Analyzing the Evolution of World Cultures
through Epic Stories: From Gilgamesh to
Games of Thrones
1Peter Praeder, 1Gloria Volkmann, 2 Peter A. Gloor
1

University of Cologne, Germany, , praederp@smail.uni-koeln.de, volkmann@wim.unikoeln.de
2

MIT Center for Collective Intelligence, USA, plgoor@mit.edu

Abstract We study humanity’s attitude towards violence by looking at the plotsof 110 epics in Wikipedia. Starting with Gilgamesh dated 2100 BC, and ending
with Games of Thrones firmly anchored in the 21st century we analyze the plot
section of each epic described in Wikipedia, using different automatic sentiment
analysis tools such as LIWC and IBM Watson ToneAnalyzer andAlchemyLanguage to calculate average sentiment . We find an increase in positive
sentiment over the centuries, confirming Steven Pinker’s theory of a reduction in
overall violence despite a still high potential for conflicts in today’s globalizedinterconnected fast changing world; indicating growing societal resilience towards
violence. We also find more positive emotion in Asian and African epics.

1 Introduction and Related Work
The goal of this project is to investigate if humankind’s attitude towards
violence has changed over the last 4000 years. According to the arguments madeby psychologist Steve Pinker in his seminal book “The Better Angels of Our
Nature” (Pinker, 2011), humanity is becoming less and less violent. Pinker showsthat tribal warfare was nine times as deadly, and the murder rate in medieval
Europe thirty times higher than it is today. This decrease in violence is still ongoing, with today’s wars killing a fraction of the people killed by wars in the past.
In a 2015 update to his book Pinker shows that this trend of decreasing violence iscontinuing, with homicide rates further dropping, as are rape and violence againstchildren.
In earlier work (Gloor et al. 2015) we used Wikipedia as a “socioscope” to study
different aspects of intercultural human evolution. We drew on different-language
Wikipedias to provide a mirror of today’s historical understanding of World2history by different cultures (Kleeb et al. 2012), to measure differences in genderequality (Marcos de Oliveira & Gloor, 2016), and track emotionality andpositivity in sentiment in different-language news, and assess whether war andconquest or science and art are considered more important in different cultures andidentify the most influential thought leaders (Frick et al. 2013).
In this project we apply our Wikipedia-based approach to the longitudinal studyof violence, as expressed in World literature with the goal of analyzing theevolution of humankind’s attitude towards violence by using literature as a mirrorof human society and its values. Art and literature, specifically, produces
subjectively modulated images of the real world and experiences made in theworld. However, as those subjective images and experiences are shaped not only
by internal conflicts, concerns and wishes of the producing artist, but also by
relations of the artist to other people, relations to nature, traditions of ancestors
passed on, spiritual forces imagined and believed in, and whatever the artist andsociety anticipates might happen in the future, those images are shaped by society
and thus mirror our societies and culture (Carroll, 2006). To investigate
humankind’s attitude towards violence expressed in world literature over time weanalyzed the plots of 110 epics through their representation in Wikipedia.
Epics are poetic narratives of length and complexity that center around deeds ofgrandeur or heroism of significance to the community (Beissinger, 1999). Thus
epic poems are a literature genre that most likely reflects the tensions, conflicts,
and moral codes of society (Carroll, 2006). They thus also mirror the evolution ofhuman societies and their attitude towards violence.
The epics in Wikipedia start with “Gilgamesh” which was written in ancient Syria
sometime between 2100 and 1500 BC, and end with Games of Throne1. Our
hypothesis is that if Pinker is right, a change in humankind’s attitude to violence
should also be reflected in world literature. Thus either epics and in accordance
the language used to describe the plots of epics, might get less violent . – which is
hard to believe considering all the violence used in the most modern epos “Gamesof Thrones”- or the opposite is to be observed, namely that violence moved fromthe real world into the fantasy world of epics and might be going up. In addition to
the main focus of this study, the evolution of humankind’s attitude towards
violence, this study also seeks to answer the question whether the description of
the plot of an epic in Wikipedia is suitable for automatic sentiment analysis.
Wikipedians strive to write in a style they call “NPOV” (Neutral Point of View),
trying to be as factual as possible. We hypothesize that this might work to ouradvantage, as the plot is described in more general language through the neutraleyes of the Wikipedia editors, one step removed from the artistic prose of theoriginal author.

