Digital History meets Microblogging: Analyzing Collective
Memories in TwitterYasunobu Sumikawa∗

Adam Jatowt

Marten Düring

University Education Center,
Tokyo Metropolitan University
ysumikawa@acm.org

Dept. of Social Informatics,
Kyoto University
adam@dl.kuis.kyoto-u.ac.jp

Luxembourg Centre forContemporary and Digital History
marten.during@uni.lu

ABSTRACTMicroblogging platforms can offer good opportunities to study how
and when people refer to the past, in which context such references appear and what purposes they serve. However, this area
still remains unexplored. In this paper we report the results of alarge scale exploratory analysis of history-focused references in
microblogs based on 11-months long snapshot of Twitter data. Besides understanding the nature of history-focused content sharing
in microblogs, the results of this study can be used for designing
content recommendation systems and could help to improve timeaware search applications.

KEYWORDS
social media analysis; history; collective memory; Twitter
ACM Reference Format:
Yasunobu Sumikawa, Adam Jatowt, and Marten Düring. 2018. Digital History meets Microblogging: Analyzing Collective Memories in Twitter. In
JCDL ’18: The 18th ACM/IEEE Joint Conference on Digital Libraries, June
3–7, 2018, Fort Worth, TX, USA. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3197026.3197057

1

INTRODUCTION

Having good knowledge and comprehension of history is important for a variety of reasons: History is commonly believed to playsignificant roles in our society: First, to help us understand theprocesses which shape the present and thereby enable us to actively take part in contemporary society. Second, to be the basis forthe development of coherent identities. Third, as a means to givemeaning and orientation with regard to the past. Others argue that
history opens up perspectives for the future and provides support
for decision making [1, 10]. As such, history remains one of thefundamental subjects taught from elementary schools onwards.
Social media has been commonly utilized to study public attitudes towards real time events such as the US American elections[19]. Yet, similarly to other media, microblogs are also used forsharing and finding information related to the past, sometimes to
distant past. This offers unique opportunities for computational∗ This research was performed while the first author was at Tokyo University of Science, Japan.

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https://doi.org/10.1145/3197026.3197057

studies on explicit references to past events and opens up novel
perspectives for the study of collective memories as well as the
pursuit of public history.
While computational collective memory studies have alreadybeen conducted on news articles [2, 6] and Wikipedia [8, 9, 15,16], with regard to microblogging, so far we are only aware of
one ongoing project which focuses on the First World War [5]and compares commemorative cultures across countries. We thenattempt at filling this gap by focusing on Twitter as a commonsocial media platform frequently used in the computational social
science. Our analysis has exploratory character and aims to provide
initial and broad investigation of history-related content sharing insocial networks.
Among others our study is guided by the following questions:(1) How do people refer to history in microblogs?
(2) What is the time horizon of history-related references?
(3) How are collective memories expressed in Twitter?
(4) What are the key remembered events and entities?
We approach these and other questions by investigating portionsof tweet messages generated from March 2016 to February 2017 andtagged with hashtags which indicate a strong relation to historicalevents. In particular, we apply a bootstrapping approach to discover
related hashtags, which allowed us to collect close to 1 million
tweets with explicit references to past events and entities.
Based on the collected data we investigate the distinguishing
characteristics of tweets which are related to the past. Among the
aspects we research are time horizons, concerned entities and popularities of hashtags. Furthermore, we propose a novel categorization
scheme of hashtags as well as perform deeper investigation of thecharacteristics of the hashtag categories (Sec. 5). By this we try
to organize and provide structure to the portion of user activities
that relate to referencing, appreciating and sharing information on
historical events and entities in social network services.
Besides being a novel approach towards the general question on
how the past relates to our lives, our investigations can be beneficial to several applications. First, the construction of specialized
content detection and recommendation systems can be informed by
the reported results. The objective of such systems would be tofacilitate sharing of historical knowledge. Historical content recommendation based on social media offers an interesting and informal
environment for learning history. Understanding the types of popular shared content and the context of sharing can be helpful for designing effective recommendation systems. Indeed several projects
already use social platforms like Twitter to stimulate interest in
history and for teaching exercises 1 .
1 https://twitter.com/RealTimeWWII, https://twitter.com/civilwarwp, https://twitter.com/1948War,

https://twitter.com/samuelpepys

Automatic content creation, especially, in the form of conversing
history-focused chatbots could be another appealing idea. Tweets
thanks to their short content and the effective means of measuring
their popularity or attractiveness (e.g., retweet counts and analysis
of user responses) are useful source of data for such systems.
Many history- and memory-focused tweets are triggered by current events or entities. Studying their formation and fluctuationsof their popularity could be useful for understanding the conditions and circumstances for enabling “historification” of present
texts. This means offering access to relevant historical references andgrounding for the present events and topics.
To sum up we make the following contributions in this paper:(1) To the best of our knowledge we are the first to study how
users refer to history in microblogging based on large scale
analysis.
(2) We provide novel findings which offer a better understandingof how collective memories are maintained and formed in
microblogging.
(3) We propose new categorization of historical references inTwitter.
(4) We outline novel research directions and potential applications that can utilize history-related content in microblogs.
The remainder of this paper is structured as follows. In the next
section we present related work. In Section 3 we describe the datacollection and processing. Section 4 provides the findings of thegeneral analysis, while the following section proposes our novel
categorization of hashtags and details the results of the related
study. We then include discussions in the Section 6. Finally, the lastsection concludes the paper and outlines future work.

