scholarly journals A big data analysis of Twitter data during premier league matches: do tweets contain information valuable for in-play forecasting of goals in football?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Fabian Wunderlich ◽  
Daniel Memmert
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Expectation ◽  
Forecasting Accuracy ◽  
Twitter Data ◽  
Premier League ◽  
Football Match ◽  
Before And After ◽  
European Football ◽  
Twitter Users

AbstractData-related analysis in football increasingly benefits from Big Data approaches and machine learning methods. One relevant application of data analysis in football is forecasting, which relies on understanding and accurately modelling the process of a match. The present paper tackles two neglected facets of forecasting in football: Forecasts on the total number of goals and in-play forecasting (forecasts based on within-match information). Sentiment analysis techniques were used to extract the information reflected in almost two million tweets from more than 400 Premier League matches. By means of wordclouds and timely analysis of several tweet-based features, the Twitter communication over the full course of matches and shortly before and after goals was visualized and systematically analysed. Moreover, several forecasting models including a random forest model have been used to obtain in-play forecasts. Results suggest that in-play forecasting of goals is highly challenging, and in-play information does not improve forecasting accuracy. An additional analysis of goals from more than 30,000 matches from the main European football leagues supports the notion that the predictive value of in-play information is highly limited compared to pre-game information. This is a relevant result for coaches, match analysts and broadcasters who should not overestimate the value of in-play information. The present study also sheds light on how the perception and behaviour of Twitter users change over the course of a football match. A main result is that the sentiment of Twitter users decreases when the match progresses, which might be caused by an unjustified high expectation of football fans before the match.

Twitter users exhibited coping behaviours during the COVID-19 lockdown: an analysis of tweets using mixed methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruchi Mittal ◽  
Wasim Ahmed ◽  
Amit Mittal ◽  
Ishan Aggarwal
Keyword(s):  
Data Analysis ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Twitter Data ◽  
Long Run ◽  
The World ◽  
Twitter Users ◽  
The Government ◽  
Using Data

Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

scholarly journals Football Manager: Mutual Shaping between Game, Sport, and Community

2020 ◽  
Author(s):  
Alexandre Hocquet
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Video Games ◽  
Computer Model ◽  
Real Life ◽  
Football Players ◽  
Sports Management ◽  
Football Match ◽  
The World ◽  
Set Up

Football Manager is one of the most popular sports management video games. For twenty years now, it has been a best seller in all the countries of the world where football is culturally important. Its purpose is to simulate a manager’s career with an emphasis on data analysis and number crunching, especially the football match scenario and the football players’ quantified characteristics. The claimed realism of the game is therefore based, among other things, on the reliability of a constantly-updated database of hundreds of thousands of real football players. The community of gamers, organized in forums and networks around the world are de facto involved in a co-construction of a database which no organization would be able to set up. Yet, Football Manager is also a piece of commercial software, and a performative computer model in the football world: its database is becoming a key issue in the real life football world and this issue provokes debates and tensions in the gaming community. As Big Data is becoming a techno scientific promise in the football world, Football Manager’s metrics and database are becoming increasingly entangled in an economically growing world of real life football and data.

scholarly journals Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data

2020 ◽  
Vol 5 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Ting Xie ◽  
Ruihua Liu ◽  
Zhengyuan Wei
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Unsupervised Learning ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Distance Functions ◽  
Important Method ◽  
Before And After ◽  
Low Efficiency

AbstractClustering as a fundamental unsupervised learning is considered an important method of data analysis, and K-means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by K-means. Different from the traditional methods, the algorithm guaranteed the consistency of the clustering accuracy before and after descending dimension, accelerated K-means when the clustering centeres and distance functions satisfy certain conditions, completely matched in the preprocessing step and clustering step, and improved the efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed algorithm.

scholarly journals The Rio Olympic Games: A Look into City Dynamics through the Lens of Twitter Data

2020 ◽  
Vol 12 (17) ◽  
pp. 7003 ◽  
Author(s):  
Ana Condeço-Melhorado ◽  
Inmaculada Mohino ◽  
Borja Moya-Gómez ◽  
Juan Carlos García-Palomares
Keyword(s):  
Olympic Games ◽  
New Approach ◽  
Urban Dynamics ◽  
Twitter Data ◽  
City Dynamics ◽  
Before And After ◽  
Huge Impact ◽  
Twitter Users ◽  
Location Based Social Networks ◽  
The City

