# Characterizing the Life Cycle of Online News Stories
Using Social Media Reactions

Carlos Castillo

Mohammed El-Haddad

Jürgen Pfeffer

Matt Stempeck

Qatar Computing Research Institute  Doha, Qatar<EMAIL_ADDRESS>Al Jazeera
Doha, Qatar  mohammed.haddad
@aljazeera.net Carnegie Mellon University  Pittsburgh, USA
<EMAIL_ADDRESS>MIT Center for Civic Media  Cambridge, USA
<EMAIL_ADDRESS>
###### Abstract

This paper presents a study of the life cycle of news articles posted online.
We describe the interplay between website visitation patterns and social media
reactions to news content. We show that we can use this hybrid observation
method to characterize distinct classes of articles. We also find that social
media reactions can help predict future visitation patterns early and
accurately.

We validate our methods using qualitative analysis as well as quantitative
analysis on data from a large international news network, for a set of
articles generating more than 3,000,000 visits and 200,000 social media
reactions. We show that it is possible to model accurately the overall traffic
articles will ultimately receive by observing the first ten to twenty minutes
of social media reactions. Achieving the same prediction accuracy with visits
alone would require to wait for three hours of data. We also describe
significant improvements on the accuracy of the early prediction of shelf-life
for news stories.

###### category:

H.4.m Information Systems Applications Miscellaneous

###### keywords:

Web analytics; predictive web analytics; online news

;

##  1 Introduction

Traditional newspapers have been in decline in recent years in terms of
readership and revenue; in comparison, digital online news have been steadily
increasing according to both
metrics.111http://stateofthemedia.org/2012/overview-4/key-findings/

Recent surveys have shown that about half of the population of the US gets
their news online, and about one third goes online every day for
news.222http://www.people-press.org/2012/09/27/section-2-online-and-digital-
news-2/

The study of patterns of consumption of online news has attracted considerable
attention from the research community for over a decade. This research started
with the analysis of access patterns to websites, and has expanded to include
topics such as new engagement metrics, personalized news recommendations and
summaries, etc. (see Section 2 for an overview).

One line of research looks at consumption and interaction patterns as a single
time series and attempts several prediction tasks on it. For example,
predicting total comments from early comments [18, 28], total visits from
early visits [16], etc. More recent works incorporate attributes from each
specific article (e.g. topic, source, etc.) into the prediction [4].

We adopt a novel approach, in which we integrate different types of
interactions of users with an online news article including visits, social
media reactions, and search/referrals. We evaluate our methods on data from Al
Jazeera English, a large international news network, deeply characterizing
different classes of articles, and predicting their total number of page views
and their effective shelf-life (the effective shelf-life of an article is the
time span during which it receives most of its visits).

The characterization and prediction of user behavior around news articles is
valuable for a news organization, as it allows them (i) to gain a better
understanding of how people consume different types of news online; (ii) to
deliver more relevant and engaging content in a proactive manner; and (iii) to
improve the allocation of resources to developing stories over their life
cycle.

Our contributions. In this paper we present a qualitative and quantitative
analysis of the life cycle of online news stories. Our main contributions are
the following:

  * •

We find that social media reactions can contribute substantially to the
understanding of visitation patterns in online news.

  * •

We characterize two fundamental classes of news stories: breaking news and in-
depth articles, and describe the differences in users’ behavior around them.

  * •

We describe classes of short-term audience response profiles to news articles
in terms of visits and social media reactions (decreasing, steady, increasing,
and rebounding).

  * •

We improve significantly the accuracy and timeliness of predictive models of
total visits and shelf-life of articles, by incorporating social media
reactions.

The remainder of this paper is organized as follows. Section 2 provides an
overview of previous works related to ours. Section 3 introduces our data
collection and defines the concepts and variables we use. The main results of
our paper are presented as descriptive and predictive analysis in the two
following sections: Section 4 describes user behavior with respect to
different classes of articles, and Section 5 demonstrates the importance of
incorporating social media information into the predictive modeling of visits.
The last section concludes the paper.

##  2 Related work

One of the earliest published studies of user behavior in online news was
conducted by Aikat aikat_1998_news, who studied the web sites of two large
newspapers from November 1995 to May 1997. This work describes many of the
patterns still seen in news sites today: visits occur mostly during weekdays
and working hours; readers “skim” pages for information so dwell times tend to
be short, and there are clear traffic “bursts” that can be attributed to
specific news developments.

With the advent in recent years of what can be considered as new forms of
journalism (blogs) and new propagation mechanisms for news (micro-blogs and
online social networking sites), the volume of research publications in this
area has increased considerably. In this section we overview a few previous
works closely related to ours, but our coverage is by no means complete.

Behavioral-driven article classification. Previous works including [8, 19]
that have studied online activities around online resources (e.g. visiting,
voting, sharing, etc.), have consistently identified broad classes of temporal
patterns. These classes can be generally characterized, first, by the presence
or absence of a clear “peak” of activity; and second, by the amount of
activity before and after the peak.

Crane and Sornette crane_2008_response describe classes of visitation patterns
to online videos, and present models that are consistent with propagation
phenomena in social networks. Lehmann et al. lehmann_2012_dynamical extend
these classes by observing that for Twitter “hashtags” (user-defined topics)
the distributions of activity in different periods (before/during/after)
induce distinct clusters of activity that can be interpreted considering the
semantics of each hashtag. Romero et al. romero_2011_differences describe how
manually-assigned classes of hashtags are related to different shapes of the
exposure curve: the probability that a user will propagate some information
(“retweet” in the case of Twitter) after being exposed to the information by a
certain number of her neighbors.

Yang and Leskovec yang_2011_patterns describe six classes of temporal shapes
of attention. Attention is measured in terms of the number of appearances of a
given phrase (of a variation of it) corresponding to an event. The patterns
describe the distribution of attention over time, as well as the ordering in
which different types of media (professional blogs, news agencies, etc.)
“break” the story.

In general, previous works have established that the evolution of the
popularity of different on-line items depends on their class. Figueiredo et
al. figueiredo_2011_tube describe how YouTube videos that are posted to a
“top” page on the website, and videos that are making use of professionally
produced content, are different from randomly-chosen videos in terms of their
visit patterns.

Recently, researchers at URL shortening service Bit.ly [6] described how an
article’s half-life (see definition in Section 3) is affected by topics,
extending a previous observation than in general there are some topics that
are more time-sensitive than others [12]. For instance, business-related
articles have on average a longer half-life, while articles related to
politics/celebrities/entertainment have an intermediate one. Sports-related
articles have in comparison a shorter half-life. Previously, Bit.ly
researchers [5] have shown that this half-life is also affected by the social
media platform where the link is first posted (e.g. links on Facebook were
longer-lived than links on Twitter).

Table 1: Selected references on predictive modeling of user behavior, sorted by publication year. Reference |  Collection |  Input / Output
---|---|---
Tatar et al. tatar_2001_predicting |  20
Minutes |  input: publication hour, number of comments after a short time, section; output: total number of comments
Brody, Harnad, and Carr brody_2006_citations |  arXiv pre-prints |  input: short-term article downloads; output: long-term article citations
Lee, Moon, and Salamatian lee_2010_popularity |  DPReviews / Myspace |  input: time to first-comment, inter comment arrival stats; output: time to last comment
Lerman and Hogg lerman_2010_news |  Digg |  input: visits; output: parameters of models that consider examination and promotion patterns
Kim, Kim, and Cho kim_2011_temperature |  Blogs |  input: clicks on first 30 minutes; output: clicks until end of lifetime
Yu, Chen, and Kwok yu_2011_predicting |  Facebook pages |  input: content and media type; output: number of FB likes/shares of each post
Lakkaraju and Ajmera lakkaraju_2011_attention |  Facebook pages |  input: text- and other characteristics of the posting and the page; output: number of FB likes/shares of each post
Szabo and Huberman szabo2012predicting |  YouTube and Digg |  input: views (Y), votes (D) in first 10d (Y), 2h (D); output: total number of views/votes
Bandari, Asur, and Huberman bandari2012pulse |  News
aggregator |  input: text analysis incl. topics, named entities, subjectivity, etc., source popularity; output: tweet count
Ruan et al. ruan_2012_prediction |  Tweets |  input: topics, past tweets, content features, user features, etc.; output: tweet count for a given topic
Pinto, Almeida, and Gonçalves pinto_2013_predicting |  YouTube |  input: time series of views in first 7 days; output: number of views after 30 days
Ahmed et al. ahmed_2013_predicting |  YouTube, Digg, Vimeo |  input: views (Y, V) and votes (D) over time; output: predict future popularity

We deepen and complement previous works on behavioral-driven characterization
of online content, by describing the life-cycle of online news articles
considering their visitation patterns as well as their social media reactions.