2

RELATED WORKThe concept of collective memory (social memory) popularized by
Halbwachs [11, 12] describes the shared reflection of the past withinsocial groups. Collective memory can be contrasted with the concept of collective amnesia defined by Jacoby [14] as forceful or
unconscious suppressions of memories, especially, those related todisgraceful or inconvenient events. In a similar fashion to personal
memory [7], social memory is known to thin out over time andto be subject to temporal variations following the occurrence ofmemory triggers such as sudden events or anniversaries [2, 16, 17].
Studies of collective memory can help us to understand the mechanisms of forgetting and remembering as well as explain the role ofthe history and the past in our lives. In addition, they have direct
implications on the archival selection by memory institutions suchas national or dedicated archives [17]. Traditionally, research oncollective memory has been based on small-scale investigationsof personal accounts and the activities of political and cultural
institutions. There is little literature on the use of computational
approaches for the quantification of the characteristics of social
memory over large text datasets. Cook et al. [6] investigated the
decay of fame over time on the basis of the collection of news articles that spans 20th century. Au Yeung and Jatowt [2] have studied
memory decay and the way in which past years are remembered
based on the dataset of English news articles spanning 90 years.
When it comes to other document genres, Ferron and Massa [8] andKanhabua et al. [16] proposed to use Wikipedia as a global memory

Table 1: Dataset statistics.
Number of history-related hashtags
Number of tweets
Period of timestamps
Period of time references
Number of tweets with time references
Number of users
Number of URLs
Number of tweets with URLs

147
888,251
8 Mar. 2016 – 24 Feb. 20178156 BC – 2029
357,682
390,106
204,075
404,136

space. However, differently to our work they focused on memory
triggers that cause forgotten or vaguely remembered events to bebrought back into social attention. Anniversaries are natural examples of memory triggers. In another case, current events may also
serve as triggers of the memories of similar, past events. Our work
can be seen as complementary to the above mentioned researches.
To the best of our knowledge only one work focuses on microblogging scenarios in which commemoration of the First WorldWar [5] is studied in relation to diverse countries. In contrast, we
use relatively large size data (at least, when it comes to historyrelated studies) and we investigate many unknown aspects ranging
from the types of references, intensity of remembering, key entities,
dates, temporal patterns and so on.

3

DATA COLLECTION

This section describes the data collection and preprocessing procedures as well as general statistics of the dataset used for analysis.
The key statistics of the collected data are summarized in Tab. 1.
Collecting hashtags and tweets. To collect tweets that refer to the past or are related to collective memory of historicalevents/entities, we performed hashtag based crawl together withbootstrapping procedure. At the beginning, we gathered several
historical hashtags selected by experts (e.g. #history, #WmnHist,#HistoryTeacher) 2 . In addition, we prepared several hashtags
that are commonly used when referring to the past: #onthisday,#throwbackthursday, #historicalevent, #thisdayinhistory,#otd. We then collected tweets that contain these hashtags by using Twitter’s official API3 .
The collected tweets were issued from 8 March 2016 to 24 February 2017. To increase the coverage, we applied bootstrapping tosearch for other hashtags frequently used with the seed hashtags.
The tweets tagged by such hashtags were then included into theseed set after the manual inspection of all the discovered hashtags
as of their relation to the history. In total, we gathered 147 historyrelated hashtags which allowed us to collect 888,251 tweets 4 . Weshow the complete list of the hashtags in Tab. 6.
Extracting time-references. We extracted time-references fromtweet content. Two categories of temporal references are typicallydistinguished based on the common distinction of temporal expressions in IR and NLP [4]: explicit and implicit temporal expressions.
The former one is a concrete time point or time period, such as“1945” or “1980s”, while the latter is a relative temporal expression
such as “yesterday” or “two years ago”. We use both types of temporal references in our study and, so, we convert all implicit (relative)
2 http://blog.historians.org/2013/08/history-hashtags-exploring-a-visual-network-of-twitterstorians/
3 https://dev.twitter.com/rest/public
4 Ids of tweets analyzed in this paper are available in http://tk2-222-20713.vs.sakura.ne.jp/jcdl_2018_

target_tweet_ids.txt

temporal expressions to the explicit (absolute) ones. To extract boththe types of time references, we use Heideltime [18], which is atemporal tagger with a specialized option for tweet processing.
Heideltime outputs normalized temporal expressions according
to the TIMEX3 annotation standard. In total, we have found over
357k tweets with temporal expressions which represents 40% of thedataset. 5

20,0006,000

15,00012,5002016

Tweet
Retweet17,500
4,000

1916
1945

2,000

10,000

1941

7,500

0
1600 1750

1900

2050

1825

1875

1900

5,000

GENERAL ANALYSIS

In this section, we first investigate characteristic features of historyrelated tweets based on three data types: time expressions, entities
and hashtags.

0

1850

1925

1950

1975

2000

Figure 1: Distribution of time references in tweets. The
peaks are in years 1916, 1941, 1945 and 2016.

Temporal Analysis

*vw

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deltime adds for such expressions "00" at the head of year information (e.g., 0016, 0088) we converted"00" to "19" or to "20" depending on whether the last two digits are less (conversion to 20) or morethan 17 (conversion to 19).

20

5 Note that some tweets contain abbreviated temporal expressions (e.g., 6/11/16 or 3/19/88). As Hei-

30%

[tb ,t e ]∈T

where T includes all the extracted time references, and the function
δ returns 1 if the first argument is included in the second argument;
otherwise, it returns 0.
Looking at Fig. 1 (especially, at the zoomed out plot in the innergraph), we can see that the number of time references is usually
rapidly increasing towards the present (neglecting short-term disturbances caused by key events to be discussed later). In general, therecent past is referred to more than the distant past, and the memory
decay is fastest in the recent years. This is intuitive and correlateswith the corresponding study conducted on news articles related
to different countries [2].
Several significant peaks are visible in Fig. 1 which represent two
key events in the last century: WWI and WWII, and year 2016. Twodates common for WWII are: 1941 denoting the Pearl Harbor attack
and the subsequent participation of USA in the war, and 1945 whichis related to the Normandy landing and the end of the war. While

