hashtags, tweets, and movie receipts

Running head: HASHTAGS, TWEETS AND MOVIE RECEIPTS 1

Abstract

Movies represent a popular icon of culture. Understanding of the role of social media in

reflecting movie success could provide insight into the function of communication in popular

culture and provide a new set of potential analytics to track and predict movie success. This

study uses a geospatially-sensitive method of tracking Twitter hashtags and movie titles, both

prior and subsequent to movie release in the U.S. market, to predict movie box office receipts.

Four movies tracked from the same weekend opening through four weeks shows correlations

between Tweet metrics and box office receipts. A total of 87,978 tweets were collected during

four weeks. Among those tweets, gross original tweets tended to present the highest correlations

to box office, and the strength of correlations tended to be stronger in week 1 than in subsequent

weeks, ranging from .58 to .98. The correlations were also generally stronger for the smaller

budget and less distributed movies than the bigger box office movies. Patterns and differences

across the movies are examined, and theoretical implications of communication diffusion

regarding popular culture are examined.

Key words/terms: Box office, Hollywood, movie, social media, Twitter, tweets.

HASHTAGS, TWEETS AND MOVIE RECEIPTS 2

MONEYBALL

Since their inception in the 1880s, films and movie-going have become a staple of the

U.S. culture. Films have contributed to the canon of popular culture as they provide new

reference points and experiences for the masses (Jeacle, 2009). Furthermore, as a major cultural

export, for good and ill, Hollywood movies contribute to the globalization of U.S. values and

cultural norms to nations far and wide (Bakker, 2005; Matusitz & Payano, 2012). Despite the

artists and the artistry, a prime goal of the American film industry is to make money (Greenwald

& Landry, 2009; Izod, 1988). And they do. Hollywood-led creative industries for between $500

and $700 billion (Associated Press, 2013; Bureau of Economic Analysis, 2015, 2016; Dodd,

2015; National Endowment for the Arts, 2013), of which movies alone contributed over $100

billion to the U.S. economy (Dodd, 2015; Kern, Wasshausen, & Zemanek, 2015). Movies also

often fail, and fail spectacularly, for reasons that continue to represent a mystery. This study is

concerned with finding impacts, outcomes and effects social media have on films within the U.S.

Risky Business

Making a movie is a huge investment and like all investments, there is an inherent

associated risk (Izod, 1988; Pokorny & Sedgwick, 2010). Understanding the nature of their

successes and failures merits further investigation. Despite the century-long history of film and

all of the trials and tribulations the industry has faced; there are still no known (i.e., non-

proprietary) accurate models predicting whether a picture is going to be a hit or make a profit.

Furthermore, being a hit and making a profit are distinct outcomes—with movies costing over

$200 million to make, even very popular movies can be industry failures.

Just because a film gets a green light with high profile names, does not mean it will

perform well. Studios attempt to bypass such risks by designing soft openings, promoting limited


releases to see how audiences react, and testing movies with focus groups. All of these methods

are cumbersome, expensive, and far from always accurate. Clearly there is significant potential

value to developing methods that better manage the uncertainty or risks created by the prospect

of a new film. Social media may hold the answer to this quandary.

THE SOCIAL NETWORK

About three quarters (~74%) of adults with Internet access use some type of social

network site (Pew Research Center, 2013), representing two-thirds of U.S. adults, a 10-fold

increase over the last decade (Perrin, 2015). Sites like MySpace, Facebook, Twitter, Instagram

fall into this category, and most people would be able to identify a social network site (SNS) if

they saw one. About 23% of those over the age of 18 have a Twitter account (Duggan, Ellison,

Lampe, Lenhart, & Madden, 2015)—as of April 2015 Twitter had an average of 302 million

active users a month (Welch & Popper, 2015).

Twitter enables its users to share their thoughts at any moment with anyone (who is

willing to “listen”), and with a low sense of commitment (Murthy, 2013). Tweets range from

emotionally charged, to banal and inconsequential. Many tweets contain outside content (e.g.,

pictures, links, videos). Overall, Twitter has four main uses: (1) daily chatter, these are everyday

things maybe even mundane occurrences; (2) conversations, strings of messages between users

preceded by @ (e.g., @user); (3) sharing information, this generally comes in the form of a

shortened URL; and (4) reporting news, users post the information about current events (Java,

Song, Finin, & Tseng, 2007). Market research indicates frequent moviegoers are particularly

invested in personal communication technologies (Motion Picture Association of America,

2015), and that social media mentions by friends are an important impetus to viewing movies

(Nielsen, 2013; Worldwide Motion Picture Group, 2013).


#BOXOFFICESUCCESS #BOXOFFICEREVENUE

Another aspect of Twitter that makes the site particularly attractive for social scientists is

the site’s use of hashtags. A hashtag is a tagged word or phrase without spacing preceded by the

pound sign (e.g., #hashtag). These tagged words or phrases enable Twitter to string together

conversations on different topics (Murthy, 2013) and anyone from across the world can search

for them and chime in on the conversation (Doctor, 2013). Hashtags are an accessible way of

tracking certain terms within SNS, as they enable users and researchers to limit their searches to

the words following the #. Hashtags can also be used to track certain goods or products soon to

be released. In more general terms, scholars and industries recently began to analyze user-

generated content from various sites to gather information about certain products or services

(e.g., Chen, Liu, & Zhang, 2012; Craig, Greene, & Versaci, 2015; Dellarocas, Gao, & Narayan,

2010; Dellarocas, Zhang, & Awad, 2007; Hennig-Thurau, Wiertz, & Feldhaus, 2015; Kim, Park,

& Park, 2013; Lee, Hosanagar, & Tan, 2015; Mestyán, Yasseri, & Kertész, 2013; Wong, Sen, &

Chiang, 2012). These efforts, in conjunction with the increasingly common practice among

movie studios of creating user profiles on SNS for themselves and for upcoming film releases,

enables studios to reach audiences in new ways. Given Twitter use by industry, for marketing

movies; and by consumers, for commenting about movie experiences, movie hashtags may

provide a key window into predicting whether a movie will be successful at the box office.