1

https://en.wikipedia.org/wiki/Game_of_Thrones

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2 Method
We analyzed the plots of the world’s major epics available in Wikipedia bycollecting all articles categorized in the English Wikipedia as “Epic_poems”. The
English Wikipedia contains 178 articles in this category. Each of the Wikipedia
entries for an epic contains a “plot” section where the storyline of the epic is
described. We analyzed the language used within these sections by means of
natural language processing methods with regard to the tendency towards violenceof an epic.
For natural language processing, only the articles with more than 300 words oftext in the plot were included, leading in the end to 110 articles with a “plot”
descriptive enough to provide a basis for our content analysis. The limit of 300
words was derived from the recommendation of the LIWC manual (Pennebaker et
al. 2001), indicating that meaningful sentiment analysis needs at least that many
words.
The plots were extracted and linguistically analyzed with regard to violence
tendencies by means of 3 different types of “tone information” that can be derived
from text: emotional tone (i.e. anger, fear, joy, sadness, and disgust), author social
tendencies (i.e. big five psychological author personally traits: Agreeableness,
Conscientiousness, Extraversion, Emotional range, Openness) and language style(i.e. analytical tone, tentative tone, confident tone). For this purpose threeautomatic sentiment analysis tools, namely LIWC and IBM Watson ToneAnalyzer
and IBM Watson AlchemyLanguage were used.
To calculate emotional tone LIWC, IBM Watson ToneAnalyzer as well as IBM
Watson AlchemyLanguage were used.
LIWC (Pennebaker et al. 2001, Pennebaker 2011) calculates the basic negativeand positive emotion of a text based on an extensive dictionary. We put particularemphasis on the basic emotional tone, and on fear, anger, and sadness. Besides
LIWC, IBM AlchemyLanguage (IBM2016a) was used to compute the polarity ofa text ranging from -1 to +1 as well as key terms used in the text. IBM
AlchemyLanguage components to compute the polarity of a text as well as IBM
ToneAnalyzer (IBM2016b,c) are based on Pennebaker’s research and computeemotional tendency, in particular anger, disgust, fear, joy, and sadness.
To Analyze Wikipedia plot author social tendencies IBM AlchemyLanguage
was used . AlchemyLangua uses the big five personality model (Costa & McCrae
1992) to categorize the personality of the author in the five dimensions openness
to experience, agreeableness, conscientiousness, extraversion, and neuroticism.
Agreeableness is a person's tendency to be compassionate and cooperative
toward others. Persons that show high agreeableness are rather altruistic and findthat helping others is genuinely rewarding. They tend to dislike confrontation andare perfectly willing to compromise or to deny their own needs to get along withothers. They are often modest, self-effacing, and humble even though they do notnecessarily lack self-confidence or self-esteem. They are generally sincere andthus see no need for pretense or manipulation when dealing with others and are4therefore candid, frank, and genuine. They are often empathetic, tender-hearted,compassionate and trusting and often assume that most people are fundamentally
fair, honest, and have good intentions.
Conscientiousness is a person's tendency to act in an organized or thoughtful
way. Highly conscientious person often try hard to achieve excellence. They arevery deliberate and thus generally are disposed to think through possibilities
carefully before acting. They have a high sense of responsibility, duty andobligation and are well-organized, tidy, and neat. They are rather Self-assured andconfident in their ability to accomplish things and are rather persistent with theself-discipline, or "will-power," to persist at difficult or unpleasant tasks until they
are completed.
Extraversion is a person's tendency to seek stimulation in the company ofothers. Highly extroverted persons are very quick, energetically and are involvedin many activities. They are assertive and like to take charge and direct theactivities of others and thus tend often to be leaders in groups. They are mostly
positive , cheerful, and also excitement- seeking. They are friendly and outgoingand genuinely like other people and are very sociable and find the company ofothers pleasantly stimulating and rewarding.
Emotional range, also referred to as neuroticism, is the extent to which aperson's emotions are sensitive to the individual's environment. Increasing valuesof emotional range indicate that a person is quicker to anger, prone to worry, moreeasily depressed and moody, less self- consciousness and more stress sensitive
than a persons with lower values of emotional range.
Openness to experience, is the extent to which a person is open to experiencing
a variety of activities. Persons with a high openness to experience are eager to try
new activities, show high artistic interests and love beauty, both in art and innature. They have good access to and awareness of their own feelings, are very
imaginative and have a high readiness to challenge authority, convention, andtraditional values, They are intellectually curious and tend to think in symbols andabstractions.
To analyze the language style also, IBM AlchemyLanguage was used.
IBM AlchemyLanguage distinguishes the style of language between “analytic”,“confident”, and “tentative”. The parameters range from 0 to 1, with 1
corresponding to a 100% likelihood that the particular emotional tone is present in
the text.
Besides the sentiment analysis, the social network of all actors in an epic was
calculated and visualized on a Web site (see http://master.it-praeder.de), and theirindegree, outdegrees as well as network size and density was calculated(Wassermann & Faust 1994). This additional analysis allows to better understandand explain possible changes in humankind’s attitude towards violence, asconflicts (Dziubinski et al. 2015) and thus violence are affected by the underlying
social network of humans. Because conflicts between two or more entities affectnot only the conflicting parties but often lead to spillovers through the network of
relations on other neighboring third parties (Dziubinski et al. 2015). Thus
comparing the development of epics social networks size and structure over time5

