JCDL 2018 @ Fort Worth, Texas, USA

Digital History meets Microblogging:Analyzing Collective Memories in Twitter○Yasunobu Sumikawa

Tokyo Metropolitan Univ.

Adam Jatowt
Kyoto Univ.

Marten During
Luxembourg Centre forContemporary and Digital History

1Motivation


History is important.
It helps us understand the processes which shape the present
Children have history lessons from elementary school.

Importance of supporting history learningTo support
1.
2.



obtaining knowledge andusing it in real situation.

History education researchers argue that applying historicalknowledge for proposing creative solutions to present issues(historical analogy) is important.
P. Lee. 2005. Historical Literacy: Theory and Research. International Journal of Historical Learning,Teaching and Research 5, 1 (2005), 25–40.

Related WorksHow does our society remember the past?
C.-m. Au Yeung and A. Jatowt. Studying How the Past is Remembered: Towards Computational History
Through Large Scale Text Mining. CIKM ’11, 1231–1240.

Analyzing edit history of Wikipedia’s event articles.
M. Ferron and P. Massa. Collective Memory Building in Wikipedia: The Case of North African Uprisings.
WikiSym ’11, 114–123.

Focusing on memory triggers that
1. cause forgotten
2. vaguely remembered events
to be brought back into social attention.

Research Questions Guiding this Study

What past events do Twitter users focus on?
1.
2.
3.
4.

How do people refer to history in microblogs?
What is the time horizon of history-related references?
How are collective memories expressed in Twitter?
What are the key remembered events and entities?

Benefits of Analyzing History-related Tweets
1.

Understanding collective memories in Wikipedia, news
and social media.
Understanding what types of entities are commonly shared.

2.

Analyzing what each user is interested in history.
Can be useful for1.
Recommendation system
2.
History-focused chatbots

UserAre youinterested inthis history?

BotContributions
1.

2.

3.

4.

Large scale history-related microblog analysis.
Novel findings how collective memories are maintainedand formed.
Proposal of new categorization of historical references.

Outline of novel research directions and potential
applications utilizing history-related content.

Presentation Schedule
Data Collection
 General Analysis
Temporal Analysis
Entity Analysis

Category Based Analysis
Definitions
Temporal Category Analysis

Discussions
 Conclusions
How to Collect History-related Tweets?
History-related hashtags based crawling.


Ex. #history, #WmnHist, #HistoryTeacher, and so on.
We use them as seed hashtags.

Bootstrapping applied to increase the coverageInitial seeds: historians’ definitions

Tweetcrawlingby seeds

HistoricalhashtagselectingStatistics of Data Collection

Num. of historical hashtags

147

Num. of tweets

888,251

Period of timestamps

8 Mar. 2016 ~ 24 Feb. 2017Period of time references*8156 BC ~ 2019

* Years mentioned in tweets

Distribution of Time References

Remembering curve:1.
the recent past is referred to more than the distant past,2.
the memory decay is fastest in the recent years.

Distribution of Time References

4 large peaks.

What Do the Peaks Represent?
Top 5 Entities Mentioned identified by AIDA1.

USA is often mentioned.

2.

1916: WWII

3.

1941 and 1945: WWII

4.

2016: 4 countries
USA, UK, Germany, Canada

What Do the Peaks Represent?
1.

USA is often mentioned.

2.

1916: WWIITalk together WWI and WWII

3.

1941 and 1945: WWII

4.

2016: 4 countries
USA, UK, Germany, Canada* An example of mentioning both of two world wars

What Do the Peaks Represent?
1.

USA is often mentioned

2.

1916: 4 places

3.

1941 and 1945:


4.

USA, UK, Melbourne, France

3 and 4 places

2016: 4 countries
USA, UK, Germany, Canada→ Users often mention places of events occurrenceTop 30 Entities Mentioned in Dataset

22 countries
USA is the mostmentioned entity

Place as a “bridge” between past and present entitiesDefinitions of Past and Present Entities
Past: anything with its “end time” before 2000.


If end time is not recorded, we set it to 2017.

