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

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TLDR
It is shown 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.
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.

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Journal ArticleDOI

Automated Text Analysis for Consumer Research

TL;DR: It is argued that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided.
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Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data

TL;DR: The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users.
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Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective

TL;DR: The study suggests that knowledge fusion to improve NPS is not automatic and requires strategic choices to obtain its benefits.
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Global disease monitoring and forecasting with Wikipedia.

TL;DR: A research agenda designed to produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art is closed.
Journal ArticleDOI

Quantifying the effect of sentiment on information diffusion in social media

TL;DR: The findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-calledpositive bias.
References
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Journal ArticleDOI

Twitter mood predicts the stock market.

TL;DR: This work investigates whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time and indicates that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others.
Journal ArticleDOI

Detecting influenza epidemics using search engine query data

TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.
Proceedings ArticleDOI

Earthquake shakes Twitter users: real-time event detection by social sensors

TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Proceedings Article

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

TL;DR: It is found that the mere number of messages mentioning a party reflects the election result, and joint mentions of two parties are in line with real world political ties and coalitions.
Proceedings ArticleDOI

Predicting the Future with Social Media

TL;DR: It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media.
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