Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data

Nicolai H. Egebjerg, Niklas Hedegaard, Gerda Kuum, Raghava Rao Mukkamala, Ravi Vatrapu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper explores the predictive power of bigsocial data in regards to football fans’ off-line and on-linebehaviours. We address the research question of to what extentcan big social data from Facebook predict the numberof spectators and TV ratings in the case of Danish NationalFootball Association (DBU). The predictive model was built fromFacebook, match attendance, and TV ratings data sets from 2014-2016. The best fit was a linear regression model with GLM coding.Ultimately, the model did best when predicting the number ofspectators based on the Facebook activity during a match as wellas the activity from the last two weeks leading up to the match.Furthermore, the data reveals that photos generates the mostactivity on the national team’s page and with videos runningat higher production costs there might be some unexploitedpotential for DBU to improve its social media marketing strategy.Although data limitations are present, this research concludesthat predictive models based on big social data can indeed offerimportant insights for companies to understand their customerbase and how to improve marketing strategies.Index Terms—Big data, Big social media data, Danish NationalTeam, DBU, Facebook data, Football fans, Spectators, TV rating
This paper explores the predictive power of bigsocial data in regards to football fans’ off-line and on-linebehaviours. We address the research question of to what extentcan big social data from Facebook predict the numberof spectators and TV ratings in the case of Danish NationalFootball Association (DBU). The predictive model was built fromFacebook, match attendance, and TV ratings data sets from 2014-2016. The best fit was a linear regression model with GLM coding.Ultimately, the model did best when predicting the number ofspectators based on the Facebook activity during a match as wellas the activity from the last two weeks leading up to the match.Furthermore, the data reveals that photos generates the mostactivity on the national team’s page and with videos runningat higher production costs there might be some unexploitedpotential for DBU to improve its social media marketing strategy.Although data limitations are present, this research concludesthat predictive models based on big social data can indeed offerimportant insights for companies to understand their customerbase and how to improve marketing strategies.Index Terms—Big data, Big social media data, Danish NationalTeam, DBU, Facebook data, Football fans, Spectators, TV rating
LanguageEnglish
Title of host publicationProceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017
EditorsGeorge Karypis, Jia Zhang
Place of PublicationLos Alamitos, CA
PublisherIEEE
Date2017
Pages81-88
Article number8029312
ISBN (Print)9781538619964
ISBN (Electronic)9781538619957, 9781538619971
DOIs
StatePublished - 2017
Event6th IEEE International Congress on Big Data. BigData Congress 2017 - Hilton Hawaiian Village Waikiki Beach Resort, Honolulu, United States
Duration: 25 Jun 201730 Jun 2017
Conference number: 6
http://www.bigdatacongress.org/2017/

Conference

Conference6th IEEE International Congress on Big Data. BigData Congress 2017
Number6
LocationHilton Hawaiian Village Waikiki Beach Resort
CountryUnited States
CityHonolulu
Period25/06/201730/06/2017
Internet address

Keywords

  • Big data
  • Big social media data
  • Danish National Team
  • DBU
  • Facebook data
  • Football fans
  • Spectators
  • TV ratings

Cite this

Egebjerg, N. H., Hedegaard, N., Kuum, G., Mukkamala, R. R., & Vatrapu, R. (2017). Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data. In G. Karypis, & J. Zhang (Eds.), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017 (pp. 81-88). [8029312] Los Alamitos, CA: IEEE. DOI: 10.1109/BigDataCongress.2017.20
Egebjerg, Nicolai H. ; Hedegaard, Niklas ; Kuum, Gerda ; Mukkamala, Raghava Rao ; Vatrapu, Ravi. / Big Social Data Analytics in Football : Predicting Spectators and TV Ratings from Facebook Data. Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. editor / George Karypis ; Jia Zhang. Los Alamitos, CA : IEEE, 2017. pp. 81-88
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title = "Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data",
abstract = "This paper explores the predictive power of bigsocial data in regards to football fans’ off-line and on-linebehaviours. We address the research question of to what extentcan big social data from Facebook predict the numberof spectators and TV ratings in the case of Danish NationalFootball Association (DBU). The predictive model was built fromFacebook, match attendance, and TV ratings data sets from 2014-2016. The best fit was a linear regression model with GLM coding.Ultimately, the model did best when predicting the number ofspectators based on the Facebook activity during a match as wellas the activity from the last two weeks leading up to the match.Furthermore, the data reveals that photos generates the mostactivity on the national team’s page and with videos runningat higher production costs there might be some unexploitedpotential for DBU to improve its social media marketing strategy.Although data limitations are present, this research concludesthat predictive models based on big social data can indeed offerimportant insights for companies to understand their customerbase and how to improve marketing strategies.Index Terms—Big data, Big social media data, Danish NationalTeam, DBU, Facebook data, Football fans, Spectators, TV rating",
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author = "Egebjerg, {Nicolai H.} and Niklas Hedegaard and Gerda Kuum and Mukkamala, {Raghava Rao} and Ravi Vatrapu",
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Egebjerg, NH, Hedegaard, N, Kuum, G, Mukkamala, RR & Vatrapu, R 2017, Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data. in G Karypis & J Zhang (eds), Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017., 8029312, IEEE, Los Alamitos, CA, pp. 81-88, Honolulu, United States, 25/06/2017. DOI: 10.1109/BigDataCongress.2017.20

Big Social Data Analytics in Football : Predicting Spectators and TV Ratings from Facebook Data. / Egebjerg, Nicolai H. ; Hedegaard, Niklas ; Kuum, Gerda; Mukkamala, Raghava Rao; Vatrapu, Ravi.

Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. ed. / George Karypis; Jia Zhang. Los Alamitos, CA : IEEE, 2017. p. 81-88 8029312.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - This paper explores the predictive power of bigsocial data in regards to football fans’ off-line and on-linebehaviours. We address the research question of to what extentcan big social data from Facebook predict the numberof spectators and TV ratings in the case of Danish NationalFootball Association (DBU). The predictive model was built fromFacebook, match attendance, and TV ratings data sets from 2014-2016. The best fit was a linear regression model with GLM coding.Ultimately, the model did best when predicting the number ofspectators based on the Facebook activity during a match as wellas the activity from the last two weeks leading up to the match.Furthermore, the data reveals that photos generates the mostactivity on the national team’s page and with videos runningat higher production costs there might be some unexploitedpotential for DBU to improve its social media marketing strategy.Although data limitations are present, this research concludesthat predictive models based on big social data can indeed offerimportant insights for companies to understand their customerbase and how to improve marketing strategies.Index Terms—Big data, Big social media data, Danish NationalTeam, DBU, Facebook data, Football fans, Spectators, TV rating

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BT - Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017

PB - IEEE

CY - Los Alamitos, CA

ER -

Egebjerg NH, Hedegaard N, Kuum G, Mukkamala RR, Vatrapu R. Big Social Data Analytics in Football: Predicting Spectators and TV Ratings from Facebook Data. In Karypis G, Zhang J, editors, Proceedings of the 6th IEEE International Congress on Big Data. BigData Congress 2017. Los Alamitos, CA: IEEE. 2017. p. 81-88. 8029312. Available from, DOI: 10.1109/BigDataCongress.2017.20