THE PROPOSAL

This study seeks to examine the intersection between online behavior (in tweets) and the

box office revenue for opening films. This objective has received limited prior attention. For

instance, edits and views on a prospective film’s Wikipedia page have been used as a good

predictor of ticket sales (Mestyán et al., 2013). In this case the higher the amount of edits and


views on a Wikipedia entry usually indicated a greater sense of interest in the film, which

contributed to the overall success of a movie. Another study measured online popularity of a film

via views and comments on its respective trailers as indicators of awareness of the film and

intention to see the movie. The latter was measured by a user’s desire to view a movie using the

ratings from the Fandango app (Craig et al., 2015). This approach measured “e-buzz” (i.e., online

hype or chatter generated by a film), which is a form of online or electronic word-of-mouth (e-

WOM) (e.g., Chiang, Wen, Luo, Li, & Hsu, 2014; Craig et al., 2015; Dellarocas et al., 2010;

Dellarocas et al., 2007; Hennig-Thurau et al., 2015; Kim et al., 2013; Lee et al., 2015).

Ultimately, the most important aspects of a film’s “e-buzz” and eventually box office revenue

are the size of film’s budget, genre (i.e., action and horror) and whether the film was a sequel

(Craig et al., 2015).

Even though many individuals may watch a trailer and leave their thoughts on forums or

comment sections, most comments or reviews represent opposite sides of the spectrum. People

tend to frequently review or comment on films hoping others may hear about them and “check

them out,” or they comment on something already popular and add on to that general discourse

(Dellarocas et al., 2010). Conversely, some individuals may pursue expert film reviews, which

tend to have specific impacts on how a film performs at the box office (Chen et al., 2012; Kim et

al., 2013). For instance, those reviews published before opening day have a stronger impact the

day of the premiere, whereas those published afterward have minimal influence (Chen et al.,

2012). Additionally, the valence of expert reviews and e-WOM frequency are important when

trying to predict film revenue in the U.S.; meanwhile, e-WOM from peers, as opposed to critics,

is a much better predictor of international success (Kim et al., 2013).


Most of the research to date has focused on varying degrees of e-WOM and how it

contributes to ticket sales (e.g., Chiang et al., 2014; Craig et al., 2015; Dellarocas et al., 2010;

Dellarocas et al., 2007; Hennig-Thurau et al., 2015; Kim et al., 2013; Lee et al., 2015). Yet, most

research has neglected using SNS as a predictive tool, even though “just the publicized intention

to promote a film using Internet social networking may lead to higher revenues” (Westland,

2012, p. 179). Although, Westland (2012) used Google search data, he found that being active on

SNS increases film revenues by 64% and “search activity” by 48%, because SNS campaigns get

people to talk about a film and search it, before and after the release of the movie.

About a dozen or so studies have exclusively used Twitter as an indicator or predictor of

box office success (e.g., Arias, Arratia, & Xuriguera, 2013; Asur & Huberman, 2010;

Barthelemy, Guillory, & Mandal, 2012; Deltell, Osteso, & Claes, 2013; Hennig-Thurau et al.,

2015; Lu, Wang, & Maciejewski, 2014; Thigale, Prasad, Makhija, & Ravichandran, 2014; Treme

& Vanderploeg, 2014; Wong et al., 2012; Tsou, Jung Allen, et al., 2015; Yang, Tsou, Jung, et al.,

2016). Overall, these studies have looked at different aspects of Twitter and how it might

contribute to predicting movie revenues. For instance, some have looked at the amount of tweets

and how they relate to gross revenues (Arias et al., 2013; Asur & Huberman, 2010; Barthelemy

et al., 2012; Tsou, Jung Allen, et al.; 2015; Yang, Tsou, Jung, et al., 2016). Others have

examined the social media of a film’s protagonists and its potential impact on ticket sales (Treme

& Vanderploeg, 2014). More specifically, Asur and Huberman (2010) used the average amount

of tweets per hour, a week before the premiere to successfully predict if a movie would be a hit.

Additionally, Arias and colleagues (2013) used the daily tweet count to predict box office

success, and claimed a causal relationship between tweet amount and revenue. Meanwhile,

others have analyzed the content of tweets (i.e., sentiment analysis) to see if the overall effect


toward films was an indicator of film’s success, ultimately concluding Twitter content does not

really matter (Arias et al., 2013; Lu et al., 2014; Thigale et al., 2014). In contrast, Wong and

colleagues (2012) compared the valence of Twitter reviews to those on critique websites and

found high approval on Twitter and IMDb can be good indicators of ticket sales. Further, people

on Twitter have more positive comments when reviewing films, but this goes by the wayside

when it comes to Oscar-nominated films (Wong et al., 2012).

