Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written

Abstract

This study investigates the impact of tweets on box office revenue. Specifically, the study focuses on the times when tweets were written by examining the impact of pre‐ and post‐consumption tweets on box office revenue; an examination that is based on Expectation Confirmation Theory. The study also investigates the impact of intention tweets versus subjective tweets and the impact of negative tweets on box office revenue. Targeting 120 movies released in the US between February and August 2012, this study collected tweet information on a daily basis from two weeks before the opening until the closing and box office revenue information. The results indicate that the disconfirmation that occurs in relation to the total number of pre‐consumption tweets for a movie has a negative impact on box office revenue. This premise suggests that the formation of higher expectations of a movie does not always result in positive results in situations where tweets on perceived movie quality after watching spread rapidly. This study also reveals that intention tweets have stronger effects on box office revenue than subjective tweets.

Think exogenous to excel: alternative supply chain data to improve transparency and decisions

“…Therefore, they can be used to create a connection between the release of a product, the overall online discussion, and then the actual sales of the products. The overall volumes of discussion can be used to predict or estimate sales volumes (Baek, Ahn, & Oh, 2014;Duan & Liao, 2013;Wang, Cai, & Huang, 2010). Even the number of fans on Facebook can be correlated with retail sales (Schultz, 2016).…”

Predictive value of video-sharing behavior: sharing of movie trailers and box-office revenue

“…For this study, movie-related data are collected from Boxofficemojo (www.boxofficemojo.com), IMDb (www.imdb.com), and Numbers (www.the-numbers.com), which have been referred to by many previous studies (Baek et al, 2014;Basuroy et al, 2003;Dellarocas et al, 2007;Duan et al, 2008b;Liu, 2006;Rui et al, 2013).…”

“…Previous studies on the motion picture industry have made diverse attempts to predict box-office revenue from electronic word-of-mouth (eWOM) and text-based social media perspectives (Asur and Huberman, 2010;Baek et al, 2014;Rui et al, 2013).…”

Public Opinion Sensing and Trend Analysis on Social Media: A Study on Nuclear Power on Twitter

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