1916

1945

We first analyze which time periods users are interested in by
investigating time references included in tweets. We map all theextracted temporal expressions on timeline as shown in Fig. 1.
We call the curve in Fig. 1, the remembering curve as it reflects
the strength of the collective attention of users towards different
time periods of history. To plot such a curve, we converted theextracted temporal references to probability distributions over theircorresponding timespans using year level granularity. In otherwords, for a given time reference (e.g., 1960s) with tb denoting
its start year (1960) and te indicating its end year (1969) we set theprobability distribution with zero values for t < tb and for t > te(e.g., before 1960 and after 1969) and with non-zero values fortb ≤ t ≤ te that sum to 1 (e.g., 1/10 for each year from 1960 to 1969).
We then combined for every year all the computed probability
distributions based on all the tweets in our dataset. The formal
definition of the probability distribution for a year y is given in Eq.
(1).
Õ
1
(1)
S(y) =
δ (y, [tb , te ]) ∗t e − tb + 1

WWII started in fact earlier with the Nazi invasion on Poland in
1939, many history-related tweets in our dataset originate fromUSA and Canada due to the chosen English hashtags resulting in
the focus on the North American involvement in the war. This canbe confirmed when looking at Fig. 2 that shows the most common
entities corresponding to the peaks and at Fig. 4 which lists the top
entities in our dataset. In this work we employ AIDA [13] - an annotation tool linking phrases in short text with their corresponding
Wikipedia articles - for detecting entities.

1941

Q. What is the character of memory decay?
Q. Which years are strongly remembered in particular?

%

4.1

2,500

70

4

r

Figure 2: Top entities associated with the four peaks of Fig.
1. "*" is used to denote abbreviations made for saving space(*wwii: World War II , *vw: Virginia Woolf, *usmc: UnitedStates Marine Corps, *us: United States, *uk: United Kingdom, *sun: The Sun News-Pictorial, *mel:Melbourne, *ge:
Germany, *fr: France, *dcc: Dachau Concentration Camp,*ca: Canada, * ber: Berlin).
Fig. 3 shows the most common hashtags used with content containing the peak years of Fig. 1. We can notice that 1916 and 1941,
1945 have strong connection with hashtags #ww1 and #wwii as thetwo events were held during these respective years. Interestingly,
Fig. 3 shows that there are many mentions of 2016 with #ww1. Thisis because 2016 marked the 100th anniversary of the Battle of Verdun which is especially remembered due to its estimated nearly 1
million casualties. In the same year (1916) another remarkable event
occurred - the British Army suffered its worst day with the loss of
19,240 men in the Battle of Somme (hence, the hashtag #somme100).

These events together with the Easter Rising of Irish republicans
against British occupation in Dublin are ones of the primary causes
for the strong remembrance of 1916. The round anniversary in 2016
especially amplified this remembering; hence, the corresponding
peak in Fig. 1 is even higher than the subsequent peaks associatedwith WWII.

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Figure 4: Top 30 entities mentioned in the dataset.

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history *otd

Figure 3: Top hashtags associated with the four peaksof Fig. 1. "*" is used to indicate abbreviations made forsaving space (*w1c: #ww1centenary, *wrm: #weremember,*tbt: #throwbackthursday, *tdih: #thisdayinhistory, *sm1:#somme100, *otd: #onthisday).
Finally, as shown in Fig. 3 #otd and #onthisday are commonlyused labels for indicating historical content that occurred on thesame calendar day in the past. Past-to-present connection throughthe reference to a calendar day is in fact a popular way of recalling
past events and is typically used in newspapers (e.g., sections about
events reported in “our newspaper on this day in the past”). #otdand #onthisday are hashtag-based mechanisms for indicating thistype of connection in Twitter.

To thoroughly investigate entities and their types, we next automatically map all the entities into DBpedia [3] to obtain their typeassignments. We then divide all the entities into five major types(Person, Group, Place, Event, and Others), and show their rates inFig. 5. It can be noticed that persons, places and groups tend to be
frequently mentioned in history-focused tweets and the person category is especially common. Note that while places were the mostcommon entity type in the set of the top frequently mentioned
entities as indicated in Fig. 4, they are actually less common thanpersons when all the entities in our dataset are concerned.
4.3% 2.9%
18.9%
Person
Group
Place
Event
Other
12.1%

4.2

61.8%

Entity Analysis

Q. Which types of entities and which entities in particulartend to be remembered?
Q. Which past and present entities are compared or mentioned together?
We now look into entities referred to in tweets as, often, a particular entity such as a person or an event is what society remembers
strongly from the past.
4.2.1 Entity Popularity and Type. We first count the numberof times a given entity is mentioned in tweets. Fig. 4 lists the top
frequent 30 entities overall. In these top 30 entities, there are 22
countries, regions, or cities, two historical events (WWI and WWII),
three persons (Adolf Hitler, Abraham Lincoln, and Donald Trump),
and three other kinds of entities. Location entity type tends tobe then most frequently mentioned within the group of the top
common entities. This is because places are key constructs helping
to locate the occurrence of events, indicate locations of historicalbuildings or areas where famous people lived, as well as they forma kind of a “bridge” between the past and the present by existing
across long time periods.

Figure 5: Rate of different types of entities.
To give some examples of entities, Tab. 2 lists the top 10 entities
for the Person, Group and Event types6 . As most of the tweets
were posted in 2016 when the United States presidential election
was held, the names of the five US presidents (Abraham Lincoln,
Donald Trump, Bill Clinton, Barack Obama, and George Washington) appear within the top 10 persons. As for the events, wars andbattles are the prevailing type. Interestingly, groups include many
military units (e.g., U.S. Army, Royal Air force), which also suggest
the significant focus on wars and conflicts from the past.
4.2.2 Connection of Past and Present Entities. The issue of connecting present and past entities is especially interesting as it relates
to the notion of “usable history”. Historical entities can be used for avariety of reasons, for example, for comparison with present entities
or present context, for emphasizing analogy, making predictions
and so on. To analyze the way in which past entities are utilized inconnection to the present ones we first need to distinguish present
from past entities, which is of course not straightforward. We apply
6 Location examples can be seen in Tab. 5.