Prediction of users’ activity. The prediction of the volume of user activities
with respect to on-line content items has attracted a considerable amount of
research. This is attested by a number of papers, some of which are outlined
in Table 1. Another active topic that is closely related, but different, is
that of predicting real-world variables such as sales or profits using social
media signals (e.g. [13] and many others).

Over the years, the models used to predict user behavior in social media have
increased in complexity. For instance, Bandari et al. bandari2012pulse and
Ruan et al. ruan_2012_prediction incorporate into their models features
extracted from the content of the articles, such as topics. Yin et al.
yin_2012_straw study voting behavior over on-line contents and describe a
model that considers that users are divided into two populations: a group that
follows the majority opinion, and a group that does not. Myers et al.
myers_2012_external study models that describe user activity in terms of
information propagations, including the presence of external influences, e.g.
traditional media sources that can reach vast audiences, such as television
networks. Huang et al. huang_2012_predicting consider an online model of
social activities that evolves over time as more information becomes
available.

In contrast with previous works, we focus on the dynamic relation between
social media reactions and visits over time, and show that both are useful to
understand the differences among classes of articles and to predict future
visit patterns.

Analysis of news visits and social media responses. Dezso et al.
dezso_2006_dynamics analyze the visits to a large news portal in Hungary. One
aspect they study which is closely related to our work is the half-life of
articles, which is shown to be distributed according to a power-law across a
broad range, with a mean of 36 hours. Agarwal et al. agarwal_2012_multi study
the actions users perform after reading an article, which include printing,
commenting, rating, and sharing through e-mail or social media. Their focus is
on performing personalized recommendations, but they also uncover that article
topics have an effect on the probability of each action, with a division
between articles users read privately and articles they share publicly: “Users
tend to share articles that earn them social prestige and credit but they do
not mind clicking and reading some salacious news occasionally in private.”

Social media reactions to traditional news media can vary not only in volume
but also qualitatively. Hu et al. hu_2011_event record tweets during the
broadcast of a speech of the US President. They observe that many tweets refer
to the speech in general, except for certain topics which are discussed in
more detail.

Finally, social media optimization company SocialFlow describes in a
whitepaper [22] a comparative study of social media responses to several large
media outlets: Al Jazeera, BBC News, CNN, The Economist, Fox News and The New
York Times. Among other findings, they note that the probability that a user
clicks on a tweet is higher for The Economist ($\approx 19\%$) than for Fox
News ($\approx 16\%$), Al Jazeera ($\approx 11\%$) or The New York Times
($\approx 4\%$). However, followers of Al Jazeera are almost twice as likely
to retweet article links than followers of the other channels.

In contrast with previous works, we consider jointly traffic to the website
and social media reactions, as both constitute acts in which users engage with
the news content. Additionally, we quantify the richness of Twitter messages
over time measuring entropy and counting unique tweets, and show that these
variables are key to more accurate predictions of future visits.

##  3 Context and dataset

In this section we provide some context to our research and describe the
dataset that will be used on the remainder of the paper.

###  3.1 Traditional news and social media

Our dataset is provided by Al Jazeera English,333http://www.aljazeera.com/ a
well-established news organization that reaches hundreds of millions of
viewers through its TV channel. Their website is divided into five major
sections: News, In-Depth, Programmes, Sports, and Weather – plus a collection
of blogs, which is outside the scope of this study. Approximately 40
editors/producers work on the areas of News, In-Depth, and Programmes.

The editors of Al Jazeera English maintain Facebook and Twitter444Currently
Facebook and Twitter are the two most frequent sources of social media
referrals to Al Jazeera. Reddit appears in a third place but only through few
articles having extremely high visibility. accounts (we call them “corporate
accounts” in the rest of the paper) and use them actively to announce their
content. This seems to be a standard practice adopted by all major media
organizations in recent years. Each account (facebook.com/aljazeera and
@AJEnglish) has over 1.5 million followers as of May 2013. Using these
accounts, articles in the News section are shared immediately after being
posted online. Articles on the In-Depth and Programmes sections are shared
throughout the day with the goal of maximizing audience reach across multiple
time zones.

The corporate social media accounts re-share articles at different times of
the day, sometimes up to 4 times, on a schedule determined by editors’
judgment and designed to increase user engagement. Close attention is paid to
the wording of the items posted in social media, including aspects such as
their length and the use of hashtags in the case of Twitter. Editors use a
variety of online tools to obtain low-latency analytics of traffic and social
media, and to decide which hashtags and keywords to use in their postings.

More than half of the visitors to the Al Jazeera English website are from the
USA, the United Kingdom, or Canada. According to an online survey taken by Al
Jazeera English in 2011 ($n=4,500$), 18% of respondents said they used
Twitter, 42% Facebook, and 12% both.

Social media interactions and traffic to the website can complement or
substitute each other. Most frequently, they complement each other: people
click on the shared content and visit the website. Sometimes, the social media
share can be a substitute for a visit to the article, such as when a video can
be viewed directly on the social media site, or when the social media content
itself delivers enough information to satisfy users without requiring them to
click through to the full article.

For instance, the news “Pakistan’s Malala now able to stand in UK” (19 Oct
2012) generated an unusually large number of shares on Facebook, but
comparatively little traffic on the website. At the time, the student-activist
was being treated from nearly-fatal wounds received ten days before, and it is
likely that users who were following the story just wanted to express their
relief or satisfaction at her recovery.

In summary, for Al Jazeera and for most large news organizations, social media
is important both because it attracts more visitors to their website than any
other external referrer, as well as because it provides more platforms in
which to have an audience. Hence, many news organizations adopt an active role
in social media in order to increase this positive effect.

Figure 1: Overall visits to articles. Our study considers all articles posted
from Oct. 8th to Oct 29th, 2012 (the graph extends until Nov. 6th).

###  3.2 Data collection

We focus on a period of three weeks between October 8th, 2012, and October
29th, 2012. The choice of this period is not random: it was a relatively
stable period of traffic, only exhibiting a relatively minor peak on October
29th due to Hurricane Sandy. Figure 1 depicts the frequency of visits to all
the articles in our dataset during the observation period.

The data collection is done via a “beacon” embedded in all article pages; this
produces events that are processed using Apache
S4,555http://incubator.apache.org/s4/ a high-performance system for online
processing, which is used to collect and aggregate the visits with a 1-minute
granularity. For efficiency reasons, only articles obtaining at least 5 visits
in a 10-hour window are monitored. The collected data is stored using a
Cassandra666http://cassandra.apache.org/ NoSQL database.

Our system also collects messages from Facebook (using the Facebook Query
Language API) and Twitter (using their Search API). Both platforms have strict
limitations on polling frequencies, which impose a trade-off between the
number of articles we can monitor and the frequency with which we monitor
them. To obtain more accurate results for popular articles, and after
experimenting with different settings, we decided to poll social media
reactions for articles that are within the list of the 30 most visited
articles during each five-minute data collection window. We remark that this
list varies considerably over time.

We selected a uniform random sample of articles whose first visit was recorded
during the observation period, and kept only those accumulating at least 100
visits during their first week after publication. A total of 606 articles was
included; this covers over 3.6 million visits and at least 235,000 social
media reactions. Table 2 presents some summary statistics on this dataset.