Present: entities with their “end time” after 2000
Past person

1809

1865

Present place

2000

2017

Top 3 Entities Co-occurring with Top 5
Mentioned Places
Place

123USA

WWII

Battles ofSaratoga

Soviet Union

UK

WWII

James II ofEngland

Germany

Adolf Hitler

John Cudahy

FranceWWII

NapoleonLouis XIV ofFrance

CanadaWWII

WWI

Dom. Of
Newfoundland

Soviet Union
Soviet Union

Top 3 Entities Co-occurring with Top 5
Mentioned Places
Place123USA

WWII

Battles ofSaratoga

Soviet Union

UKWWIIJames II ofEngland

Germany

Adolf Hitler

John Cudahy

FranceWWII

NapoleonLouis XIV ofFranceCanada

WWII

WWIDom. Of
Newfoundland

Soviet Union
Soviet Union

How Often Different Kinds of Entities are
Mentioned Together?
Entity type resource: DBpedia


Entity types: place, person, event, group and other.

Conditional probabilities 𝑃(𝐵|𝐴)Each cell represents a conditional probability for x axis.

𝑃(𝐴) =|𝑇𝐴|,|𝑇|

𝑃(𝐵|𝐴) =𝑃(𝑇𝐴 ∧𝑇𝐵)
𝑃(𝑇𝐴)Scores of Conditional Probabilities

Red: higher
Blue: lower

Conditional Probabilities of Present PlaceAll are relatively highConditional Probabilities of Present Person
Present persons tend to be mentioned with present events.

Conditional Probabilities ofPast Person and Event

Past events and persons are mentioned with past places.


Examples of past places: Ottoman Empire, Holy Roman Empire

Why Places and Persons are Mentioned?
Counting number of tweets including hashtags.

1. #history
2. #otd
3. #onthisday
4. #WWII
5. #ThrowBackThursday
6. #WW1
7. #WW2
8. #hitler
9. #ThisDayInHistory
10.#Holocaust

Summary:
4 commemorative days
4 events

1. Locations of event occurrences or historical buildings
2. Areas where people lived

Categorizing Historical References
Hashtag Category

Examples of hashtag

General history (General)#history, #worldhistory,#historicalcontext

National or regional history (National)#ushistory, #ottomanempire,#jewishhistory

Facet-focused history (Facet)#ArtHistory, #DigitalHistory

General commemoration (Comm.)#OnThisDay, #ThrowbackThursday

Historical events (Events)#ww1, #ww2, #ColdWarHist

Historical entities (Entities)#Stalin, #Napoleon,#AbrahamLincoln

Rate of Each Category based on Tweets
Ranking1.
Facet2.
General3.
Comm.

Relatively significant amount of specialized history-related
content besides broad and general history related content.

Similarities of the Categories
Co-occurrence of Different Categories
Measurement: Jaccard coefficient

Number of tweets including hashtags in different classes.

Scores:

Red: highBlue: lowCo-occurrence of Different Categories
General: truly general as all scores are highCo-occurrence of Different Categories
Entities, Events and Comm.: are sometimes used together

many famous entities wereinvolved in key events


Ex., Stalin, Hitler in WWII.

Comm. tend to be used withpast events and key persons.

Temporal Category Analysis
All categories have strong relation to the presentTemporal Category Analysis
Event (yellow line): strongly focusing on WWI and WWII

Temporal Category Analysis
Comm. (black line): many diverse years in the past

Log scale y axis

Discussions | Limitations
1.

Data collection
Difficult to collect history-related tweets if there is no hashtags.
The coverage of hashtags is also limited.
→ Classification (Historical vs. Not tweets) should be done.
2.

User-focused analysis3.

Who posts historical tweets, bots, experts or learners?
Important for recommendation system.

Fine-grained analysisEx.: analyzing popularizes of entities to identify tendencies.

Discussions | Future Works
This study

Future Work ③: Bridging two data

Future Work ①:
More content analysis

1. Qualitative approach
2. Educational approach

Future Work ②: User analysis

Discussions | Potential Applications
1.

Historical contents recommendation
User

2.

Finding, summarizing and explaining past entities
This should be useful for analogy determination
ComputerScience

DigitalHistoryEducation

BotConclusions
We collected and analyzed history-related tweets.
 Our tweet crawling method was based on historicalhashtags.


Bootstrapping procedures

Contributions

Large scale history-related microblogging.
Findings how collective memories are maintained and formed.
Proposal new categorization of historical references.
Outline novel research directions and potential applications utilizing
history-related content.
Thank you!