Table 2: Top 10 entities of persons, groups, and events.
PersonAdolf Hitler
Abraham Lincoln
Donald Trump
Sharon Corr
Bill Clinton
Alan Evans
Barack Obama
NapoleonGeorge Washington
Jerrard Tickell

Group
Jews
U.S. Congress
Royal Navy
U.S. Army
U.S. House of Representatives
Luftwaffe
Royal Air Force
U.S. NavyBBC
Federal Bureau of Investigation

Event
World War II
World War I
Vietnam War
Battles of Saratoga
Battle of Verdun
American Revolutionary War
Korean War
Omaha Beach
Cinco de Mayo
Battle of Gettysburg

a simple division rule, according to which, an entity is regarded
as a past entity if the end of its lifetime7 (e.g., life, event duration)
falls within the last millennium. We are aware that this may beseen arbitrary, yet, we had to set some threshold to carry out theanalysis, and the turnover of the millennium seemed like a good
choice for a temporal landmark. Entity lifetimes were collectedfrom DBpedia 8 .
First, in Tab. 3, we compare the sizes of the past and present entity
sets extracted from our dataset. We can observe that the numberof unique past entities extracted by AIDA and typed by DBpedia
is relatively large constituting roughly half of that of the present
entities. This confirms that our dataset is specifically focused on
history.

and 1 city, while for the past events the corresponding number oflocations is 12. Apparently, when recalling past persons and past
events users tend to also mention where the persons lived or where
the events occurred, thus, “grounding” them in spatial dimension.
Finally, Tab. 5 lists past entities co-occurring with the top frequent locations, from where again the significance of WWII in
relation to collective memories of many countries can be observed.
0.150
Present Event

A

0.135

Past Event

0.120

Present Place

0.105
0.090

Past Place

0.075
Present Group

0.060

Past Group

0.045

Present Person

0.030
0.015

Past Person

Pas

0.000
P
P
P
P
P
P
P
t P resen ast G resen ast P resen ast E resen
ers t P rou t G lac t P ven t E
on ers p
lac tvenr ou eeontpBFigure 6: Conditional probabilities P(B|A) of entity type on x
axis (in column) given the presence of entity type on y axis
(in row).

Table 3: Sizes of the past and present entity sets.

7 For currently valid entities such as currently alive persons the end of their lifetimes is set to thecurrent year.
8 We use “birthDate” and “deathDate” for person entities, “formationDate” and “dissolutionDate” forgroups, and “foundingDate” and “dissolutionDate” for locations.

Present person
Past person

We plot in in Fig. 6 conditional probabilities based on the studied entity types to understand how often the different types appear given another entity of a given type. We can observe that
present places tend to co-occur with entities of any other type. Especially, many past entities tend to appear together with present places.
Furthermore, P(PresentPerson|PresentEvent) has relatively highvalue which is understandable. Finally, if a past place, such as "Nazi
Germany" and "Holy Roman Empire", is in a tweet, past persons
or past events tend to occur as well. This is also expected. Interestingly, past places are often mentioned with present places as well
(supposedly for emphasizing place continuity, spatial relations orfor place-oriented comparisons).
Next, in Tab. 4 we take the top 5 common present persons (column “Entity" in the top part of the table), the top 5 common past
persons (column “Entity" in the middle part of the table), and the
top 5 common past events (column “Entity" in the bottom part of
the table). We then output in columns “1”, “2” and “3” their top
3 most often co-occurring entities from the opposite time frame.
In particular, if the “Entity" column contains past entities then incolumns “1”, “2” and “3” we show their top co-occurring present
entities. Otherwise, we show past entities. When looking at past
persons and past events, one can observe that indeed present places
commonly co-occur with them. For example, for the top 5 past persons, their most common co-occurring entities contain 8 countries

Table 4: Top 5 present, top 5 past persons and top 5 past
events (given in “Entity” column) with their top 3 cooccurring past/present entities of any type. "-" denotes cases
when no corresponding entity can be found.

Past event

Total Person Group Place Event
Size of the past entity set
8,262
6,368
1,257
746
637
Size of the present entity set 16,355 10,858 4,723
340
774

Rank
Entity1
2
3
1
Donald Trump
Adolf Hitler
Abraham Lincoln Nazi Germany
2
Sharon Corr
3
Bill ClintonCharles Frankel
Nazi Germany
World War II
4
Barack Obama Muhammad Ali of Egypt
Adolf Hitler
5
Elizabeth IIJames II of England George Washington
1
Adolf Hitler
Germany
Donald Trump
United States
2
Abraham Lincoln
United States
Donald Trump
Israel3
Alan Evans
4
NapoleonFranceUnited Kingdom
Italy
5 George Washington
United States
Philadelphia
Martin Scorsese
1
World War II
United States
United Kingdom
France2
World War I
United States
CanadaUnited Kingdom
3Vietnam War
United States
CBS
Canada4 Battles of Saratoga
United States
5
Battle of Verdun
FranceGermany
United Kingdom

4.3

Hashtag Analysis

Q. What are the most popular hashtags?
In this section we investigate popularity patterns of hashtags.
Hashtags are commonly used to indicate specific themes of tweets
allowing others to find them. First, Fig. 7 (a) shows the top frequently used hashtags based on their tweet counts. We also list the
top hashtags ranked by the retweet count in Fig. 7 (b) and by the
number of Twitter accounts from which the tweets originate in Fig.
7 (c). From these data, we can observe that #throwbackthursday,Table 5: Top 10 locations and their top 3 co-occurring past
entities of any type. The abbreviated names of entities arefor: Helen Farnsworth Mears (H. F. Mears) and Dominion of
Newfoundland (Dom. of Newfoundland).