Table 2: Summary statistics of our dataset. | Total | Article avg.
---|---|---
Number of articles | 606 | -
Visits after 1 hour | 260 K | 430
Visits after 1 day | 2.5 M | 4,273
Visits after 7 days | 3.6 M | 5,971
Facebook shares | 155 K | 256
Tweets | 80 K | 133
Tweet entropy |  | 5.6 bits
Fraction of unique tweets |  | 19.9 %
Fraction of corporate retweets |  | 36.8 %

###  3.3 Metrics

For each article we collected a number of metrics regarding user visits and
social media reactions. First, we observed at a granularity of one minute the
number of visits (page views) to each article, and the URL of the previous
page seen by the users before reaching an article (referral). We bucketed the
latter into four classes:

  * •

internal links, mostly from the home page of the website: these are the
majority of the traffic sources and comprise 70% of the visits;

  * •

external links from other sources including social media sites, news
aggregators, and others: 14%;

  * •

direct links, which have an empty referral and correspond
mostly777http://www.theatlantic.com/technology/archive/2012/10/dark-social-we-
have-the-whole-history-of-the-web-wrong/263523/ to people sharing news through
instant messaging, e-mail, or other non-web application: 11%; and

  * •

search referrals, basically links from organic search results: 5%.

We remark that this distribution of referrals corresponds to the articles in
our sample, which do not include the homepage of the website, section index
pages, or older articles. If we take those into account, the numbers are
different, e.g. the search referrals account for 30% of the visits.

We also collected periodically the number of times an article has been shared
on Facebook, and the content of any Twitter message containing the URL of the
article, or a variant of the URL produced by a URL shortening service. We used
this data to compute the following variables:

  * •

Number of Facebook shares per minute (interpolated).

  * •

Number of tweets per minute.

  * •

Number of unique tweets per minute. A tweet is deemed unique if its edit
distance with all previous tweets pointing to the same article (after
discarding shortened URLs and “retweet” prefixes) is more than 10 characters.

  * •

Tweet vocabulary entropy. To compute this, at any given point in time we
create a document by concatenating all the tweets received up to that time.
Then, we compute the entropy of the distribution of terms in that document.

  * •

Number of corporate retweets per minute. A tweet is a “corporate retweet” if
it includes “RT @AJEnglish” or “RT @AJELive” in its text. A tweet can be both
corporate retweet and unique, as users are free to edit the retweet before
posting it.

  * •

Number of followers, friends (followees) and statuses of each of the users
posting a tweet.

Table 3: Top 10 most frequent words (stemmed and lowercased) in article titles in the “News” and “In-Depth” sections. Words that appear in both lists are italicized. Top words (News, $n=322$) |  Top words (In-Depth, $n=139$)
---|---
Word | News | In-Depth | Word | News | In-Depth
us | 34 | 17 | us | 34 | 17
kill | 21 | 1 | pictur | 0 | 10
attack | 19 | 0 | obama | 6 | 6
syria | 15 | 4 | interact | 0 | 6
dead | 15 | 1 | america | 0 | 6
protest | 13 | 0 | muslim | 0 | 5
rebel | 12 | 2 | syrian | 11 | 4
vote | 11 | 3 | syria | 15 | 4
syrian | 11 | 4 | presid | 4 | 4
pakistan | 10 | 2 | polici | 2 | 4

##  4 Behavioral-Driven Classes

In this section we describe classes of articles according to patterns of user
behavior.

###  4.1 News vs In-Depth

We observe that articles in the two larger sections of the Al Jazeera English
website trigger distinct user behavior patterns: visits and social media
reactions on articles in the News section (322 articles in our sample) are
different from the ones on articles in the In-Depth section (139 articles).

Titles. Table 3 includes the most frequent words in titles of articles in
these two sections, after converting to lowercase and applying Porter’s
stemmer.888http://snowball.tartarus.org/algorithms/porter/stemmer.html While
in our sample the US and Syria appear prominently in both sections, articles
in the News section include several violent acts, while articles in the In-
Depth section are dominated by photos and political analysis. A chi-squared
test comparing the entire distributions shows $p<10^{-13}$, rejecting the
hypothesis that they are equal.

Figure 2: Visits per minute (left y-axis) as well as Tweets and Facebook
shares per minute (right y-axis) for the first 12 hours. For visits, the
shaded area covers 50% of the data (quantiles 0.25 to 0.75). Top: average for
a News item. Bottom: average for an In-Depth item.

Visits. Figure 2 (top) depicts the average time series of some variables for
articles in the News section. Time is expressed in hours-equivalent, which are
hours corrected by the seasonality (day-night, weekday-weekend) of traffic on
the website, as in [27]. Initially there are a number of visits and activity
on Twitter and Facebook, that decays rapidly after a short time. This is often
the pattern in news media as observed e.g. by [9, 21] and others. After a few
hours, a large amount of visits can be explained by “internal traffic”, i.e.
visitors arriving from the homepage of the site. For most articles, once the
news article is displaced from the homepage by more recent items, its traffic
slows down considerably.

The profile of visits to In-Depth articles can be more complex. Figure 2
(bottom) depicts the average series for these articles. We can observe that a
sustained level of visits is observed during several hours, as the contents of
these articles are not as time-sensitive as those of the News section. We
remark that in both cases (News and In-Depth) there is considerable
variability from one article to another.

Figure 3: Visits in the first hour versus visits on the first week for
articles in the two largest categories. A simple function that assumes that
visits after seven days are a multiple of visits after one hour has been
included, by performing a least-squares fit in the central portion of each
distribution. Figure 4: Differences in the distribution of Facebook vs Twitter
shares. On average the ratio of Facebook shares to tweets is 1.9:1 (1.6:1 for
News, 2.7:1 for In-Depth). The result of a least-squares fit in the central
portion of each distribution is included. Figure 5: Differences in the
distribution of the fraction of unique tweets. In both cases, Twitter activity
is dominated by re-tweets or repetitions of the same tweets, but In-Depth
articles attract more unique tweets. Figure 6: Differences in the distribution
of fraction of corporate retweets. In-Depth articles have a larger share of
re-tweets from the @AJEnglish and @AJELive accounts.

News items compared to In-Depth items have a more intense first hour, as can
be seen in Figure 3. For News, visits in one hour are roughly $1/12$th of the
visits in the first week, while for In-Depth they are on average around
$1/29$th. The two groups are similar to “promoted” (homepage) and “not-
promoted” stories in Digg as observed in [27].

This difference in behavior can to some extent be explained by the design of
the website. News articles are displayed more prominently on the home page,
with the most salient location being typically used by a news item; however,
In-Depth articles are also visible across the website, including a prominent
slot on the top right corner of every page. Additionally to the differences in
social media sharing that we discuss next, we observe that long-lived News
articles (in terms of effective shelf-life as defined in Section 5.2) tend to
include analysis that would actually make them fit for the In-Depth section.
Indeed, the top-3 longer lived News articles in our observation period are
“Profile: Malala Yousafzai” (Oct 10th, 2012), “Syrian rebels in uneasy
alliances” (Oct 25th, 2012), and “Malala is the daughter of Pakistan” (Oct
13th, 2012); their contents, while motivated by specific news events such as
the Syrian conflict and the shooting of a school girl, do not describe the
events but rather the context in which they are taking place.

Social media. On average the ratio of Facebook shares to tweets per article is
1.9:1, which is to some extent consistent with the survey described in Section
3.1 that indicated that there were twice as many website visitors using
Facebook as there were Twitter users. Additionally, In-Depth articles are
shared more on Facebook given the same level of activity on Twitter, as shown
in Figure 4. On average News articles have 1.6 Facebook shares per tweet,
while In-Depth articles have 2.7.

As shown in Figure 5 there is also a difference in the number of unique
tweets. On average, 17% of the tweets about News articles are unique, versus
25% of the tweets about In-Depth articles. This means that a majority of users
do not change the content of the tweets when clicking on the “tweet” button
next to the articles, or when retweeting from another Twitter user.

There is also a difference in the number of corporate retweets, as shown in
Figure 6. On average, 27% of tweets about News articles are corporate
retweets, compared to 44% of tweets about In-Depth articles. This means that
for In-Depth articles a larger share of Twitter activity can be attributed to
users who are followers of @AJEnglish or @AJELive, and thus are probably more
engaged with these Twitter accounts.

