ISSN 2186-7437

NII Shonan Meeting Report

No. 132NII Shonan Meeting Report
Prof. Dr. Gerhard Heyer
Prof. Dr. Mitsuyuki Inaba
Jun.-Prof. Dr. Martin RothMarch 11–14, 2019

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Modelling Cultural Processes
Organizers:
Prof. Dr. Gerhard Heyer (Leipzig University)
Prof. Dr. Mitsuyuki Inaba (Ritsumeikan University)Jun.-Prof. Dr. Martin Roth (Leipzig University)
march 11–14, 2019
When modeling real life dynamics, computer science has up till now mainly
been concerned with business processes. However, with the intrusion of data-rich
digital media and applications such as computer games and web communicationin everbody’s everyday life, the modeling of cultural processes becomes possible
and requires an equally thorough analysis. The future of digitization will to alarge extent depend on how successful computers and computing technologies
can be adapted to different cultures of gathering, processing, altering and creating various types of information. While we assume that business processes canbe described in a strictly logical manner focusing on preconditions and sequential actions, cultural processes such as the formation of public opinion in web
fora, for example, happen to a high degree in parallel without much interdependencies, unpredictability of agent’s decisions, and surprising feedback loops,
just to name a few characteristics. In our workshop we have addressed thischallenge by bringing together computer scientists and media scientists fromJapan and Germany in a truly intercultural discussion. In order to better understand and model cultural processes as complex, self-regulating, media based
communication dynamics, we have looked at digital manifestations of culturalprocesses in various media (video games, SNS, VR/AR), and discussed analyses
that have already been carried out by participants. Different kinds of interpretations from computer science and media science, as well as the German andJapanese perspectives, allowed us to look for and discuss differences and complementary views among the participating scientists. This discussion helped
summarize the state of art and highlight areas where further research is needed.
On the the media and cultural studies side, we have focused on the following
problems found in cultural analysis:
1. Workgroup 1: Identity in the digital age;
2. Workgroup 2: Shifting contents, shifting meanings across media, acrosstime;
3. Workgroup 3: Constructions of culture;
For each of these areas, a team of researchers involving representatives fromthe humanities and computer science discussed and proposed a theoretical andmethodological framework capable of modeling the cultural processes in question. In combination, the results achieved by each team allow us to develop a1new perspective on the global significance and status of digital phenomena intheir construction by actors and critics, as well as questions arising in comparison between different contents and cultures. In effect, our results contribute to
the following problems, among others:• theory, in particular a clear sense of the theoretical basis for our analysis;• data, especially data sources, as well as access and analytical strategies todifferent kinds of data resources such as computer games and log files of
how they have been played, or social media discussions on controversial
topics;• requirements, in particular the development of criteria that help to describe cultural dynamics in general as well as in detail, such as the natureand role of interpretations, or cultural identity;• basic notions of modelling, such as the applicability of business processmodels and network theory, as well as, ideally, new models for specificcontexts; and• tools for modelling, such as different kinds of text mining tools, or visualizations for the dynamics of cultural processes.
The workshop addressed basic aspects of modeling cultural processes and developing software to support cultural dynamics. Apart from the enrichment ofperspectives on our topic, the mixed German and Japanese group allowed us todiscuss differences between the cultural engineering and computer-based modeling that have evolved due to different historical developments and cultural
traditions of perception and action, in particular visualization. As a result, theparticipants have delivered multiple research agendas outlined below, which relate to representations, simulations, and evaluation. They may serve as a basis
for intercultural and comparative media studies and a better development of
future software that involves representations of cultural processes. Ultimately,
the workshop challenged us to think about the status of hermeneutics and therelation between the humanities and computer sciences today, and highlights
the ways in which both scientific perspective may benefit from and stimulate
each other.