25,00020,00015,00010,000
5,000
0

ar
mw
tna
view
y ion
fway istor olut
dd sich nrev
muerica er
amsday eacht
thi tory rbor
hisarlha all
perlinw g
be lin berin
sta em r ory
rem wa isthdl
co ayintodhe
tdi toirreylancoln
his mo linme ahamyr rabhisto ans
us i storiwwitter ust story
twloca inhi
ho ay
d
s
thiler
ay
rsd
hit 2
hu
ww1 acktwwowb
thr ii ay
wwhisdtonotd orythisRank Present locations
1
2
3
1
United States
World War II
Battles of Saratoga
Soviet Union
2 United Kingdom
World War II
James II of England
Soviet Union
3
Germany
Adolf Hitler
John Cudahy
Soviet Union
4
FranceWorld War II
NapoleonLouis XIV of France
5
CanadaWorld War II
World War I
Dom. of Newfoundland
6
Japan
World War II
H. F. Mears
Hideki Tojo
7
Italy
Holy Roman Empire
World War II
Battle of Monte Cassino
8
RussiaWorld War II
Catherine the Great Nicholas I of Russia
9IsraelWorld War II
Nazi Germany
Vichy France
10
India
Vasco Da Gama
Edwin Lutyens
World War II

30,000(a) Tweet count70,00060,00050,000

CATEGORY BASED ANALYSIS
Q. How can past-related tweets be categorized and arranged?
Q. What kinds of semantic categories hashtags can begrouped into?
Q. What are the characteristics of these categories?

5.1

Definitions

In this section we introduce our categorization scheme of hashtags.
The objective is to determine key types of history references. Basedon the proposed categories automatic classifiers could be built toallocate tweets into different classes. Automatically labeling tweets
could be then used for improving content retrieval, recommendation or for further analysis that would lead to better understandingof history-related interest and content sharing. Based on manual
investigation of a large sample of tweets, we propose the following
categories:
1. General History : hashtags used in general to broadly identify history-related tweets that do not fall into any specific
type (e.g., #history, #historyfacts).
2. National or Regional History : hashtags annotating tweets
which describe national or regional histories, for example,

30,00020,00010,0000(b) Retweet count
10,000
8,000
6,000
4,000
2,000
0

rii
wa
rld
wo p warrm
tru tnammbeevierem ar2
we rldwe
wostoir ar s
hi ldw orianory fr t t lwoittersinhis titse
tw sdayrepea
thi tory rbor
hisarlhaies
pe mor
meay
dd 1
ww i
ww2
ww alltbt inwlr
ber
otdldwag
co lin erin lnb osta em linc
rem hamy
ra aab thisd
on
t
i
i
s
e
wwocau lan
y
l
r
ho mo
y
da
meler
u rs
hit ory cktht ahisowbthr

540,000ytormhis ilm he
us entf bert
sil emr e ml i n r e rs t a d w a m brl e
c o r e ma r b o
w ea r l h
pe hm
tst
his
arth ryoytdihist
or
ist r
e
art
w sh hcn
fw ma reayw ot o r yor
his y hist tory
a n isdd ayi inh
t o ds d a y
thi 1
ww
ler
hit
st
u
t b tl o c ar e
ho toris
his ii
an
i
ww
or
2
t
s
w wi t t e r
ay
rsd
tw i
hu
ww ory cktta
h i so w b
thray
o t dt h i s donwhich is representative for a trend among social media sites including Twitter and Facebook to post own past photographs (often fromone’s childhood), gains most attraction across all these dimensionsof popularity. The tweets with hashtag #throwbackthursday andsimilar ones predominantly refer to personal experiences and thesehashtags are used by large number of users. Another observationis that Fig. 7 (a) shows that #onthisday, #otd are present in a relatively large number of tweets and retweets, yet, they are used by
fewer accounts. Unlike #throwbackthursday, these hashtags tend
to be used by specialists (often historians and scientists from relatedareas) who select and disseminate interesting content about the past.
This content then triggers relatively high engagement from otherusers as evidenced by the high retweet popularity of #onthisday,#otd in Fig. 7 (b). We note that the demographics analysis should
provide many more interesting insights regarding typical profilesof posting users, and is going to form the part of our future work.

(c) User countFigure 7: Top 30 popular hashtags by counts of tweets,
retweets and users.

#canadianhistory or #ushistory including also past namesof locations (e.g., #ancientgreece).
3. Facet-focused History : hashtags which relate to particular thematic facets of history (e.g., #sporthistory, #arthistory).
4. General Commemoration : hashtags that contain tweets
commemorating or recalling a certain day or period (often
somehow related to the day of tweet posting), or unspecified entities, such as #onthisday, #otd, #todayweremember,#4yearsago and #rememberthem.
5. Historical Events : hashtags related to particular events inthe past (e.g., #wwi, #sevenyearswar).
6. Historical Entities : hashtags denoting references to specific entities such as persons, organizations or objects (e.g.,#stalin, #napoleon).
Tab. 6 shows our assignment of all the history-related hashtags
found within the dataset into these categories. Note that hashtags