Despite the value of such studies, the use of hashtags as a way of measuring, tracking, or

predicting box office success has generally been neglected. Only a few studies specifically used

movie hashtags in their prediction efforts (e.g., Deltell et al., 2013; Issa, 2016; Lu et al., 2014;

Yang, Tsou, Jung, et al., 2016). This appears to be a missed opportunity by researchers and

industry alike, because hashtags provide an easy way of tracking topics while reducing

extraneous information (i.e., noise), especially given certain convenient social media search

procedures. For example, Chiang and colleagues (2014) state that in e-WOM or online settings

using keywords is the most important aspect of marketing a film.

THE MULTILEVEL MODEL OF MEME DIFFUSION (M3D)

A recent theoretical synthesis Spitzberg (2014) formulated the multilevel model of meme

diffusion (M3D) to integrate various theories, including framing, narrative, diffusion of

innovations, information, and communicative competence). The M3D model anticipates certain

features of (1) memes or social media messages, (2) communicators, (3) structural and subjective

network structures, (4) societal processes, and (5) geo-technical factors predict or moderate

meme diffusion dynamics (Spitzberg, 2014). A meme is “an act or meaning structure” capable of

being imitated or copied by any given interactant (Spitzberg, 2014, p. 312). Essentially, any

tangible idea that can be replicated is a potential meme, which means tweets and similar digitally


transmitted messages are potential memes because they are inherently communicative and

replicable (Spitzberg, 2014).

Several of the M3D components appear comparable to those that who contribute to a

movie’s online presence and ultimately its fate at the box office. For instance, at the meme level

particular phrasings, insights, jokes, or images might be particularly infectious in their

propagation. At the source level, social network analysis may reveal that certain sources, critics,

or popular media celebrities may be particularly potent as amplifiers of meme propagation, and

thereby movie buzz. At the social network level, there are questions of whether homophilous or

heterophilous networks are more efficient at energizing tweet propagation in correspondence to

movie sales. At the societal level, there is attention competition in the form of news events and

competing movie campaigns and releases. At the geo-technical level, little is currently known

about the relationships between limited versus national release, west versus east coast release,

film format (e.g., 3D, IMAX, etc.), and social media.

By capturing data in a manner that holds certain history factors constant (e.g., holidays,

seasonal factors, weather, etc.), this study permits a natural comparison of tweets across

competing options for patrons’ attentional and economic investments (e.g., time, tickets). As

such, the relative influence of social media may be permitted to demonstrate their impact across

these movie choices. It is anticipated that Twitter attention to movie hashtags and movie box

office will reveal a reciprocal correlation. Given that virtually all movies experience gradual

declines after initial release, the predictive value of tweets is expected to decline proportionally.

METHOD

Data Collection


A cross-disciplinary team of researchers at a large public southwestern university

developed a web-based social media analytics and research testbed (SMART) dashboard with

geo-targeted Twitter application programming interfaces (APIs) to provide real-time surveillance

for programmed search terms (Yang, Tsou, Jung, et al., 2016). The dashboard mines tweets and

provides categorizations of some of the most relevant information as visual analytics (Yang,

Tsou, Jung, et al., 2016; Tsou, Jung, Allen et al., 2015). The data collected are downloadable as

Excel files.

This study selected a movie release weekend instead of a particular film, an approach that

seems to be largely unexplored. A set of six prospective weekend release dates was selected

based on Box Office Mojo’s release schedule from January 22 to February 26, 2016

(http://www.boxofficemojo.com/schedule/). The weekend of January 29 was randomly selected.

Only films Box Office Mojo deemed “wide release” (i.e., released in over 600 theaters; Escoffier

& McKelvey, 2015) at the moment of selection were tracked and the weekend of January 29 had

four major releases: Kung Fu Panda 3 (KFP3), The Finest Hours (TFH), Fifty Shade of Black

(FSoB), and Jane Got a Gun (JGaG). Since there is the potential for capturing extensive

information, both unnecessary and extraneous, only tweets using the films’ official hashtags

were used. The official hashtag requires minimal exertion from followers, yet is “challenging”

enough that only those who are motivated to share or are particularly invested or interested in a

film are likely to take the time to do so. The hashtags were found either on the film’s official

website, the studio’s Twitter feed, or the film’s official Twitter account. All of the films for the

weekend of January 29 had relatively straightforward and unique hashtags (i.e., the film’s title

with no spaces, preceded by #). The following terms were the ones used for tracking tweets:

#KungFuPanda, #TheFinestHours, #FiftyShadesOfBlack, and #JaneGotAGun.


Data on film revenues were tracked and collected via Box Office Mojo

(http://www.boxofficemojo.com/daily/), which is one of the most frequently cited and used sites

for film earnings. Box Office Mojo provides detailed information on a film’s domestic

performance (e.g., number of theaters released, daily/weekly revenue, film ranking on any given

weekend, etc.).

This study only used data (i.e., tweets and revenue) from the U.S. for two reasons. First,

revenue data for the U.S. on Box Office Mojo is far more comprehensive than for foreign

earnings. For instance, U.S. revenues can be distilled into daily earnings; meanwhile foreign

sales can only be seen by week. Second, in order to have corresponding data sets tweets from the

U.S. were collected. More specifically, 32 major U.S. metropolitan areas were surveyed.