to the development of language and its violence tendencies in epics plots overtime can support and help explain the observed changes in language.

3 Results
Figure 1 shows the results of the sentiment analysis of the 110 epics from 2100
BC to 2010AD, aggregated by averages per century, and calculated with
ToneAnalyzer and LIWC. As we can see, the overall emotionality tents to be morenegative, with particularly high levels of sadness and fear.. However this is to be
expected, considering that epics center around deeds of grandeur or heroism ofsignificance to the community (Beissinger1999) and that such heroic deeds aremost often performed by raising and solving mostly violent conflicts.
In addition, particularly in the first millennium AD, we observe highfluctuation in emotionality.
Fig. 1. Different emotions over the centuries from 2100BC to 2010AD

Figure 2 illustrates the development of sentiment (i.e. overall emotional tone ofthe plots of the epics) calculated with AlchemyLanguage. While the sentiment
trend line is slightly positive indicating a positively increasing emotional tone, it isin the negative range reflecting the generally more negative emotionality observedin figure 1 and is not statistically significant. Informally, we nevertheless can saythat the sentiment of the epics has been increasing slightly over the last 4000
years, in spite of all the violence in the most recent epic we analyzed, Games ofThrones. For the analysis in figure 2 only the 49 epics with a sentiment deviationof 0.3 on the positive or negative side were included.