Table 6: Collected hashtags and their categories.
Category
General History

Hashtags
history, historyfacts, oldpicture, historyteacher, memorylane, histoire,
twitterstorians, historicalcontext, colorization, memories, oldphoto, earlymodern, historicalevent, worldhistory, twitterstorian, historynerd,
histedchat, historyfeed, archives, historymatters
National or Re- canadianhistory, ushistory, histoireducanada, jewishhistory, nazigergional History
many, ottoman, cdnhistory, dchistory, cdnhist, thirdreich, tohistory,
mdhistory, bchist, abhistory, vthistory, britishhistory, ancientchina, ancientegypt, ancientgreece, americanhistory, thisiscanadashistory, ottomanempire, ontariohistory, earlyamhistory, japanhistory, japanesehistory, chinesehistory, localhistory
Facet-focused His- blackfacts, histoiremiliterre, wmnshist, arthistory, sporthistory, womtory
enshistory, navalhistory, presidentialhistory, musichistory, militaryhistory, blackhistory, envhist, histmed, wmnhist, todayintennishistory, todayinblackhistory, ibhistory, u2history, historythroughcoins, histSTM,
silentfilm, historyscience, histsci, digitalhistory, foodhistory, histmonast,histnursing, histgender, histtech
General Commem- onthisday, otd, otdh, thisdayin, thisdayinhistory, todayinHistory, tdih,
orationonthisdayinhistory, otdih, 100yearsago, thisday, lessthan100yearsago,
todayweremember, titanicremembranceday, weremember, 100yearsago,
remembering, wewillrememberthem, rememberthem, remembranceday,
historyrepeatsitself, throwbackthursday, tbt
Historical Events 1ww, gulfwar, ColdWar, ww2, ww1, worldwar, worldwarii, vietnamwar,
worldwar2, worldwartwo, veday, worldwarone, greatwar, battleofmidway, holocaust, frenchrevolutionarywar, wwii, wwi, sevenyearswar,
firstworldwar, coldwarhist, gulfwar, battleofokinawa, dday, berlinwall,
ddayoverlord, operationoverlord, fww, pearlharbor, americanrevolution,
6juin44, sww, june61944, victoryineuropeday, dday72, neverforget84,
warof1812, ww1politics, ww1centenary,ww1economy, cw150
Historical Entities stalin, hitler, abrahamlincoln, rudolfhess, napoleon

concerning particular dates or time periods such as #june61944
could be considered as a separate category or could be made apart of General Commemoration. We decided however to placethem under the Historical Events category as they tend to be
used to refer to particular events by their occurrence dates (e.g.,#june61944 referring to the Normandy landings).
In Fig. 8 we show the rate of each category based on the count of
tweets in our dataset. Facet-focused History appears to be quite
common category followed by General History and General
Commemoration. This suggests relatively significant amount ofspecialized history-related content besides broad and general history
related content.
8.8%

22.4%

8.4%

10.4%
20.6%

General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities

29.4%

Figure 8: Distribution of categories.

5.2

Inter-category Similarity

To better understand characteristics of the proposed categories,
we now investigate inter-category affinity by measuring the cooccurrence values between the categories. We use Jaccard coefficient computed as follows:
Score(A, B) =| TA ∩ T B || TA ∪ T B |(2)

where: | · | is the size of a set. TA and TB are the numbers of tweetsthat include hashtags classified in category A and B, respectively.
0.10
General History
0.08

National or Regional History
Facet-focused History

0.06

General Commemoration

0.04

Historical Events

0.02

Historical Entities

His

0.00
Na
Ge Fa
Ge
H
t o r i i s t o r i n e r a l cCe t - f o c t i o n a l n e r a l H
calc
E n t a l E v e o m m eu s e d Ho r R e g i s t o r yities nts
mor isto ionaatio ry
l Hi
n
stor

y

Figure 9: Category co-occurrence.
Fig. 9 plots the obtained co-occurrence values between the categories. We can see that the category General History is truly “general” since the hashtags in this category tend to highly co-occur withhashtags in the other categories. This is the only category that has
relatively high similarity with every other category. Another observation is that the co-occurrence value between Historical Eventsand Historical Entities is quite high. This is because many famous entities in our dataset were involved in key events (e.g., Stalin,
Hitler in WWII). Similarly, when users refer to the past as a generalcommemoration they tend to focus on particular well-known past
events and key persons. Hence, General Commemoration andHistorical Entities/Historical Events hashtags are sometimes
used together. On the other hand, National or Regional History
and Facet-focused History have rarely co-occurring hashtags
with the hashtags of other categories (except for General History). This indicates that they tend to be assigned to relatively
unique and specialized content.

5.3

Temporal Category Analysis

We now investigate temporal references in tweets in relation totheir categories. Our interest is in understanding how similar aretime references included within tweets annotated with the hashtagsof the same category (or in other words, whether tweets under thesame category tend to mention similar or rather different years).
We compute such temporal coherence for each category by comparing the vectors of hashtags in a given category. These are built
based on temporal expressions associated with the hashtags. In
particular, for each hashtag, we construct a vector representing
year scores derived from temporal references within tweets labeledby this hashtag. We first map all the temporal references to theyear level granularity and then compute year scores using Eq. 1.
Such vectors reflect commonly mentioned years for each hashtag.
Pairwise similarities of hashtags falling into the same category arethen computed by using cosine similarity measure and are averaged
to give the final scores displayed in Tab. 7.
Tab. 7 shows that the time-based similarities of hashtags in Historical Entities and General History are relatively high (whencompared to the average value for the entire data shown in the lastrow). Though, we should keep in mind that many tweets underthese hashtags lack time references as indicated by their ratio values

being lower than the average values (see the 4th column). Nevertheless, hashtags under these two categories tend to be relatively
similar to each other in terms of the focused time periods. This actually is not surprising for the Historical Entities category since
several its hashtags represent entities with overlapping lifetimes(at least this is the case in our dataset). Yet for General History it
would mean that there is a good level of agreement in the questionof the most important historical periods and events (e.g., WWI and
WWII).
Table 7: Average cosine similarity for each category basedon years (2nd column), standard deviation of the similarities (3rd column) and the rate of tweets including time references (4th column).
Categories
General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities
Total