The Olympic Games have a huge impact on the cities where they are held, both during the actual celebration of the event, and before and after it. This study presents a new approach based on spatial analysis, GIS, and data coming from Location-Based Social Networks to model the spatiotemporal dimension of impacts associated with the Rio 2016 Olympic Games. Geolocalized data from Twitter are used to analyze the activity pattern of users from two different viewpoints. The first monitors the activity of Twitter users during the event—The arrival of visitors, where they came from, and the use which residents and tourists made of different areas of the city. The second assesses the spatiotemporal use of the city by Twitter users before the event, compared to the use during and after the event. The results not only reveal which spaces were the most used while the Games were being held but also changes in the urban dynamics after the Games. Both approaches can be used to assess the impacts of mega-events and to improve the management and allocation of urban resources such as transport and public services infrastructure.

scholarly journals The Rio Olympic Games: A Look into City Dynamics through the Lens of Twitter Data

2020 ◽  
Author(s):  
Ana Condeço-Melhorado ◽  
Inmaculada Mohino ◽  
Borja Moya-Gómez ◽  
Juan Carlos García-Palomares
Keyword(s):  
Olympic Games ◽  
New Approach ◽  
Urban Dynamics ◽  
Twitter Data ◽  
City Dynamics ◽  
Before And After ◽  
Huge Impact ◽  
Twitter Users ◽  
Location Based Social Networks ◽  
The City

Olympic Games have a huge impact on the cities where they are held, both during the actual celebration of the event and before and after it. This study presents a new approach based on spatial analysis, GIS, and data coming from Location Based Social Networks to model the spatiotemporal dimension of impacts associated with the Rio 2016 Olympic Games. Geolocalized data from Twitter are used to analyze the activity pattern of users from two different viewpoints. The first monitors the activity of Twitter users during the event -the arrival of visitors, where they came from, and the use resident and tourist made of different areas of the city. The second assesses the spatiotemporal use of the city by Twitter users before the event, compared to the use during and after the event. The results not only reveal which spaces were the most used while the Games were being held but also changes in the urban dynamics after the Games. Both approaches can be used to assess the impacts of mega-events and to improve the management and allocation of urban resources such as transport and public services infrastructure.

Twitter Data Analysis

2018 ◽  
pp. 124-151 ◽  
Author(s):  
Chitrakala S
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Information Diffusion ◽  
Big Data Analytics ◽  
Influence Maximization ◽  
Streaming Data ◽  
Hybrid Analysis ◽  
Topic Analysis ◽  
Twitter Data ◽  
Twitter Data Analysis

Analyzing Social network data using Big Data Tools and techniques promises to provide information that could be of use in recommendation systems, personalized service and many other applications. A few of the analytics that do this include sentiment analysis, trending topic analysis, topic modeling, information diffusion modeling, provenance determination and social influence study. Twitter Data Analysis involves analyzing data specifically obtained from Twitter, both tweets and the topology. There are three major classifications on the type of analysis being performed such as Content based, Network based and Hybrid analysis. Trending Topic Analysis in the context of Content based static data analysis and Influence Maximization in the context of Hybrid analysis on data streams using the power of Big Data Analytics are discussed. A novel solution to Trending Topic analysis to generate topic evolved, conflict-free sequential sub summaries and influence maximization to handle streaming data are explained with experimental results.

Political Sentiment Analysis on Indian Perspective Using Twitter Data

2019 ◽  
Vol 16 (10) ◽  
pp. 4224-4231
Author(s):  
Dharminder Yadav ◽  
Himani Maheshwari ◽  
Umesh Chandra
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Analysis Data ◽  
Indian People ◽  
Opposition Party ◽  
Twitter Data ◽  
Twitter Users ◽  
Indian Perspective

This paper aims to analyse the opinion of Indian people on the bases of tweets about the supreme leaders of party 1 (present government of Indian) and president of the second-largest party or leader of the opposition party is party 2. Researchers used Twitter API using R to get the tweets. R is a language used for data analysis, data mining, sentiment analysis, and opinion mining. In this paper corpus-based and dictionary-based methods were used to explore the tweets. This paper tried to show the sentiments of Twitter users towards leader of party 1 and leader of party 2 individually and classified the same as positive, negative and neutral.

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