Anecdotally, we know that editors spend more time crafting tweets to promote
In-Depth articles than News articles, given that the former are not as time
sensitive as the latter. In the case of News, the headline is often posted
without modifications to Twitter, which may produce a comparatively less
appealing tweet.

###  4.2 Analysis of news articles

We observe that News articles have attention profiles that are quite
predictable, while In-Depth and other article categories show significantly
more variability. We focus on the first 12 hours-equivalent after publication
of each article in the News section, and observe the time series of data from
all sources including internal links, external links, search engines, and
social media (similarly to the time series shown in Figure 2, but for each
individual article instead of as an average). We then classify articles into
several classes based on visit patterns that are apparent from these
observations, starting with the largest class (“decreasing”) and following
with the other classes. The classification is done by the authors seeking
consensus and discussing borderline cases.

At a high level, the classes of articles in our News sample can be roughly
described by an “80:10:10 rule”. The traffic to $\sim$80% of the articles
decreases monotonically during the first 12 hours, the traffic to $\sim$10%
does not decrease, and the traffic to the remaining $\sim$10% decreases first,
but then rebounds. Articles on each are listed in Appendix A.

Next we provide a brief description of each class and examples of stories
appearing in each of them. We remark that in this work we do not attempt to
provide a comprehensive content-based typology for news articles within each
attention profile.

Decreasing (78%). The largest article class represents about 78% of the sample
set. Articles in this class demonstrate an initial spike in visits following
article publication, followed by a rather consistent drop in the number of
visits, either immediately (244 articles), or after a short delay (7
articles).

Delayed onset traffic decreases have been observed before, such as in [21]
with respect to the shooting in Aurora, Colorado, in 2012. This attention
pattern can often be attributed to breaking news that resonates with readers
located in a time zone that is off-peak when the article is first posted, such
as when that portion of the audience is mostly asleep. A story about Hurricane
Sandy’s movement up the East Coast of the United States, for example, sees an
initially sharp visit growth that begins to decline as the East Coast retires
for the evening.

The predominance of this class of article indicates that while news itself
occurs, and can even be covered, at a constant rate, in most cases readers
will only be interested on a news article for a brief period of time after its
publication.

Steady or Increasing (12%) Roughly 9% of the sample’s articles retain
relatively constant visitor rates during their first 12 hours. Compared to
news categories with very short shelf-lives, such as sports news, these
articles are remarkably consistent. In this subset of news articles, dramatic
news and emotional stories appear to garner Facebook shares and, often as a
result, extended shelf-lives.

In the U.S., multiple articles on Obama and Romney’s sharp-tongued
presidential debate drive consistent Facebook and Twitter responses for a
relatively long period of time following the articles’ publication. A poll on
racism in the US has similar staying power and Facebook sharing. In Central
Asia, the Taliban attack on Pakistani schoolgirl Malala appears in a number of
these articles, where consistent Facebook sharing buoys the article traffic
beyond average shelf-life. In Europe, furor over a seismology scandal is
posted to Facebook, while in the Middle East, atrocities in the war in Syria
and violence between Israel and Hamas also generate hours of steady traffic.
Africa sees a new prime minister in Libya, the police shooting of 34 striking
miners in South Africa, and a bomb attack on a church in Nigeria, all of which
see sustained traffic thanks in part to significant Facebook and Twitter
sharing many hours after their initial publication.

Stories in this group were mostly developing stories and many of them had
regular updates. One such example is the story about Malala for which Al
Jazeera sent a correspondent to the Swat Valley. Being a complex region to
cover, a series of news articles and feature stories were written. In
addition, Al Jazeera reached out to the Reddit community for a Q&A session
which topped the “Ask me anything (AmA)”
section999http://www.reddit.com/r/IAmA/comments/11p6q3/i_am_asad_hashim_journalist_for_the_al_jazeera/.
section.

A relatively small number of articles (3% of our sample) buck the usual trend
and see increased page traffic as time passes after their publication, rather
than a decline. To the extent that these articles can be generalized, they
resemble the class of articles detailed above. Some of these articles were
also updated with supporting content. For at least half of them, web producers
added video packages after publication, which may explain to some extent the
increase in visits.

Rebounding (10%). About 10% of the articles in our sample initially exhibit a
decline in visits/minute, until a point where such decline is reversed. This
“rebound” occurs either because of internal or external links.

In the case of internal traffic, the traffic patterns behind these rebounding
articles sometimes reflect the common newsroom practice of linking to previous
coverage in more recent articles. This practice provides additional background
context to readers just arriving at the story, but also helps news
organizations extract additional value from articles that are otherwise
statistically becoming valueless. Stories that required a significant
investment of resources to produce are also promoted more heavily than regular
articles. We can see that in these cases, these internal links do indeed
deliver readers to articles whose shelf-lives have nearly expired, when
measured by homepage and social media traffic.

The articles that rebound as a result of external traffic are beneficiaries of
attention directed from outside of the news organization (e.g. a social
networking site, the website of another news network, etc.). Typically each
observed burst in external web traffic can be tracked to a single source.
Breaking stories can also gain visits as ongoing developments drive
significant additional interest. This phenomenon is evidenced, for instance,
by three rebounding articles tracking Hurricane Sandy’s descent upon the
United States.

In general, we see that when News articles cover topics that stray from “hard
news”, the article’s attention profile reflects the increased variability seen
in the In-Depth pieces. For example, some articles ostensibly cover specific
actualities, but also bridge into long-standing issues: in the U.S.,
“Immigrant family in pursuit of the American Dream” and “Living the modern
American Dream” stoke passions around immigration.

The sometimes blurry line between reporting on immediate actualities and
longer-term trends like immigration is an area of tension in journalism, one
identified by Galtung and Ruge when they asked “how do ’events’ become
’news”’? [11].

##  5 Improving Traffic Predictions
Using Social Media Data

An increased amount of social media reactions is often correlated with more
traffic to online articles. This is particularly marked in the case of non-
decreasing and rebounding News articles, as well as In-Depth articles whose
visitation patterns are more varied and less predictable than regular
(decreasing) News articles. In this section, we combine social media reactions
with early visitation measures to provide improved predictions of (i) the
volume of visits to an article after 7 days from its publication and (ii) the
effective shelf-life of articles, i.e. the time during which they will receive
most of their visits. We begin by fitting models to our sample data, and then
explore the practicality of this approach for new data.

###  5.1 Modeling visiting volume

Our first goal is to determine to what extent social media reactions can
improve the prediction of the overall popularity (total number of visits) of
an article. The dependent variable that we want to describe with our models is
the total number of visits after 7 days ($v7d$). We use a straightforward
approach to answer this question—linear regression models. We include the
following variables (described in Section 3.3) as observed at the time at
which the prediction is performed: number of visits ($v$), number of visits
from link referrals ($vr$) and from “direct” traffic from e-mail/IM ($vd$),
shares on Facebook ($f$), Twitter ($t$), mean number of followers of people
sharing on Twitter (foll), entropy of tweets ($ent$), number and fraction of
unique tweets ($uni$, $unip$) and fraction of corporate retweets ($cp$). We
use a linear regression model that includes all first-order effects as well as
second order interactions. We included second-order interactions because of
the interdependency of the variables (e.g. an article with more visits is more
likely to have more social media reactions):

$lm(vis7d\sim v)$

$lm(vis7d\sim(v+vr+vd+f+t+\textrm{\em{foll}}+ent+uni+unip+cp)^{2})$.

Figure 7: Proportion of explained variance ($r^{2}$) for the prediction of total volume of visits, for News and In-depth articles. Table 4: Modeling visiting volume after 7 days: Significance levels for regression models after 20 minutes. Variable | In-depth | News
---|---|---
Facebook shares | 0.0349 | * | 0.0204 | *
Twitter tweets | 0.0026 | ** |  $<$0.0001 | ***
Twitter entropy |  $<$0.0001 | *** | 0.0003 | ***
Twitter avg. followers |  $<$0.0001 | *** |  |
Volume of unique tweets |  |  |  $<$0.0001 | ***
Unique tweets % |  |  |  $<$0.0001 | ***
Corporate retweets % | 0.0092 | ** |  |

The distribution of visits to articles is log-normal distributed in our data,
consistently with previous works [29, 4]. We log-transform ${log(x+1)}$ the
visits as well as the volume of social media reactions. For t=5, 10, 15, …we
calculate the proportion of the explained variance of these two linear models.
The result is shown in Figure 7.