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3

Schedule

4Overview of Input-Presentations

Modelling Culture
Shigeo Sugimoto, University of Tsukuba

Between Scylla and Charybdis: Trade-Offs between the
Need for Generic Tools and the Need for Hermeneutic Sensitivity in the Digital Humanities
Cathleen Kantner, University of Stuttgart

Metagaming and Ecologies of Video Games
Hiroshi Yoshida, Ritsumeikan University

Implementing Platforms of Cultural Construction
Mitsuyuki Inaba, Ritsumeikan University

Text Mining as a resource for modelling / NLP Toolbox in
Action: Usecase: Overton Window
Gerhard Heyer and Christian Kahmann, Leipzig University

Exploring algorithmic/platform-centered online community
culturesMartin Roth and Peter Mühleder, Leipzig University / Leipzig University Library

Visualization and Digital Humanities
Stefan Jänicke, Leipzig University

5Workgroup Outcomes
Workgroup 1: Modeling Cultural Processes for Collective
Identities
Akito Inoue, Peter Mühleder, Martin Potthast, Steffi Richter, Martin Roth,Gerik Scheuermann

Preface: Methodology of the work group
In our workgroup, we started with an exploratory discussion of the conceptof identity and the associations all participants had with this concept and its
various dimensions. Following this, we make first attempts at visualizing ourconcepts. The next step was to come up with a series of concrete research questions for interesting cases. Based on these questions, we discussed promisingcase studies and concrete approaches to them. This process resulted in singlingout two interconnected cases, as well was in two general questions we would
like to deal with in our project: a.) gaps in the concept of representation. b.)finding the discourses in the data.

I. Modeling and Representation
Any collaboration between computer scientists, humanities scholars and social
scientists needs to be not only aware of the different perspectives and definitionsof terminology, but should actively be problematized, constantly reflected on,
and made productive in research. In our case, one particularly important gap
between the fields concerns the concept of model and representation we apply.
We organize the terminology in a broad way as follows:

In computer science, modeling it is an inadvertent requirement. In this regard,
the term model has been inductively defined as follows:“To an observer B, an object A* is a model of A to the extent that B can use
A* to answer questions that interest him about A.” (Marvin Minsky, 1965)
This pragmatic way of defining a model is strictly question-oriented: it presumes
there are specific measures that will allow us to verify or check whether the answers produced by a model are actually valid with respect to the real world.
Modeling is a way of purposefully abstracting over the real world systems andprocesses in question, reducing them to the least required, well-defined concepts that can be used to reason, both theoretically and practically. Although
the definition is subjective (with respect to the vantage point of observer B),
6the usefulness of a model transcends its usefulness to an individual in a given
situation to answer a given question at hand, but is due to its generalizability
to other people in other related situations with other related questions.
Since the goal of computer science is to solve problems with the help of a computer, modeling can be seen as a first step towards operationalizing a specific
way of question answering: Can the model also be used by a computer to answer the question, rather than the observer. The model hence must be encoded
in a computer, and that is what is typically called a computer representations(representation, for short). The representation, again, is an abstraction, andat the same time a discretization. Not only the model instance itself, but also
its solution must be represented, and once the answer has been computer fromthe representation of a model instance through algorithmic means, it has to betransferred back into the real world by means of interpretation or action. Representations can take many forms; one that is currently often applied includes
feature representations, where a feature is a function that maps real world objects onto a well-defined set of possible values to capture a specific characteristic
or aspect.
For humanities scholars and many social scientists, representation and, more
specifically, modeling, are political in the sense that they are not only supposed
to approximate reality, but always also construct a specific reality. How I represent a phenomenon or structure also creates that structure or phenomenon in a
distinct way. The positions of researchers range from moderate to more radical
constructivist approaches, which regard reality as real only in the sense of its
representation.
The key difference in understanding and modeling between the two disciplines isthus that computer science is somewhat pragmatically only concerned with how
a certain part of the world can be encoded in a computer, thus differentiating“real” from model world and its representation, whereas humanities researchers
try to understand the world in situ, being part of that same “real” world.
The challenge for the two fields is hence to reflect on the gap between thesetwo concepts in all steps during the research process and consider how they can
inform each other productively.