5,000

10 4

Similarity
0.75
0.58
0.57
0.54
0.47
0.81
0.56

1916

Std. dev.
0.29
0.35
0.32
0.34
0.31
0.12
0.33

Ratio
0.19
0.20
0.32
0.55
0.18
0.09
0.29

2016

4,000 10 3
10 2
10General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities

1941

3,000
1

1945

2,000 10 0
1800

2025

1925

1,000
0

1800

1850

1900

1950

2000

Figure 10: Distributions of time references in the categories(small inner graph shows the plots in log scale).
We next plot in Fig. 10 the distributions of time references extracted from tweets in each category. Naturally, all the categories
have strong relation to the present (e.g., to the current events orpresent entities) since year 2016 is characterized by a high peak for
all the plots. Historical Events category is strongly focused on
the two critical events of the last century: WWI and WWII. While
General Commemoration has also strong focus on the two wars,
it features actually a very different time plot when compared tothe ones from other categories. Since the common reason for commemorating events are their anniversaries (e.g., #otd) rather than
external triggers such as ongoing events, the tweets under General
Commemoration relate to many diverse years in the past. The pattern of tweeting under this category reflects thus smaller selectivity(or higher diversity) of the collective attention towards time periodsof history.

5.4

Entity-focused Category Analyses

Next, we look into entity distributions to investigate their coherence in each category. In a similar way to the above-discussed

Table 8: Average cosine similarity for each category basedon entities (2nd column), its standard deviation (3rd column)
and the rates of: entities, past entities and present entities,
displayed respectively in the last three columns.
Categories
Sim. Std. dev. All Past Present
General History
0.13
0.19 0.15 0.03 0.05
National or Regional History 0.05
0.16 0.16 0.03 0.08
Facet-focused History
0.12
0.16 0.19 0.05 0.07
General Commemoration 0.19
0.25 0.35 0.09 0.14
Historical Events
0.11
0.18 0.20 0.05 0.08
Historical Entities
0.12
0.12 0.09 0.02 0.03
Total
0.10
0.17
0.14 0.05 0.08

temporal analysis, we first count how many times each entity is
mentioned with a given hashtag in order to create hashtag’s entity
vector. We then calculate pairwise similarities between hashtags
in each category by comparing their vectors and we average thesesimilarities to give the final score per each category.
In Tab. 8 we show per each category the average similarities,
their standard deviations as well as we output the rates of all theentities, the rate of past entities and the ones of present entities. The
entity-focused similarities are in general relatively low indicatingthat hashtags of the same category tend to refer to different entities.
Interestingly, when looking at Tab. 8 we can observe that all thevalues of General Commemoration are higher than the averagevalues for the entire dataset (see the last row). Over one third oftweets under this category contain entities (see 4th column in Tab.
8), while more than half of its tweets contain time references asshown in Tab. 7. Thus, compared with other categories, users tend toinclude more entity names and more temporal expressions into tweets
tagged with the hashtags from General Commemoration category.
This may be distinguishing characteristic of commemorating activity.

5.5

Analysis of Entity and Time Reference
Dispersions

Finally, we study the dispersions of entities and time expressions
for each category. Fig. 11(a) places hashtags according to their entropy values calculated over the distributions of contained entities
and mentioned temporal expressions. We can observe that General Commemoration hashtags (e.g., #otd, #thisday, #otdih)and some of General History hashtags (e.g., #twitterstorians,#history, #colorization) are often characterized by high values of entropy over time references. Hashtags under the General
Commemoration category tend to also have relatively high valuesof entropy over entities (e.g., #otd, #throwbackthursday, #tdih).
On the other hand, Historical Events and Historical Entities
categories have on average low values of the entropy over temporal references, which is understandable given that they focus on
relatively short-lasting events (e.g., #ww1politics, #ww1economy,#june61944). For the same reason, these categories also achieve
low values of entity-based entropy. Furthermore, National or Regional History hashtags (e.g., #britishhistory) tend to haveless varying entities compared to other categories.
Lastly, in the remaining two figures (Figs. 11(b) and (c)) we look
into the relation between the entropy of user distributions and theentropy of entity distributions as well as the relation between theentropy of user distributions and the entropy of temporal references,respectively.
The right hand side corner of Fig. 11(b) is occupied by both General Commemoration and General History hashtags suggestingthat many different users tweet with these hashtags and the users
tend to include references to many different entities. Historical entities in our dataset like #stalin and #hitler tend to be referred toby large number of users and, interestingly, they also have relatively
high values of entity entropies. In Fig. 11(c), General Commemoration tweets contain both many diverse dates and are issued bymany users.

Entropy over time references

7

6

otdh otdih

5colorization
britishhistory

4

botd

3

otd
tdih
twitterstorian
historyarthistory
historyteacher
throwbackthursday
silentfilm
memorylane
thisday

ww1

2

General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities

1
ww1politics
ww1economy

0
-1
-1

0

1
2
3
4
5
6
Entropy over entities(a) Named entities (x axis) vs. time references (y axis)

7

8

10
throwbackthursday
history
memorylane
onthisday
ww1
otdtwitterstorians
histoire

hitlerberlinwall stalin

Entropy over users

8

historynerdoldphoto

6

ancientegypt
4 britishhistory

tdih
todayinblackhistory

otdh

2

General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities

colorization
vthistory

0

ww1economy

-2
-1

0

1

ww1politics

2
3
4
Entropy over entities

5

6

7

8

(b) Named entities (x axis) vs. users (y axis)
10

General History
National or Regional History
Facet-focused History
General Commemoration
Historical Events
Historical Entities

Entropy of users

8
6

hitlerdday

throwbackthursday
history
remembering
thisdayotdonthisdayjune61944
4

tdih
otdh
otdih

2 victoryineuropeday
japanhistory

0-2

historyfeed
earlyamhistory ww1economy
-1

0

1

colorization
bchist vthistory

2
3
4
Entropy over time references

56(c) Time references (x axis) vs. users (y axis)

Figure 11: Entropies related to different categories.

