↓ Skip to main content

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Overview of attention for article published in PLOS ONE, August 2013
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
18 news outlets
blogs
9 blogs
policy
1 policy source
twitter
116 X users
facebook
6 Facebook pages
wikipedia
5 Wikipedia pages
googleplus
5 Google+ users
reddit
2 Redditors
video
1 YouTube creator

Citations

dimensions_citation
235 Dimensions

Readers on

mendeley
328 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0071226
Pubmed ID
Authors

Márton Mestyán, Taha Yasseri, János Kertész

Abstract

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

X Demographics

X Demographics

The data shown below were collected from the profiles of 116 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 328 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Germany 3 <1%
United Kingdom 2 <1%
Netherlands 1 <1%
France 1 <1%
Italy 1 <1%
Australia 1 <1%
Brazil 1 <1%
Switzerland 1 <1%
Other 4 1%
Unknown 309 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 74 23%
Student > Master 73 22%
Researcher 43 13%
Student > Bachelor 33 10%
Professor 13 4%
Other 46 14%
Unknown 46 14%
Readers by discipline Count As %
Computer Science 92 28%
Social Sciences 46 14%
Business, Management and Accounting 38 12%
Engineering 22 7%
Economics, Econometrics and Finance 14 4%
Other 57 17%
Unknown 59 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 309. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2023.
All research outputs
#113,883
of 25,872,466 outputs
Outputs from PLOS ONE
#1,787
of 225,654 outputs
Outputs of similar age
#680
of 211,471 outputs
Outputs of similar age from PLOS ONE
#38
of 4,749 outputs
Altmetric has tracked 25,872,466 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 225,654 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 211,471 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 4,749 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.