance fan identification and engagement and ultimately make financial and reputational gains.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

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