6Fig. 2. Sentiment average over the centuries from 2100BC to 2010AD using AlchemyLanguage

Figure 3 shows the sadness of all 110 epics distributed over time, calculatedwith IBM ToneAnalyzer. We find that sadness is decreasing over time, althoughagain not statistically significant. Nevertheless, this further supports our
hypothesis that positivity in epics is going up, and sadness going down.
Fig. 3. Sadness (y-axis) for all epics, calculated with IBM ToneAnalyzer (x-axis is time)

From figure 1 to 3 emerges a differentiated picture, where the subcomponentsof sentiment show different trajectories. It appears that most epics have overallstrong negative sentiment
Further condensing sentiment into four major phases, we observe a u-shaped
curve as shown in figure 4 , with negative sentiment (i.e. more negative emotions)

7

until 1350, more positive sentiment (i.e. more positive emotions) from 1350 to1800, and negative sentiment for the period from 1800 until today.
Fig. 4. Overall sentiment development (y-axis) over time for four major time phases (x-axis),
calculated with IBM AlchemyLanguage

Sadness is quite dominant for all epics, in all epochs, however it is decreasing
for the immediate past. Fear however is growing over time, measured andaggregated into 4 data points. Happiness has a spike in 1500, and is slowly
increasing overall.
In a final analysis we categorized the epics by region of origin, into five
regions: MiddleEast, Asia, Africa, America, and Europe. When simply comparing
the average values of the epics from the different regions, we found that theancient Middle East epics such as Gilgamesh are more “tentative”. Asian epics
have more “joy”, and African epics have more “positive emotions”. On the other
hand, European and American Epics show more “analytical thinking” and are less“tentative”. We suspect that these last two properties might be influenced by themore recent modern epics such as “Games of Thrones”.
When applying a varying intercept multilevel regression model to the 110 epicsof the five different regions with “age” as dependent variable (table 1), we foundthat the age of an epic is explained partially through the language style of the epic.
The variables “emotional range” (the authors’ level of neuroticism) and “tentative
language” are significant. The less tentative the language is, and the less emotional
range (less neuroticism) the language has, the more modern is the epic.

8

Table 1. Varying intercept multilevel regression results, regressing the 110 Epics in the 6different Regions against their age. Predictors are group (region) mean centered
Depended variable: age
Model 1
Regression coefficients (fixed effects)
Intercept
1023.974(0.080)emotional tendency
Anger
DisgustSadnessauthor personality traits

Model 2Model 31023.935(0.080)1023.537(0.080)1022.843(0.081)51.95902(0.893)55.71526(0.862)-413.9309(0.469)154.9731(0.693)173.0391(0.589)-123.5488(0.835)276.4459(0.462)384.4662(0.216)281.1427(0.625)-20.65198(0.964)156.3928(0.535)497.2998(0.140)192.9523(0.537)-895.5592**(0.039)

532.5112(0.271)327.548(0.249)354.0083(0.271)
267.3015(0.386)-1190.146***(0.005)

Openness
AgreeablenessConscientiousness
Extraversion
Emotional Range
language style-23.11262(0.951)-23917.62***(0.003)
16.06683(0.098)Confident
Tentative (LIWC)
Analytical Thinking (LIWC)
Variance Components (Random Effects)
Intercept (variance among
epics between regions)
1297.246
Residual (variance among
epics within regions)
485.6962
interclass correlation
0.8770547
Log likelihood-848.03549
LR chi20
Prob > chi20*p<0.1, **p<0.05 ***p<0.01

Model 41297.37484.4044
0.8776485-847.75614
0.56 (3)0.90581298.641
470.9669
0.8837644-844.80511
6.46 (8)0.59581300.862
446.4617
0.894623-839.19953
17.67(11)0.0895*The negative effects of “tentative language” as well as “emotional range” weobserve, might be due to a shift in the purpose and knowledge of the epics overtime. Modern epics like “Games of Thrones” are fictional, written particularly forentertainment purposes. Therefore the universe, i.e., the time, environment andinterdependencies of an epos are fully known, which might be reflected in thelanguage. The main purpose of ancient epics is the preservation of historicalevents and teaching of moral concepts. Due to lack of knowledge of the actual