It takes about 3 hours to be able to explain $>0.6$ of the variance for In-
Depth articles, and the additional variables are profitable from the first
minutes. After 10-20 minutes we observe the largest difference in our
regression models (+0.5 in terms of $r^{2}$).

We take a closer look at the model variables after 20 minutes to identify the
sources of this improvement. For this purpose we stepwise fit the model
variables by AIC (Akaike information criterion) as implemented in stats.step
in R. Table 4 shows the reliability of the Social media variables to serve as
good predictor for the volume of visits after 7 days.

The fraction of traffic from different sources does not appear to be a
reliable predictor when all variables are used for the model; when we reduce
the model to exclusively these two variables, the traffic from e-mail/IM is a
more reliable predictor than the traffic from external links.

Social media variables, particularly the number of tweets and the entropy of
the vocabulary used in them, seem to be reliable predictors for both In-Depth
and News articles. The number of followers of people posting an article on
Twitter together with the fraction of corporate retweets seem to be
particularly important for In-Depth articles. A possible interpretation is
that the response to these articles has a larger component driven by
influential accounts and the actions of Al Jazeera editors. In contrast, the
number and fraction of unique tweets can be used for the prediction of traffic
to News articles. Consequently, a rich online discussion around a breaking
within its first minutes is a signal of potentially high and sustained user
interest.

Figure 8: Distribution of effective shelf-life. Table 5: Modeling effective shelf-life: Significance levels for regression models after 20 minutes. Variable | In-depth | News
---|---|---
Visits $R^{2}$ | 0.0005 |  | 0.0921 |
Social media $R^{2}$ | 0.4457 |  | 0.2193 |
Social media $R^{2}$ adjusted | 0.2274 |  | 0.1505 |
Twitter tweets | 0.0138 | * | 0.0061 | **
Twitter entropy | 0.0027 | ** | 0.0024 | **
Twitter avg. followers |  |  | 0.0001 | ***
Volume of unique tweets | 0.0026 | ** |  |
Unique tweets % | 0.0190 | * | 0.0445 | *
Corporate retweets | 0.0001 | *** |  |
Traffic from e-mail/IM | 0.0482 | * |  |

###  5.2 Modeling shelf-life

We define the effective shelf-life $\tau_{\ell}$ of an article as the time
passed between its first visit and the time at which it has received a
fraction $\ell$ of the visits it will ever receive. In this work we set
$\ell=0.90$, but similar values (e.g. $0.85$, or $0.95$) yield similar results
to the ones presented here. When $\ell=0.50$ this is equivalent to half-life
[5, 6].

Given that our observation period is finite, we use a seven-day observation
period as a proxy for the total number of visits the articles will ever
receive, as for basically all the articles in our sample, there is little
activity after 3 or 4 days. This is consistent with the experience of Al
Jazeera editors and with observations in previous works (e.g. [30]). We remark
however that there are rare cases where an article is “re-born” after weeks,
for instance when it provides background information for a new development.

The distribution of the shelf-life for both classes is depicted in Figure 8.
As observed in the qualitative analysis, the average shelf-life of In-Depth
articles, 2 days and 9 hours, is longer than the one of News articles, 1 day
and 16 hours. Their average half-lives are respectively 20 hours and 8 hours
(both are shorter than the 36 hours observed by [9]).

We observe that the effective shelf-life of all articles is independent from
their total number of visits after 7 days (Pearson’s correlation $r=-0.03$).
This will lead to low accuracy when predicting based solely on visits. For the
predictive task the linear regression model setup is analogous to the one used
to model visiting volume; this time the dependent variable is $\tau_{90}$. Our
focus is again on the variables after 20 minutes. Running the first regression
model (only visits) for this time period reveals differences for News and In-
Depth stories (Table 5). For News stories, at least 9.2% of effective shelf-
life variance can be described, while visits show no predictive information
for In-Depth stories. Including social media variables changes this picture
dramatically. Especially for In-Depth stories, a significant part of the
variance can now be described. Stepwise fitting of the social media models
shows that the number of Facebook shares and the traffic from external links
are no reliable predictors for effective shelf-life. In contrast, all Tweet
variables reach significant levels. For In-Depth articles corporate retweets
and traffic from e-mail/IM also serve as reliable predictors.

In a nutshell, using social media variables to model effective shelf-life of
stories can increase the accuracy of early prediction significantly. This is a
very promising result for future research given that we describe the effective
shelf-life pattern with data from one single time point without the use of
time series or other elaborate models.

###  5.3 Online predictions

Figure 9: Screenshot of http://fast.qcri.org/ depicting predictions for four
articles. Green bars indicate number of pageviews so far, gray bars indicate
predictions. Exact numbers are business-sensitive so they are omitted.

A live system implementing these ideas on data from the Al Jazeera English
website is available online.101010http://fast.qcri.org/ This allows us to
further test the effectiveness of our methods in an online setting, in
addition to the off-line tests we have described so far. Figure 9 shows a
screenshot of this application.

The system collects data for all articles irrespective of their section, and
produces predictions for all articles in the News section using one set of
models, and for all the remaining articles (In-Depth, Videos, Programmes,
etc.) using another set. In each model set there are models that are executed
1 hour, 6 hours, 12 hours and 24 hours after an article is published. The
target variable in this live system is page-views after 3 days. Every 24
hours, it re-trains the models by adding the articles that have passed the
3-day deadline to the training set. After an initial warm up period of 3
weeks, we monitored all 350 article URLs published during a period of 1 week
in July 2013 and kept all the predictions done by the system.

First, we evaluated the coverage of our system, which as explained in Section
3.2 is designed to focus on the top 30 most visited URLs in every 5-minute
period. In practice, we produce predictions within 6 hours for 194 (55%) of
the articles seen. Taking as comparison Google Analytics, which is also used
by this website,111111http://analytics.google.com/ we observe that this covers
65% of the page-views to Al Jazeera English articles. We remark that this
partial coverage is not an intrinsic aspect of the system, but a limitation of
using public (instead of paid) access to Twitter’s API.

Second, we evaluated the quality of the predictions. In order to do so, we
store the predictions done by the different models. Conceptually, each of the
350 articles is in the testing set, and the training set is composed of all
the articles published in the period of 1 to 4 weeks before its publication
day. Figure 10 compares the actual number of page-views with predictions done
1 hour and 6 hours after an article is published. The quality of the
predictions after one hour is similar to off-line tests ($r^{2}=0.72$) even
when we are mixing articles from other sections; 134 articles (38%) not in
News or In-Depth – but we also remark the predictive horizon of the live
system is shorter (3 days vs 7 days). Predictions after 6 hours have
$r^{2}=0.85$. The location of the best trade-off between timeliness and
accuracy in the range of 1 to 6 hours is an important problem, which requires
understanding how editors react to the predictions and use them in practice.

Figure 10: Predictions of visits after 3 days using the online system across
all articles. Left: predictions 1 hour after publication. Right: predictions 6
hours after publication.

##  6 Conclusions

Main findings. By adopting an integrated view of users’ behavior, we have
observed that there are two classes of articles that generate qualitatively
and quantitatively different responses from readers. News articles describing
breaking news events tend to decay in attention shortly after they are
published and thus have a shorter shelf-life. These articles also have more
repetitive social media reactions, as most users simply repeat the news
headlines without commenting on them. In-Depth items portraying or analyzing a
topic tend to exhibit a longer shelf-life and a richer social media response,
including more content-rich tweets in terms of vocabulary entropy and fraction
of unique tweets, and more shares on Facebook for the same level of tweets.

By going deeper into the first few hours after publication of News articles,
we found three distinctive response patterns in a roughly 80:10:10 proportion:
decreasing traffic, steady or increasing traffic, and rebounding traffic. We
found that there can be multiple causes for non-decreasing traffic, including
the addition of new content to articles, social media reactions, and other
types of referrals.