Using the SMART dashboard, tweets that included any of the movie hashtags were

collected from January 22 through February 25. Time frames for data collection across the

literature are inconsistent. Therefore, this analysis took two different suggestions from the

literature: one week before the premier (Asur & Huberman, 2010) and four weeks after opening

day (Escoffier & McKelvey, 2015) as these times frames were believed to be sufficient for valid

analysis of movie revenues. According to Box Office Mojo’s data, a “week” in the life of most

films is from Friday through Thursday, given that a slate of new films is released every Friday.

The first step involved downloading the daily revenue data from Box Office Mojo into an

Excel workbook. The second step was to download the Twitter data from the SMART

dashboard, into an Excel workbook. During the selected time period 87,978 fell in the desired

range (Jan. 22 – Feb. 25) and were used in the analysis. Although U.S. cities were being tracked,

these tweets were from all across the globe. The dataset includes several variables, but five


columns were mainly used for the analysis: hashtag used, date and time (GMT), tweet text, user

name, and whether or not it was a tweet or retweet.

Once separate sheets were created for all four films with all relevant variables filtered by

movie, the respective tweets (i.e., overall tweets and retweets; gross tweets; gross retweets) in

them were counted by date. In order to account for “bots” the users column was copied and

pasted into a word cloud website. This allowed a visual and numeric representation of the most

frequent users, which were: @week99er, with 972 (re)tweets; @MarlonWayans, with 526

(re)tweets, and @caseysherman123 with 507 (re)tweets. No other users came as close to

reaching such a high volume of (re)tweets, the next closest user was @jemandboo63 with 285

(re)tweets. More importantly, Marlon Wayans was the protagonist, co-writer and co-producer of

FSoB; meanwhile Casey Sherman is the author of the book “The Finest Hours,” upon which the

film by the same name is based. Both of these men clearly had a sense of investment in “their”

film’s performance. On the other hand, @week99er, is a blogging mom from Detroit, MI who

was constantly retweeting anything pertaining to Kung Fu Panda 3. Her contributions were

probably due to some sort of script or bot-program that enabled extraordinary retweet rapidity

and volume. The only film that did not have such an influential advocate was JGaG. Regardless,

a similar sorting and count feature was conducted for all tweets that included the aforementioned

usernames, and these analyses were conducted with and without these outliers. The resulting

cleaned and combined data sheet contained all the final figures necessary for the analysis: day of

the week and date, counts for gross tweets, tweets only, retweets only, tweets without top

contributor, daily gross revenue, aggregated gross revenue, theater count, and daily average

revenue by theater.


The following operational definitions describe the major variables analyzed: gross tweets

sans “user,” gross tweets all, gross original tweets, gross retweets, and revenue. First, gross

tweets sans “user” counts all the (re)tweets, those from the highest contributor for each film were

filtered out and only the remaining (re)tweets are counted. This was done to account for any bots

or advertisers that could skew or generate bias in the analysis. Second, gross tweets all includes

every tweet and retweet for a particular movie. Third, gross original tweets is compromised

exclusively of original tweets using the film’s official hashtag. Fourth, gross retweets counts a

film’s retweets. Finally, gross revenue is a film’s total earnings in U.S. dollars, not adjusted for

inflation.

Three major forms of analysis were conducted with the data: graphs, correlations, and

word clouds (using an R script). Dual axis graphs for all films were analyzed to identify any

anomalies or trends between daily gross tweet count and daily gross revenue. In addition, using

the already curated final data, correlations were derived to identify the strength of relationship

between variables and to facilitate interpretation and explanation. The columns analyzed were:

all (re)tweets and revenue; all tweets, excluding top contributors, and revenue; only original

tweets and revenue; only retweets and revenue. The aforementioned categories were analyzed

daily and weekly for all four films. Finally, word clouds were created to easily spot any major

themes or sentiments about a given film during opening weekend.

“SHOW ME THE MONEY”: RESULTS

All films debut within a social context. Films are constantly competing for the audience’s

attention. So, at any given time movies are at odds with external factors and with other films

released before or after them. To contextualize the findings, a few notable global and national


events are presented in the time frame within which the four movies studied were competing for

attention (see Table 1).

There are two preliminary noteworthy findings. First, the number of retweets from the

three most frequent users was still relatively insignificant compared to the greater volume

generated by the entire tweet set. In fact, these top contributors only accounted for roughly 1% of

the overall tweet count. Therefore, analyses proceeded with these users’ tweets included.

Second, the number of geotagged tweets was so low in most cities they could not be used to

conduct analyses..

#KungFuPanda

This film had a particularly distinctive pattern reflecting revenue and tweets (Figure 1).

The movie’s tweets (Table 2) peak on opening day (Jan. 29) and reveal two separate bursts of

tweets in the middle of different subsequent weeks (Tuesday Feb. 2 and Wednesday Feb. 10).

Meanwhile, revenue for the film appeared to trail after the bursts of Twitter chatter, with the

sharpest peaks on Saturdays. However, from February 13-15 the revenue line is more rounded,

perhaps due to Presidents Day weekend.

There was a moderate correlation (r = .367) between all gross tweets count and daily

gross revenue (Table 3). In contrast to the overall correlation, the correlations between daily

original tweets and revenue was strong (r = .578), and in particular, the correlations between

original tweets and gross revenue during week 1 (r = .70) and week 3 (r = .84) were very strong.

The word cloud (Figure 2), on the other hand, does not reveal much about the film or any

sentiments around it. The biggest and thus most prevalent term, @originalfunko, is a toy

manufacturer. Furthermore, one of the terms beginning with “https” is to a sweepstakes link from

@originalfunko that gave those who retweeted (https://t.co/pyShnzw2Dn) would have an


opportunity to win one of their toys. One of the smaller yet noticeable terms is @dwanimation,

which is the handle for Dream Works Animation, the studio that created and released the film.