7

6 DISCUSSIONS
6.1 Limitations
Data Collection. We note that the data collection method that
we relied on naturally misses the portion of tweets not tagged byany history-related hashtags. We list here two other approaches
that could be used to collect history-related data: (1) collecting content with temporal expressions pointing to the past, and (2) collecting
content that contains past entities. Every approach is however notwithout its shortcomings. The first method was used in [2] for extracting past references in news articles and relied on the presenceof temporal expressions in text. This however is not always guaranteed for history-related tweets. Indeed, as it can be seen in Tab. 1
the rate of tweets with temporal expressions is 40%, hence, about
4 tweets out of 10 contain tweets any time reference. Thus, thisapproach would miss many relevant tweets resulting in rather low
recall. Similarly, history-related tweets may not mention any past
entity. Indeed, from Tab. 8 (see the column “Past” and the last row)
we can actually notice that the rate of tweets containing at least one
past entity, which can be recognized by the state-of-the-art tools,
is only 0.05 for our dataset. Furthermore, some entities, especially,
more obscure ones may not be even present in any knowledge baseor may not be detectable using standard tools.
We thus assumed in this work an approach that relies on extracting explicit history-focused hashtags and on subjecting them to themanual analysis of tagged content. While such a choice is likely
characterized by a high precision, it may obviously suffer fromlowered recall as discussed before. Yet, in the view of the reported
statistics, we still believe it is superior than collecting tweets basedon contained dates or historical entities. Future work should nevertheless explore more refined approaches for extracting implicit
past-related content; ideally, ones making use of the combinationof all the signals (hashtags, past entities, temporal expressions, etc.).
We also note that while we put great effort into the manual verification of used hashtags as for their relevance to the history, there
is always chance that some might not be fully devoted to the past,or, in general, their selection may cause certain biases.
Different Languages. In the current study we have mainly
focused on English tweets. Further exploration should involve
different languages (e.g., French, Japanese) as well as the crosscomparison of the obtained results. Our current aim is however
not to look into particular aspects and specificities related to different countries but to rather uncover general tendencies. The focus
on English was a natural decision for this initial study due to theinternational role and ubiquity of this language.
Fine-grained Analysis. The present study is quantitative aiming to provide first glimpse into the issue of historical-references in
SNS and to conduct broad exploratory analysis. Qualitative exploration should be later carried for obtaining fine-grained comprehension of the way in which users refer to the past. For example,
the future analysis could examine why some entities are (or arenot) popular as well as could apply sentiment analysis to identify
tendencies in polarity towards particular past events or entities. Another research direction could be the detailed analysis of contexts
in which the past entities or past years are mentioned. These would
necessarily require some sort of manual and qualitative explorationof tweet content.

User-focused Analysis. Our study has exploratory characterand focuses on the shared content first. However, a very interestingquestion is about the type of users who share or are interested inhistorical content in social network services. Due to the time andspace constraints, we have left however the user-focused analysis
for the future work.
Studying Temporal Data Snapshots. Subsequent work should
investigate portions of data collected over different time frames inorder to verify which of the results are specific to the particulartime frames of data generation and which are of general character.
We are currently in the process of collecting more data to perform
such comparative investigation (aiming at the comparison of results
based on tweets from 2016 with ones based on tweets issued in2017).

6.2

Potential Applications

Finally, we list here several example applications that could be
potentially constructed based on the history-related content sharedin Twitter:(1) Recommending past-related content for readers interested instudying history. This could be, for example, popular and interesting content or the content that matches particular userinterests such as tweets under hashtags of the Facet-focused
category that a user is interested in. Through this analysis and other forthcoming ones we could better understandwhat kind of history-related content at what time periods is
becoming attractive to many users.
(2) Creating history-focused chatbots for disseminating historicalknowledge and for entertaining users.
(3) Finding, summarizing and explaining past entities which are
mentioned in relation with the popular present entities to provide analogy and a novel, potentially interesting context for
the latter.
(4) Automatically summarizing and comparing history-related
opinions and popular topics across different regions.
(5) Automatically suggesting hashtags for tweets based on included
entities, years and based on the predicted hashtag categories.

7

CONCLUSIONS & FUTURE WORKIn this paper we have for the first time studied how people explicitly
refer to the history in microblogging and in which contexts suchreferences occur. As mentioned before, history-focused content
recommendation should offer opportunities for the disseminationof interesting and trendy recollections by pushing them to users.
Our analysis does an initial groundwork in establishing how microbloggers conceive of, share and refer to history-related content
such as one on past events and persons. Through this promising
study we hope to shed more light on the way in which historyrelated content is used and shared in microblogging, and by this toencourage subsequent research and development of systems aiming
at educating history. We perform basic study on a coarse level, providing initial observations, identifying several interesting research
directions and suggesting potential applications. Our analysis isnevertheless conducted from multiple perspectives.
Future work will identify (a) differences between general andpersonal histories as well as will look into what makes tweets about

personal history appear interesting. Social media provide novel opportunities to create such personal connections which can help
raise an interest in the significance of historical knowledge beyond the personal experience. Future work will also include (b)geographical analysis of tweets which may point to different cultural
practices with regard to references to the past. As currently most of
the tweets in our dataset originate from the English-speaking partof the world we should contrast these results with ones obtained ondata collected in different languages. Next, we plan also to (c) explore the interdependence between present-day events, their function
as triggers for references to history and the latter’s effect on the interpretation of the present. Finally, as mentioned before, (d) we plan
to study in detail the characteristics of users sharing history-related
content in Twitter such as their demographics, characteristics oftheir followers and followees, and their interaction patters.
Acknowledgments. This work was supported in part by MIC
SCOPE (#171507010) and MEXT Grant-in-Aids (#17H01828 and#17K12792).

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