II. Operationalizing Collective Identity
1. Understanding Identity
Culture is inseparable intertwined with identity (and power) and so - whether welike it or not - Humanities has to deal with it. There are “what”-questions (whatkind of identities/”snapshots”) and (more important for us) “how”-questions,i.e. searching processes of (de-)construction of identities: process of drawing
border between me/we and you/the other(s) (or even stranger(s)), of “in” and“out”. Including somebody (and/ being included in a group/collective) takes
places by articulating something (a topic) and somewhere, and means automatically excluding somebody. Articulation means linking/fixing language elements
in a certain way - whereby “language” is understood in a broad sense: natural language, expert language, algorithms, body language (gestures). A central
question is: WHO has the power to define, what the “right” way of fixing theelements is. . . (the question of hegemony/hegemonial identity (identities)

72. Where can we find expressions
The expression of our identity takes place at various levels. Some people express
it as an action under consciousness. Some people express it as conscious verbal
acts. Moreover, even we use ”word”, some words were expressed consciously
and some words were expressed in under consciousness. They are mixed in it.
The expressed identity affects the identity of others. They contribute to orderthe structure of both personal identity and collective identity.
3. Operationalization
Within a discourse, we are interested in identifying practices that reference(construct) collective identities, both of the self and the other, and/or express
a position or relation to collective identities. Such practices may involve “rational” and affective elements, and different strategies, including for example
irony. In order to identify the collective identities (WHAT) and trace the process of their construction over time (HOW), we need to identify instances inwhich collective identities are referenced, answer the question who is identifying
whom as what how and why. We are specifically interested in measuring levelsof solidarity and discrimination.

III. Research Questions(1) (Political) collective identities in Yahoo News comments(2) Formation of a ‘Gamergate’-style collective identity in JapanThe formation process of the Japanese gamer community is considered to becomparable to the English gamer community in several ways. Controversy about
feminist discourses is constantly happening around otaku culture. By definingthe ”enemy” in the process makes the structure of the argument. Who defines
and who follows it? Today’s discussion doesn’t have an organization, but someof the people persistently take a similar role. Who could get the role gradually?

IV. Interlude: General approaches
The two research problems above lead to two distinctive general approaches on
how a data-driven research process on collective identities can be formulated:
one explorative (identifying collective identities in a data corpus), and one process orientated (understanding the formation of collective identities):
1. given a specific data corpus (e.g. Twitter, News comments, etc.), whatcollective identities emerge/surface within and how do they define/position
themselves? How do they relate to or define other (specific or unspecific(the ‘other’)) collective identities?
2. being interested in a specific collective identity (‘the gamers’, ‘the gamergaters’, . . . ), we want to understand the formation and development of
this identity. How can we identify traces of this collective identity andbuild a data corpus accordingly?