#TheFinestHours

The Finest Hours data share a few similarities with KFP3. First, the highest number of

tweets came in on opening day (Jan. 29), which was followed by the highest revenue the

following day. Second, the highest peaks in revenue were on Saturdays, including the three-day

curve over Presidents Day weekend, which also coincided with Valentine’s day. According to

the daily box office results it was also during the week of February 12 (Week 3) that the amount

of theaters dropped from 3,143 to 1,794 (Box Office Mojo, n.d.b). This could have contributed to

a substantial decline in tweets and revenue (see Figure 3).

Meanwhile, the overall daily correlations between tweets and revenue were stronger for

TFH. The relationship between original tweets and revenue was the strongest correlation (r =

.722) out of this category (see Table 6). In terms of week-to-week relationships with revenue,

gross original tweets from Week 1 had a moderately high correlation (r = .652).

The word cloud for TFH, shares a few similarities with that of KFP3 (Figure 4). First,

one of the most frequent terms was the name of the studio in charge of the film’s release and

distribution: @disneystudios. Another similarity is the prevalence of terms like “win,” “chance,”

and the presence of a few truncated URLs by virtue of phrases beginning with “https” and a

string of unrelated letters. One of these links (https://t.co/r519XlEMnL) led directly to the

Fandango website, so users could purchase their tickets for the film. Meanwhile, another link

was for a sweepstakes (https://t.co/uJs5nai6oo). However, the opening weekend tweets for TFH

mention some of the actors who have a role in the film (e.g., Chris Pine, Casey Affleck). Another

noticeable difference is the mention of terms associated with the actual story. These were


sentiments or themes the movie addressed, such as “courage,” “rescue,” “honor,” “impossible,”

“inspired,” and “true” to name a few.

#FiftyShadesOfBlack

Fifty Shades of Black was the proverbial firework, because it was fast to rise and it faded

out just as quickly (Figure 5). On opening weekend, its revenue and Twitter chatter were

relatively comparable, but after that, revenue and tweets drastically dwindled. The film’s

strongest performances came on Saturday Feb. 6 and its other minimal revenue bump came the

following weekend. The Twitterverse was virtually silent after opening day during the first week

(Table 8). Perhaps the low level of tweets, and revenue, was partially due to the steep decrease in

theaters on Friday February 12, from 2,075 theaters to 485 (Box Office Mojo, n.d.a).

Despite such low Twitter traction, FSoB had very strong correlations between the amount

of tweets and revenue (Tables 9 and 10). The strongest correlation came from total original

tweets (r =.95), followed by strong relationships on gross tweets (r = .92) and all tweets (r = .92).

Meanwhile, the world cloud for FSoB shares a few similarities with TFH (Figure 6).

Chief among them the mention of @marlonwayans, who starred, co-wrote and co-produced the

film, as well as the appearance of a few shortened URLs “https.” More specifically, one of the

links (https://t.co/gHthZacTYy) led users to a site where they could type in their zip code and

purchase tickets for a theater near them. Two additional terms appearing frequently were

@joejonas and the link to his retweet (https://t.co/Z4eykZEk8K). Jonas was showing support and

providing an endorsement for the film and his friend Marlon Wayans. In a deviation from KFP3

and TFH, the buzz around FSoB had no mention or appearance of a movie studio; instead, one of

the smaller terms that stand out is the “fsobmovie,” indicating at least one alternative hashtag or


term was created by the Twitter community. Despite this, very little sentiment surrounded this

film, the most noteworthy terms were “see,” “movie,” “hilarious,” and “comedy.”

#JaneGotAGun

The Twitter conversations surrounding JGaG were minimal, as were the film’s revenues.

Despite this, the “distance” between tweets and revenue remained rather constant from January

29 to February 6 (Figure 7). After this, both revenue and commentary plummeted after the

second weekend. Unfortunately, JGaG had the most noticeable cut in theaters, losing screens

every week after its release. It started out on 1,210 theaters on Jan. 29. By its second week, the

film had lost 179 theaters and was only screened on 1,031. Yet, by the third week, the film was

only screened at eight.

Even with its low level of tweets (Figure 7) and revenue, JGaG had the strongest

correlation out of all four films (Table 12). The number of original tweets and revenue were

correlated .97. All tweets also had a strong correlation (r = .93); however, JGaG did not have a

Twitter “advocate” so there were no data available to correlate without a top user (Table 12).

The word cloud for JGaG has a few noteworthy findings (Figure 8). It is the only word

cloud including the names all other films released during the same weekend. Not surprisingly, it

also includes the names of the actors in the film (e.g., Natalie Portman, Ewan McGregor, Joel

Edgerton), as well as a few condensed URLs by virtue of the “https” terms. The word cloud also

contains the name of the distributor @MarsFilms. Despite this traditional find, a few recurring

phrase revolves around the late night show Live! with Jimmy Kimmel, terms like: “Kimmel,”

“tonight,” “JimmyKimmel,” and “JimmyKimmelLive” to name a few. This word cloud also

reveals the phrase “janegotagunfilm,” which could have been an unofficial hashtag created by

Twitter users to express interest or support for the film.