We have shown that social media signals can improve by a large margin the
accuracy of predictions of future visits, as well as the accuracy of
predictions of article shelf-life. In particular for In-Depth articles which
exhibit more complex visit patterns over time, we have found that
incorporating measures of the quantity and variety of social media reactions
can lead to substantial gains in terms of prediction accuracy.

Practical significance. From the perspective of a news provider, while no
automatic system can replace editorial judgment, understanding and predicting
the life cycle of stories has three main benefits:

  * •

In the case of News stories, knowing how the audience is interacting with an
article is not just “nice to have”, but increasingly a critical component in
delivering timely and relevant content to an ever growing online audience.

  * •

For In-Depth stories, which operate on a slower news cycle, knowing when to
allocate additional time and resources can significantly improve the news
planning process. This is particularly useful for an emerging class of news
programmes that combine live online discussions with more traditional TV
coverage.

  * •

To a web producer, an article with a longer shelf-life means judicious time
can be spent preparing backgrounder pieces which are valuable in providing
context to a story. From a reach perspective, articles with steady or
increasing levels of traffic translate into higher user engagement.

Our work depends on having access to a large repository of social media
reactions. As more people get into social media (e.g. Twitter), this line of
work will become more relevant and will be able to produce even higher quality
predictions.

Limitations and future work. We combine findings from computer science,
journalism, and media studies. The research presented here is more difficult
to execute than the traditional single-discipline study, but we expect
interdisciplinary work on this area to become increasingly common as our media
and technology continue to converge.

Our data gathering system collects only aggregate information and does not
attempt to link actions across sessions or users across platforms; this
prevents us from separating post-read from pre-read sharing, an important
distinction explored in [1]. Another limitation of our work is that we used
data from a single website, and we are in the process of gathering data from
other sources in order to strengthen our claims. We also used a manual process
for categorization of the article classes
(decreasing/steady/increasing/rebounding), and we did not attempt a
comprehensive content-based classification of articles inside each class.

In this work, we used linear models and did not attempt anything more
sophisticated. We do not claim that our models are the more accurate that can
be built using this data, but used them to demonstrate in a clear way the
importance of social media signals for the predictive tasks we undertake.
Better models are definitively possible, and may yield even larger gains in
accuracy when incorporating social media signals.

We also used a data-driven approach in which shelf-life is derived from
observations. Alternatively, shelf-life can be derived by fitting a visitation
curve produced by a parametrized model [29]. This may lead to an improvement
in the prediction accuracy.

Reproducibility. The data sample used for this study, including feature
vectors and the categorization of articles done during the qualitative
analysis, is available for research purposes upon request. A live demo is
available at http://fast.qcri.org/

Acknowledgments. The authors wish to thank Al Jazeera English for the data
used for this study, Kiran Garimella from QCRI for his work in the live
system, Janette Lehmann from Yahoo! Research for her valuable help and
comments on an early version of this manuscript; Michael K. Martin and Ju-Sung
Lee from Carnegie Mellon University for insightful discussions on regression
models; and Edward Schiappa for his feedback on the methodology for the
analysis of article classes.

Key references: [7, 27]

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##  Appendix A Example articles

List of articles in the non-majority classes described in the qualitative
assessment of Section 4.2. The data sample is available for research purposes
upon request.

Delayed decreasing:

    Hurricane Sandy moves up US Atlantic coast - Americas
    Skydiver lands safely after record jump - Americas
    Third-party candidates spar in US debate - Americas
    Arrests by French police foiled ’bomb plot’ - Europe
    Scotland’s independence referendum signed - Europe
    Rival protesters clash in Egypt’s capital - Middle East
    Syria opposition ’captures’ Assad soldiers - Middle East

Steady:

    Bomb attack hits northern Nigerian church - Africa
    Libya assembly elects new prime minister - Africa
    Police admit ’overreacting’ at Marikana - Africa
    Marking the Cuban missile crisis - Americas
    Obama and Romney face off in final debate - Americas
    Obama and Romney meet in combative debate - Americas
    Poll finds fresh increase in US racism - Americas
    US exports to Iran soar despite sanctions - Americas
    Asad Hashim: Ask Me Anything on Malala - Central & South Asia
    Clerics declare Malala shooting ’un-Islamic’ - Central & South Asia
    India suspends Kingfisher licence - Central & South Asia
    Pakistani schoolgirl Malala arrives to UK - Central & South Asia
    Profile: Malala Yousafzai - Central & South Asia
    Teenage rights activist shot in Pakistan - Central & South Asia
    Italian seismologists could face jail term - Europe
    Karadzic to begin Srebrenica defence at Hague - Europe
    Russia says fighters killed in North Caucasus - Europe
    Scientists found guilty in Italy quake trial - Europe
    Bomb blast hits Damascus’ Old City - Middle East
    Fatah claims victory in West Bank poll - Middle East
    Fighting dims hopes for Syria Eid truce - Middle East
    Hariri calls on Lebanese to attend funeral - Middle East
    Israel strikes Gaza after Hamas retaliation - Middle East
    Marginalisation of disabled people in Egypt - Middle East
    Palestinians vote in municipal elections - Middle East
    Rights group says Syria used cluster bombs - Middle East
    Syrian children killed in Idlib air raids - Middle East
    US and EU urge political stability in Lebanon - Middle East

Increasing:

    Colombia and FARC rebels launch negotiations - Americas
    Immigrant family in pursuit of American Dream - Americas
    Living the modern ’American Dream’ - Americas
    Man charged over attempted US bank bomb plot - Americas
    Minors flee Central American violence - Americas
    Anti-austerity protests erupt in Athens - Europe
    Lithuanians vote out austerity government - Europe
    Scientists await verdict in Italy quake trial - Europe
    Assault on Yemen base blamed on al-Qaeda - Middle East
    Qatari emir in historic Gaza visit - Middle East

Rebounding:

    African and EU leaders to hold Mali summit - Africa
    Evidence of mass murder after Gaddafi’s death - Africa
    Nigerian soldiers kill dozens of civilians - Africa
    State-linked Libyan militias shell Bani Walid - Africa
    Tunisia clash leaves opposition official dead - Africa
    UN urges military action plan for Mali - Africa
    Wounded Mauritania president flown to Paris - Africa
    Argentine crew to vacate ship seized in Ghana - Americas
    Armstrong ’unaffected’ by doping report - Americas
    Biden and Ryan set for crucial VP debate - Americas
    Brazil forces set for raid on Rio slums - Americas
    Candidates spar in US vice president debate - Americas
    Cuba’s Castro appears in public - Americas
    First planet with four suns discovered - Americas
    Forecasters predict ’serious’ Hurricane Sandy - Americas
    Hurricane Sandy approaches eastern US - Americas
    Tsunami warning for Hawaii lifted - Americas
    US deficit tops $1 trillion for fourth year - Americas
    US East Coast prepares for Hurricane Sandy - Americas
    Dozens dead in Afghanistan Eid suicide blast - Central & South Asia
    Pakistan court probes bartering of girls - Central & South Asia
    Pakistan teen activist in critical condition - Central & South Asia
    Berlusconi vows to remain in political arena - Europe
    Boxer a big hit as Ukraine readies for vote - Europe
    EU leaders agree on banking supervisor - Europe
    Germany’s Merkel reassures Greece - Europe
    Merkel arrives in Greece amid tight security - Europe
    Russia demands Turkey explain intercepted jet - Europe
    Russian opposition aide arrested - Europe
    Baghdad area hit by more deadly Eid attacks - Middle East
    Eid truce awaits Syrian government response - Middle East
    Kuwait police fire tear gas at protesters - Middle East
    Syrian forces continue to shell Aleppo - Middle East


# AI-Generated Imagery: A New Era for the ‘Readymade’

Amy Smith1 and Michael Cook2
1 School of Electronic Engineering and Computer Science, Queen Mary University
of London, UK
2 Department of Informatics, Kings College London, UK

###### Abstract

> While the term ‘art’ defies any concrete definition, this paper aims to
> examine how digital images produced by generative AI systems, such as
> Midjourney, have come to be so regularly referred to as such. The discourse
> around the classification of AI-generated imagery as art is currently
> somewhat homogeneous, lacking the more nuanced aspects that would apply to
> more traditional modes of artistic media production. This paper aims to
> bring important philosophical considerations to the surface of the
> discussion around AI-generated imagery in the context of art. We employ
> existing philosophical frameworks and theories of language to suggest that
> some AI-generated imagery, by virtue of its visual properties within these
> frameworks, can be presented as ‘readymades’ for consideration as art.