9historical event and changing environment circumstances in human history in thelong timespan the epos was passed down, there is much more uncertainty thatmight be reflected in the language. Therefore we see a decline in “tentative
language” and “emotional range” over time. In addition a the negative effect ofemotional range, i.e. a decline in neuroticism might also be a reflection of theobserved although not significant positive increase in emotional tone even if theoverall sentiment (i.e. emotional tone) is on the negative side.
In line with this observation the high interclass correlation as well as thesubstantial variation of mean age in the five regions with a standard deviation of1300 years reflect human evolution over time and mirror the rise and fall ofadvanced civilizations and with it the evolution of cultural heydays of different
regions over time. Putting it in other words, regions subsume the change oflanguage over time and a large part of the difference in language can be explainedby the different regions where the epics are originating.
Besides the sentiment, also the social network of actors in the different epics
was calculated and visualized because conflicts and violence affect not only the
conflicting parties but through the network of relations also other neighboring
third parties (Dziubinski et al. 2015). A change in networks of actors in epics overtime reflects a change in networks in societies of time and thus gives further
insight to changes of humanity’s attitudes towards violence over time.
The network of actors of each epic was constructed based on links between
actors on Wikipedia. A link is drawn if the Wikipedia page of an actor in an epic
links to the Wikipedia page of another actor in the same epic.
Fig. 5. Actor network of Ulysses (see http://master.it-praeder.de/graph/ for live online version)

10

Figure 6 illustrates the actor network of “Games of Thrones”, with Tyron
Lannister, Jon Snow, and Joffrey Baratheon being the most central characters.
This corresponds to the plot of “Games of Thrones” where these three actors are
key figures.
Fig. 6. Actor network of “Games of Thrones” (see http://master.it-praeder.de/graph/ for live
online version)\
While the number of actors is growing over time (figure 7) with the older epics
having fewer actors (Gilgamesh has 14), the density of the social network is going
down over time (figure 8), which means that in the ancient epics almost every
actor was interacting with most other actors, while in the more recent epics there
are subplots, where only a subgroup of actors is interacting with each other.

11Fig. 7. Development of epic actor network size (y-axis) over time (x-axis)

Fig. 8. Development of epic actor network density (y-axis) over time (x-axis)

Assuming that dense networks lead to an increased and faster diffusion of
conflict and violence (Dziubinski et al. 2015), the increase of actors in epics anddecline in density of epic actor networks over time help to explain and support the
decline in violence expressed in world literature.
The observed albeit not significant positive increase in emotional tone as wellas the significant observed decline in neuroticism and tentativeness indicate adecline in violence expressed in world literature. A decline in actor network
density which theoretically should reduce or at least slow the spread of conflictand violence thus further explains and support the in world literature observed
violence decline over time.

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4 Discussion and Conclusion
We have introduced a novel way of looking at the evolution of cultures andtheir attitudes towards violence through the style of language in epics over time.
We find that in spite of the neutral writing style of Wikipedia, the synopsis of theplot differs enough to demonstrate key differences in the use of words over timeand regions. As a long-ranging trend we find indeed more positive and less sadlanguage as time progresses, older epics are sadder and more negative than newer
ones. We also find that older epics tend to have fewer actors, who are allconnected, while more modern epics tend to split into subplots of isolated groupsof actors. We also found that African and Chinese epics show more positive
emotions than epics from the Western world.
While the number of our analyzed epics is comparatively small, the analysis
still has led us to interesting results. If we wanted to verify our findings on a larger
scale, we would have to build a system on the scale of Google ngrams, analyzing
the full text of a representative sample of epics over 4000 years. Such a project
unfortunately is right now far above the resources of our team. In the meantime, itwould be more feasible to increase the scope and the number of works of World
literature, which are available as a synopsis in Wikipedia, to include in our
analysis additional categories besides epics such as for example plays and novels.
To conclude, we have indeed been able to show that epics in modern times
show more positive sentiment – in spite of all the violence in Games of Thrones –and consequently support Steven Pinker’s argument that humanity is getting lessand less violent and thus that societal resilience seems to be increasing.

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