When the performance of all four films (i.e., all tweets and revenue) from this weekend

are examined together, they reveal a similar pattern. For this analysis the measurement unit,

week, is considered a standard week (i.e., Monday-Sunday); except for week one, which includes

all data available from Jan. 22 through Jan. 31. A variable was created to summarize the key

relationship between social media (Twitter) and movie revenues. A ratio of box office revenues

(numerator) to social media (all tweets) was standardized, given the vast disparities in raw

scores. All four movies had their highest point of sales on opening weekend, Saturday, more

specifically; and the highest rate of tweets came in on Thursday Jan. 28, the day before the films

were released. But, after this expected burst of online recognition and revenue all films saw a

decline. Yet, most films held a steady pattern after their first week (see Figure 9). Fifty Shades of

Black, on the other hand, started out relatively high had the sharpest decline after opening day

out of all movies that weekend. Although, incredibly different in audience, budget size, tweets,

and revenue, JGaG and KFP share an almost identical pattern. Meanwhile, TFH seems to be the

midpoint between all films for the first week, and then reaches a similar pattern to KFP and

JGaG. Finally, as a way to visualize all the data previously presented the following graph plots

the all daily tweets and gross income from Jan. 22 through Feb. 25 (see Figure 10).

FIN

This study aimed to measure and predict the financial outcomes of films by using their

official hashtag on Twitter. Additionally, this study contributes to the literature by focusing on a

specific release weekend to account for different external variables (e.g., seasons). This study

was based in part on the M3D framework, which postulates that several macro-level variables

(e.g., meme, source, social network, competing social networks, societal factors, and

geotechnical factors) affect the virality of memes (Spitzberg, 2014). The M3D model also


anticipates that sometimes memes create events (etymemic), and other times, events create

memes (evememic). With movies, etymemic influences (i.e., tweets generate buzz and influence

others in a social network to go see a film, or not see the film) and evememic influences (i.e., a

movie premier party or marketing incentive), are expected thus making movies a potential form

of polymemic activity. This study was primarily focused on the relationship between memes and

movie popularity and found support for a few of the macro level influences, while finding a

limitation in one of them. Using tagged tweets from around the world and revenue data from the

U.S. the following results and implications are considered.

First, regardless of the film or genre, all films had significant revenue spikes on every

Saturday during this four-week period. Second, three out of the four films (i.e., TFH, FSoB,

JGaG) made the bulk of their money within the first two weeks. This is could be partially due to

the decrease in theaters each movie had after their second week. Kung Fu Panda 3 is the

exception to this because the number of theaters it was displayed did not drastically decrease. It

also had the added benefit of being a sequel. Both the number of theaters and sequel status are

key factors in a film’s financial success (Kim et al., 2013).

Third, a film’s official hashtag can be effectively used to track a movie’s Twitter

presence. This is a more specific take on Chiang and colleagues’ (2014) notion of using key

words to promote and market a film online. However, for revenue predictions to be strong the

content needs to be non-redundant, which leads to the fourth finding: the amount of original

tweets had a consistently higher correlation to revenue than any other tweet variable. While some

of the literature suggests, “more content is better” so far there has not been a distinction of the

type of content comprising that volume. This research suggests that while a lot of tweets may

have a relationship with revenue, the best predictor is original content (i.e., tweets). Fifth, it


appears that a common practice is for users to include a truncated URL in a tweet in order to

drive followers to a given site, mainly to buy tickets. Whether or not this is an effective way to

increase revenue is outside the scope of this study, but merits further investigation.

For individual movies, some of the speculative findings are as follows. The release date

for KFP3 was moved a few times, to avoid competition with Star Wars: The Force Awakens

(Ford, 2015; McClintock, 2014), which would become a runaway hit in 2015 (McClintock, 2014,

2015). This allowed KPF3 to be in a position to dominate the box office during an otherwise

unexciting time since most major films had already been released and the clamoring for awards

was over.

One possible reason for the moderate correlation between Tweets and revenue for KFP3

is the audience. Since the film is an animated feature, it most likely appeals to younger

audiences. Most of those interested in watching KFP3 are children accompanied by their parents.

This leads to a few plausible speculations. First, young children are unlikely to be tweeting about

their activity or potential interest in the film since Twitter. So, the main audience is potentially

unable to express their interest in seeing the film, and have to rely on someone else to articulate

this interest for them. Second, since most of those interested in KFP3 are children, they are

unable to attend the film on their own. This means children are essentially making parents their

“plus one” for movies, which leads to higher ticket sales and overall revenue.

On the other hand, a film like JGaG did not garner as much attention on Twitter but it

had the highest correlation between tweets and revenue out of all films in this study. Independent

films may appeal to demographics that are highly correspondent in social media such as Twitter.

According to Film Independent (2013) there are about a dozen or so influentials on Twitter that

are fierce advocates of independent films. Independent or art house films, like JGaG, may have a


devoted following who are willing to share their thoughts and support such films. Minor movies

may be benefitted disproportionately more than major movies by social media that activates echo

chambers of like-minded fans and friends. This falls in line with research suggesting niche

products have strong and loyal followings who are particularly vocal about those products

(Dellarocas et al., 2010; Dellarocas et al., 2007). Additionally, Michaelian (2013) suggests that,

if effectively used, social media can be the great equalizer for independent films.