## Introduction

Within many popular online spaces such as Twitter and Instagram, the recent
abundance of artistic imagery produced by text-to-image generative deep
learning models has sparked debate as to the nature of these images in the
context of more traditional modes of understanding art, and the art world
111We use the term ‘art’ here to primarily refer to visual art.. We seek to
bring some clarity to this discussion using established theoretical
frameworks. Text-to-image synthesis requires natural language descriptions
that are used to generate corresponding images; the natural language
description often being referred to as the ‘prompt’. Online platforms, such as
Midjourney, allow users to generate artistic imagery within seconds, with as
little as a single word providing the driving concept for the image synthesis
process. The term ‘art’ readily eludes any concrete definition, yet it seems
valid given the current climate around the affordances of generative AI, to
examine how such a large corpus of digital images has come to be regularly
referred to under this title (see figure 1(a)). After over a decade of
computational creativity research attempting to encourage a shift in the
public perception of computer-generated art, the recent and drastic change in
this domain leaves the question open of how and why this has happened now. The
notion of ‘framing’ has been explored within the field of computational
creativity with an aim to deepen our understanding of the relationship between
the perception of the observer and the artistic artifact being perceived (?).
Traditional ideas of framing do not tend to engage with the conceptual baggage
of the observer and instead seek to expose aspects of a generative system in
the hope that this transparency positively affects the perception of the
system, and its output, as creative. We propose a distinct view (but to the
same ends) that it is the _viewer_ framing, rather than the _artist_ framing,
that is important to consider in this context.

(a) Examples of online references to AI-generated imagery as art

(b) Image generated by Midjourney when prompted with the text: ‘A work of art’

(c) Image generated by Midjourney V5 by Pablo Xavier

Figure 1:

## ‘AI Art’ - A Philosophy

There are many frameworks, from the fields of art theory and philosophy, that
can help to interrogate the perceptual status of AI-generated imagery in the
context of categorising imagery as belonging to the class of ‘art’. We propose
that philosophical theories around the formation of meaning, specifically
Wittgenstein’s theory of meaning, can inform our understanding of the
perception of AI-generated imagery in this contemporary context.

### Wittgenstein’s theory of meaning

In his work: ‘Philosophical Investigations’ the German philosopher Ludwig
Wittgenstein tackles how it is that, despite the endlessly variable
manifestations of things in the world, we can still come to understand
similarities between them and can identify groups of occurrences around us
(?). He also addresses how it may be that we come to attach meaning to these
groups through language, specifically words.

#### Wittgenstein’s Thought experiment

To demonstrate this theory, Wittgenstein uses a thought experiment that
questions what it means for something to be considered a ‘game’. The many
differences between games clearly do not define whether or not they can still
be perceived as qualifying as such, as when trying to describe what the word
‘game’ means, any single defining characteristic of a game can also be found
outside of what we perceive ‘game’ to mean. For example, Chess and Solitaire
are both considered to be games, even when one involves a winner and a loser
and the other does not. So the notion of ‘game’ is a contested concept, as an
attempt at any ostensive definition is rejected. Wittgenstein theorises that
it is only through a complex web of learnt resemblances and associations that
we come to understand a game as belonging to the class of ‘game’; and not
through any concrete definition present in the word itself. Wittgenstein
proposes that it is the learnt combination and exemption of traits that
contribute to our notion of what the word ‘game’ means, despite there being
many ways that people come to understand the umbrella term ‘games’ (?).

#### Family Resemblance

Wittgenstein uses the analogy of ‘family resemblance’ to illustrate how it is
that we identify members of cognitive groups as belonging, whilst
discriminating against others. Wittgenstein proposes that we identify one
activity as belonging to the category of ‘game’, but not another, in the same
way that we might recognise a person as belonging to one family and not
another. This is through identifying the resemblances that a person would have
with their family due to the fact that they are relations, and the lack of
this resemblance otherwise. Factors such as eye colour, surname, hair colour,
height, or accent cannot individually be responsible for the decision that
someone may be related to someone else, as it then could be concluded that
everybody was related. A combination of factors, however, becomes
‘resemblances’ (?).

#### Socio-cultural Context

Wittgenstein argues that our socio-cultural backdrop is imperative to our
understanding of language. He famously theorised that:“If a lion could talk,
we could not understand him” (?). A lion is playfully used here, as lions
exist within a very different set of social contexts to people. Within
Wittgenstein’s framework, which anchors meaning in lived experience, it seems
consistent to assume that even if a lion could talk, we would not be able to
understand what it would say. This is because the language would be derived
from a set of socio-cultural circumstances that we are unfamiliar with, not
being lions ourselves. Wittgenstein makes the case that it is the matrix of
resemblance and associations within a social and cultural context that gives
language meaning (?).

### Art $\Leftrightarrow$ Game

We propose that the word ‘game’ used in Wittgenstein’s thought experiment is
interchangeable with the word ‘art’, as this is a similarly contested concept
(?). As such, we propose that we come to understand an image as art in the
same way that we come to understand an activity as a game - in reference to a
plexus of resemblances and associations with existing art.

One could try and make the argument that art is pigment on canvas, but there
are examples of sculpture that contradict this, and so on. So, we rely on a
self-reflective consciousness of social context, resemblances, and
associations in order to recognize something as belonging to a perceptual
category such as Art. Just in the same way that all games contribute to the
meaning of the word ‘game’, all art contributes to what is meant by the term
‘art’.

Images and objects that are readily considered to be art manifest in many
different forms and so defy any ostensible definition. Given this, we propose
that in the same way that Wittgenstein’s theory of meaning allows for an
explanation for how it is possible that we can identify previously unseen
examples of games as belonging to the category of ‘game’, this theory of
meaning also helps to explain how it is that AI-generated imagery can be
categorised as ‘art’. It is through our understanding of other art, despite
its relentless internal physical differences, that we come to be able to
classify something else as art. So it follows, that if an image generated by
AI has the abstracted properties and resemblances needed to identify it with
this perceptual class, then it becomes a member of that class (Art).

### Saussure’s ‘Signifier’ and ‘Signified’

Semiotics is concerned with the making of meaning through the creation and
interpretation of ‘signs’. Signs can be words, images, sounds, acts, or
objects. According to the American philosopher, mathematician, and scientist
Charles Sanders Pierce: “We think only in signs” (?).

The Swiss Linguist Ferdinand de Saussure and, later on, the writings of the
French literary theorist Roland Barthes (who famously declared ‘The Death of
the Author’) influenced thought on the role of semiotics in the deconstruction
and extraction of meaning in the social context. This is through the
recognition of a dyadic system of signs. For Saussure, semiotics breaks down
the factors in our external environment into signs, which are comprised of a
‘signifier’ and a ‘signified’. The ‘signifier’ is the physical form the sign
takes, and the ‘signified’ is the concept that it represents. The sign is the
result of the association of the signifier with the signified, and the
relationship between them is referred to as ‘signification’ (?). The meaning
of signs is said not to be inherent _in_ the words, images, sounds, acts or
objects themselves, but rather in the meaning we attach to them. Anything can
be a sign as long as someone interprets it as referring to something else. We
often interpret signs unconsciously, relating them to familiar systems of
conventions: “A sign is the basic unit of language (a given language at a
given time). Every language is a complete system of signs” (?).