In regard to the M3D, there are at least three implications. First, the findings suggest

original tweets have a much stronger relationship to a film’s revenue, compared to overall tweets

(i.e., tweets and retweets). This suggests a limitation of the M3D framework, given that M3D

considers retweets as a relatively pure form of communicative influence. In the case of these

movies, however, does not appear to directly translate into a financial outcome. Instead, there

seems to be a possibility that the original tweets express an explicit interest, desire, or excitement

to see a movie; whereas a retweet might not. An alternative possibility is that retweets make

substantial difference, but an ambivalent one. If tweets circulating are both enthusiastic and

critical of a movie, these influences may diminish collective interest in a movie, and movies may

need relatively univocal praise and enthusiasm to achieve critical mass for “a hit.” This prospect

suggests that future analyses of tweets may benefit from both standard metrics as well as

sentiment analyses.

Second, the findings support at least two levels of M3D: Geotechnical factors and societal

processes. For this study, the most relevant geotechnical factors were time and number of

theaters. If people have more (spare) time they are much more likely to see a movie. The

findings presented this clearly in two instances, the first one being on each Saturday, and the

second one being on Presidents/Valentine’s day (Feb. 12-15). These were the periods where


films made the most money. The other factor that played a significant role in a film’s success

was number of theaters. The more widely distributed a film is, the more likely it is to make more

money. However, having a film screened in multiple locations also incurs a cost, so most studios

sequentially reduce the number of theaters for a film on a weekly basis. Yet, most films in this

study had the deepest decline in theaters by Feb. 13 (the third week), further suggesting that a

film’s moneymaking period is in the first two weeks. M3D’s inclusion of such factors highlights

the potential “strong effects” bias of many existing media theories, and reveals simple contextual

factors that can work in diverse ways to constrain collective attention to any given topic in social

media. At the same time, all four movies faced the same contextual parameters, and therefore

should reveal somewhat different patterns of social media influence given such contextual

parameters.

Third, the other aspect of M3D having some influence, on both tweets and revenue, were

societal factors. The clearest examples of societal factors for this study are the movies

themselves, since they are not released in a vacuum. Instead, on any given weekend there is an

average of three new films on the marquee. This does not take into account the competition

between previous or future film releases. This is what poses the greatest challenge to a movie.

M3D would label these competing films as counterframes—symbolic resources directly

competing against attention to an existing meme regime or campaign. Another example of a

societal factor that drove a lot of traffic was the sweepstakes campaign. Since these mostly

generated rewteets, which were not a strong indicator of interest or revenue, they appeared to

have little influence over movie revenue outcomes.

Since its inception, the moving image has captivated the collective cultural conscience.

Film in general, and Hollywood’s central role in its evolution and market, are institutions that set


societal trends, stimulate the economy, and provide cultural touchstones. The extent to which

social communication processes make or break such films, and how films co-construct those

communication processes, represent questions that are more empirically accessible than ever

before. The advent of social media and the big data they generate offer a unique window into the

role of film and the movie industry in society. This study represents one of a growing number of

investigations into the degree to which electronic word-of-mouth and social media ‘buzz’ are

directly reflective of movie box office success. The better such models get, the more such

models will reflect the social construction of reality, and the bottom line of theatrical arts

production in our culture.

Acknowledgments

We would like to acknowledge the assistance of Elias Issa in some of the analyses. This material is partially based upon work supported by the National Science Foundation under Grant No. 1416509, IBSS project titled “Spatiotemporal Modeling of Human Dynamics Across

Social Media and Social Networks”. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect

the views of the National Science Foundation.


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Table 1. Major National and International Events From January 22 Through February 25.

Major Events Movies Released

Week 0 (Friday Jan 22 -Thurs Jan 28)

Blizzard in the East Coast The 5th Wave Zika Virus Outbreak The Boy Election Coverage Dirty Grandpa Flint Water Crisis

Week 1

(Friday Jan 29 - Thurs Feb 4)

Kung Fu Panda 3 SAG Awards The Finest Hours Bombing in Damascus Fifty Shades of Black

Jane Got a Gun

Week 2 (Friday Feb 5 - Thurs Feb 11)

Iowa Caucus The Choice Superbowl Sunday Hail, Caesar! Republican Debate Pride and Prejudice and Zombies Democratic Debate


Oregon Standoff Deadpool Death of Justice Scalia How to be Single Pope visits Mexico Zoolander 2 Presidents/Valentine's Day Weekend

BAFTA Awards


Tornado and Storms in MI and LA Race KA mass shootings Risen Fake Marco Rubio Story The Witch Jeb Bush ends Presidential bid

Figure 1. Tweets and Box Office Plotted by Date for Kung Fu Panda 3


Table 2. Tweet Counts by Week for Kung Fu Panda 3

Tweet Count

Gross Tweets

(sansWeek99er*) Gross Tweets

(all) Gross Original

Tweets Gross

Retweets Week 0 6,659 6,889 2,312 4,577 Week 1 18,920 19,575 5,647 13,928 Week 2 10,008 10,077 2,885 7,192 Week 3 3,567 3,567 1,222 2,345 Week 4 2,654 2,654 636 2,018

*Gross Tweets sansWeek99er: The (re)tweets of @Week99er were removed here since they were the highest contributor.

Table 3. Daily Correlations for Kung Fu Panda 3 Tweets and Daily Revenue

Daily Tweet Count Daily Revenue N Gross Tweets (sansWeek99er) .361*** 41,808 Gross Tweets (all) .366*** 42,762 Gross Original Tweets .578*** 12,702 Gross Retweets .287*** 30,060

* p < .05, ** p < .01, *** p < .001

Table 4. Weekly Correlations for Kung Fu Panda 3 Tweets and Daily Revenue (N=363-19,575)

Week Tweet Variable

Gross Tweets (sansWeek99er)

Gross Tweets (all)

Gross Original Tweets

Gross Retweets

Week 1 .368*** .371*** .694*** .305*** Week 2 -.448*** -.445*** .193*** -.458*** Week 3 .459*** .459*** .838*** -.219*** Week 4 .357*** .357*** .337*** .344***

* p < .05, ** p < .01, *** p < .001


Figure 2. Tweet Word Cloud From January 29-31 for Kung Fu Panda 3

Figure 3. Tweets and Box Office Plotted by Date for The Finest Hours


Table 5. Tweet Counts by Week for The Finest Hours

Weekly Tweet Count

Gross Tweets

(sansCaseySherman123*) Gross

Tweets (all) Gross Original

Tweets Gross

Retweets Week 0 7,767 7,830 1,400 6,430 Week 1 12,273 12,463 3,165 9,297 Week 2 1,665 1,818 757 1,061 Week 3 1,110 1,168 496 672 Week 4 983 1,024 628 396 *Gross Tweets sansCaseySherman123: The (re)tweets of @CaseySherman123 were removed here since they were the highest contributor.

Table 6. Daily Correlations for The Finest Hours Tweets and Daily Revenue

Daily Tweet Count Daily Revenue N Gross Tweets (sansCaseySherman123) .704*** 16,031 Gross Tweets (all) .706*** 16,473 Gross Original Tweets .723*** 5,046 Gross Retweets .677*** 11,426

* p < .05, ** p < .01, *** p < .001

Table 7. Weekly Correlations for The Finest Hours Tweets and Daily Revenue (N=396-12,463)

Week Tweet Variable

Gross Tweets

(sansCaseySherman123) Gross Tweets Gross Gross

(all) Original Tweets Retweets Week 1 .591*** .590*** .652*** .525*** Week 2 .455*** .447*** .522*** .392*** Week 3 -.884*** -.886*** -.766*** -.851*** Week 4 0.027 .086* .248*** -.219*

* p < .05, ** p < .01, *** p < .001


Figure 4. Tweet Word Cloud From January 29-31 for The Finest Hours


Figure 5. Tweets and Box Office Plotted by Date for Fifty Shades of Black

Table 8. Tweet Counts by Week for Fifty Shades of Black

Weekly Tweet Count

Gross Tweets (sansMarlonWayans*)

Gross Tweets (all)


Gross Retweets

Week 0 5,825 5,957 999 4,958 Week 1 10,192 10,509 2,271 10,046 Week 2 1,044 1,092 425 1,334 Week 3 516 527 215 624 Week 4 286 294 147 294 *Gross Tweets sansMarlonWayans: (re)tweets of @MarlonWayans were removed since they were the highest contributor.

Table 9. Daily Correlations for Fifty Shades of Black Tweets and Daily Revenue

Daily Tweet Count Daily Revenue N Gross Tweets (sans-MarlonWayans) .918*** 12,038 Gross Tweets (all) .920*** 12,422 Gross Original Tweets .947*** 3,058 Gross Retweets .834*** 12,298

* p < .05, ** p < .01, *** p < .001

Table 10. Weekly Correlations for Fifty Shades of Black Tweets and Daily Revenue (N=147-10,509)

Week Tweet Variable

Gross Tweets (sans-MarlonWayans)

Gross Tweets (all)


Gross Retweets

Week 1 .945*** .948*** .982*** .766*** Week 2 .620*** .651*** .589*** .555*** Week 3 -0.077 -0.041 .722*** -.243*** Week 4 .824*** .844*** .809*** .775***


* p < .05, ** p < .01, *** p < .001

Figure 6. Tweet Word Cloud From January 29-31 for Fifty Shades of Black


Figure 7. Tweets and Box Office Plotted by Date for Jane Got a Gun

Table 11. Tweet Counts by Week for Jane Got a Gun

Weekly Tweet Count

Gross Tweets (sans “top user)

Gross Tweets (all)


Gross Retweets

Week 0 -- 1,307 365 954 Week 1 -- 1,596 715 951 Week 2 -- 185 95 94 Week 3 -- 130 34 96 Week 4 -- 29 15 14

Table 12. Daily Correlations For Jane Got A Gun Tweets And Daily Revenue

Daily Tweet Count Daily Revenue N Gross Tweets (sans “top user”) -- -- Gross Tweets (all) .926*** 1,940 Gross Original Tweets .966*** 859 Gross Retweets .871*** 1,155

* p < .05, ** p < .01, *** p < .001

Table 13. Weekly Correlations For Jane Got A Gun Tweets And Daily Revenue (N= 859-1,940)

Week Tweet Variable

Gross Tweets (sans "top user")

Gross Tweets (all)


Gross Retweets

Week 1 -- .913*** .962*** .846*** Week 2 -- .432*** .495*** .367* Week 3 -- -.218* .780*** -.279* Week 4 -- -.039 -.218 .217

* p < .05, ** p < .01, *** p < .001


Figure 8. Tweet Word Cloud From January 29-31 for Jane Got a Gun


Figure 9. Standardized Ratio Scores (Revenues/Total Tweets) by Week for Each Movie

Figure 10. All Tweets and Gross Revenue From Jan 22 Through Feb 25.

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

Week1 Week2 Week3 Week4

Z-scoresGrossTweetsAll

KFP3 TFH FSoB JGaG

hashtags, tweets, and movie receipts

Documents