### Image as ‘signifier’, Art as ‘signified’

The question of how a digital image can be removed from its status as simply
an ‘image’ and be interpreted within a new field of understanding where it is
perceived to have a meaning beyond its basic function, we propose, starts in
the interaction between the ‘consumer’ of the image, and the image itself.
This echoes the ideas explored in work by Colton and Wiggins (?), where
Computational Creativity is centered around observers and audiences. The way
in which an image is perceived is similarly relevant to examine here, as it is
at this point of consumption by an audience that an understanding can be
formed by the observer, and where a decision as to the status of an image can
be decided (i.e. an image is given the status of ‘art’). According to the
dyadic theory of semiotics, images have the potential to become art if they
are a signifier for the concept of art. In this case, the image is the
‘signifier’ and the idea of art is the signified. The signifier is able to
function as such as it alludes to the matrix of associated properties
(Wittgenstein) that is signified. Figure 1(b) shows an example of this idea.
We suggest that if meaning arises in the consumption of a dyadic system of
signs, and art consists of these very factors, then it follows that an image
can be understood as art through a participation in this system and the
reciprocal nature of the interaction between the perceiver and the sign being
perceived. Within the framework of Wittgenstein’s theory, it is possible to
understand how an image may come to be understood as art given the social
context. Through the linguistic phenomena of semiotics, it is possible to
understand the relationship between an image as a signifier, and the concept
anchored to it as the signified - which is shaped by the given social context.
Given this, it seems that it is necessary for there to be a much wider
presence in a work of art, of a space for the intuitive understanding of the
perceiver, but also a reference to something ‘learnt’. We propose that it is
the _reading_ of an image that facilitates its attribution to the class of
art.

Figure 1(b) is an example of an AI-generated image (made using Midjourney)
that demonstrates the many different visual properties in an image that AI may
generate in order to be a signifier to the concept of art and satisfy its
internal categorisation system given a prompt (i.e. the user asks for ‘a work
of art’, as shown in figure 1(b)). A brief visual analysis exposes the
following properties associated with art genres: portrait painting (a 3/4
profile of a man is shown), realism (the portrait features are mildly
abstracted), graffiti art (colourful and chaotic spray paint marks and
splatters), Impressionism (colour blocks in the face give the impression of
form, and create highlights and shadow - but are not true to a real-life
depiction), Pop Art (bold, bright, multi-coloured areas in direct contrast
with areas of black). A closer analysis reveals that the colours in the image
correspond to colour theory. The image is mostly constructed from two sets of
complementary colours: blue/orange and red/green. Furthermore, different
shades of these colours are used to create highlights and shadows. These
aesthetic properties are indicators within the signifier (the image) that
signify these well know conceptions of visual art.

Figure 1(c) shows an example of an image generated by Midjourney that doesn’t
explicitly hook into the visual properties associated with artworks, and yet
has cultural significance within a social context. In fact, when the image of
the Pope first became viral on Twitter, many people believed it to be a real
photograph (?). It only surfaced later on that it was in fact an AI-generated
image. This phenomenon is an example of the importance of the audience’s
perception of an image regarding the classification of that image within the
discourse around it.

### Duchamp’s ‘Readymades’

Marcel Duchamp’s concept of the ‘readymade’ was a significant development in
the history of modern art. The term refers to everyday mass-produced objects
that were selected and presented as works of art. The act of choosing these
objects, and presenting them in the context of an art exhibition, was
challenging traditional notions of what could be considered art and
questioning the value of artistic skill and craftsmanship. The chosen objects
were often prefabricated and were separated from their intended ‘mundane’ use
by being brought into the art gallery context and discourse (?). We propose
that AI-generated imagery can be a conceptual extension of the phenomena of
the ‘readymade’, by virtue of them being mass-produced and can challenge our
existing notions of what can constitute an art artifact. As we will explore in
the next section, many of Duchamp’s readymades were intended to challenge
traditional perceptions of the role of craftsmanship in art. Mass-produced AI-
generated imagery poses a similar challenge to the kinds of skills needed to
create culturally significant and artistic imagery.

#### Mass production and the ‘Bach Faucet’

Text-to-image models are systems that are capable of synthesising potentially
unlimited amounts of new and high-quality digital imagery, which we can view
as a kind of ‘Bach faucet’, a term coined by Kate Compton to refer to a
situation in which: ‘a generative system produces an infinite amount of
content that is of equal or better quality than a culturally significant
original…since the endless supply of this content makes it no longer rare, it
decreases its value’(?). This phenomenon represents the inverse of the value
transaction inherent in the process of creating readymades. In the case of the
readymade, an object of low scarcity and value is transmuted into a scarce
object with high value. AI however is able to take scarce and high-value
artifacts (artworks) and mass produce images of equal or increased quality
which lowers scarcity, decreasing the overall value.

### Latent space imbues generated imagery with artistic signifiers

It is timely to intersect this theory with notions of machine learning and how
complexity is abstracted during the training, and image synthesis, process.

We propose that image synthesis algorithms, that are trained on large-scale
data sets of corresponding signs (or patterns (?)), are able to abstract the
complexity of the matrix of multiple associated properties (or signifiers),
that according to Wittgenstein and Saussure accumulates as an understanding of
what art comes to mean, into a ‘latent space’. Any image generated from this
space, that relates to ‘art’ (via a text prompt), is imbued with the
properties learnt from the signs in the training data which act as signifiers
to the concept of art. We propose that, as a result, when we encounter these
properties in AI-generated imagery, we are easily able to associate the image
(signifier) with the web of resemblances and associations that we bring to the
interaction regarding art (the signified). With some degree of inevitability,
given the potential for exposure to the images in the data (pre-training), the
generated images match our perception of ‘art’ - as the model is trained on
many thousands of existing examples of art. This is why, we propose, that so
many AI-generated images are considered to belong to the category of art.

### Kate Compton’s ‘Liquid Art’

The term ‘Liquid Art’ was disseminated at an invited talk given by Kate
Compton at MIT in the autumn of 2022: “The ARTIFACT is dead. All that remains
is the EXPERIENCE. Welcome to the world of liquid art” (?). This term
describes a new form of art experience, specifically in online spaces, in the
wake of the invention of text-to-image generative AI systems (?).

Liquid art is described as a space of potential art artefacts, that is moved
through by surfing and filters, and that is experienced in streams or
overwhelming waves (most commonly in an online space). Liquid art is the
phenomenon of being exposed to mass-produced artefacts, such as AI-generated
imagery, in a space where its abundance means that the experience of ‘surfing’
this media becomes the experience of art. In this framework, art is a verb -
as art becomes the experience of moving through this possibility space (?).
This is opposed to more traditional forms of ‘Solid’ art, where there is an
art artifact of fixed form. Liquid art has implications for images and their
function as signs in our language system, including visual language systems,
as the abundance of imagery and the properties of this imagery (high-quality
artistic imagery) shifts the social context and environment for the sign
within art, and the context within which signifiers of art function.

#### Liquid Art and the Readymade

We propose that, out of this endlessly generated sea of imagery, an image can
be selected based on the ways in which it functions as a sign. Another way to
conceptualise this would be to class the output space of the image generator
as a mass-produced artefact (a space of images) and the act of prompting and
identifying an image as ‘good’ is what creates the readymade. We propose that
because the history of a generated image (the transmutation of meaning from
the training data into the latent space, re-manifested as an image in a liquid
perceptual possibility space, from which it is then selected as a readymade
for its quality as a sign within visual art) is so rich, that there is an
increase in value in the selection of generated imagery in particular as a new
era of readymade, because traditional readymades as a concept (and also an
aesthetic to be signified) are already a part of the web of understanding that
resonates with this selection process as they are an established part of art
history now. At the same time, these images can still serve the function of
challenging traditional notions of what can be considered as art (as with
1(c)) and question the value of artistic skill and craftsmanship as the
traditional readymades did.

## Conclusion and Future Work

AI-generated imagery can come to be perceived as art according to its
perception as a sign by an observer within their social context. The use of
mass-produced AI-generated imagery as art can be seen as a conceptual
extension of the ‘readymade’ within in a complex contemporary context, where
the selection and presentation of the image can challenge even current notions
of what is required for the experience of ‘art’. We suggest that future work
could go on to discuss memes as readymades, based on the way that they involve
the use of mass-produced imagery and hold relevance to the socio-cultural
backdrop of the time.

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