8V. Methodology
The research questions of the previous paragraph will be addressed by using
computer based methods together with human intervention and interpretation.
Several steps are necessary for this endeavor. In addition, the challenges of formulating the concept of identity require an iterative process as earlier decisions
will most likely have to be changed based on insights gained by evaluation andinterpreting the results later.
Data acquisition
The first step in the process is data acquisition. This requires several criticaldecisions on the data sources, followed by the actual retrieval of the data. For
the Yahoo News example, the source platform is clearly defined. However, thedata will be the news text, the comments on the news including user names,
and at least also likes. One can enrich this with recommended articles from theplatform if this is available. For the gamer gate question, the identification ofsources is a major methodological challenge. In principle, all available data insocial media, email threads, comments and likes of news media, etc. is of interest. The identification of the small relevant portion in this data flood is difficult
and will require substantial research including close cooperation between computer science and social science. After the sources are identified, the data willbe crawled and stored.
Regarding Gamergate in Japan, there is a series of medium-sized controversial
topics in Japan. Not one big thing like Gamer Gate. Also, some of them dowith a hashtag, but some of them didn’t. There are several ways to obtain data.
One way is to collect data from Twitter and To check who speaks (or RT, like)
about those topics. We may visualize who speak those topic several times, andhow many people depart/join the discussion. Probably, some people who join
the discussion several times may have a strong position.
Analysis / Methods / Tools / Visualization
According to our current expectation, a larger part of the data processed will be
textual data together with user names, relations like one text commenting another texts and rating data (e.g. likes). Therefore, natural language processing(NLP) and social network analysis will play a major role in the data processing
pipeline. This holds specifically for aspects of NLP like sentiment analysis, andmethods relation to discourse analysis. A critical aspect will be to find methods
on how to derive information on collective identity formation within the observed (electronic images, i.e. user names) of real humans. [Missing: Research
Challenges with respect to choosing methods, missing methods/tools] After theinference of collective identities with regard to the chosen computer representation, visualization will be used to inform social scientists about the outcomeof the computer analysis of the acquired data. This requires an overview ofthe data, the possibility to zoom and filter, and retrieve details going back tooriginal data pieces (e.g. set of textual comments with markers of detected sentiments) underlying the analysis results. [Adapted from Shneiderman’s mantra- Overview first, Zoom and Filter, Details on Demand].
[Missing: expected visualization challenges]

9Interpretation“The purpose of computing is insight not numbers.” [Richard Hamming] Itshould be clearly stated that this working group does strongly believe thatcomputers cannot answer the original research questions. This requires humaninterpretation, including a careful consideration of the underlying assumptions,especially in terms of constructive and computer representation, as well as selecting of data (sources), and all choices along the processing pipeline. We embrace
the use of interactive visualizations to allow effective human access to the data
mass. The information communicated from the data via the visualization will
be processed by the social scientists in an interactive fashion to create insight
into the social phenomena of interest. We hope for a “discussion with the data”
style of interaction that allows the social scientists to generate hypothesis fromthe data by exploration and to test hypothesis with the data. This requires careto avoid statistical traps.
Evaluation• Critical review of results in terms of used methods/method parameters,
data acquisition, data selection; especially checking statistical significance• Critical review of results in terms of structural and computer representation of identity• Checking the computer based reasoning by anectodal evidence based ondata pieces selected from the data manually/other tools or selected bydetail-on-demand possibilities of the visualization
Visualization for Communication
Besides carrying out the actual research, communication of the results in therespective social science community is of high importance. Traditional methods
like scientific presentations, research papers, and potentially books will be used,
but also digital publication including access to underlying data and used tools
are possible. An important part of this communication is a representation of thedata supporting the findings using visualization. Such a visualization may bedifferent from visualizations used to generate the scientific results as the focus
is on communicating a previously known aspect of the underlying data. While
it is well-known in the visualization community that visualizations serve threedifferent purposes, i.e. exploration, hypothesis testing, and communication, thecommunication aspect is usually less researched, making this aspect an interesting aspect of this research agenda. Due to the challenges related to the wholeproject, the final communicating visualizations can not be described in detailat this point, but these visualizations will probably involve overview on a large
parts of the data of relevance to the communicated fact and selected individual
data pieces supporting the claim (e.g. comments showing collective identity ofsome gamers in the gamer gate discussion). Another possibility would be amuch more involved visualization based on the visual representations used in
the actual research process that allows an informed judgment of other social
scientists on the used constructive and computer representation of identity, thewhole digital processing pipeline (i.e. provenance data, access to tools), and theinterpretations.

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Iterating the Pipeline
Experience shows that a challenging subject like collaborative identity and its
manifestations on social platforms will not be studied in a sufficient way in a
first attempt. Therefore, we expect an iterative process involving social scientists and computer scientists that will (hopefully) lead to answers to the researchquestions.

Workgroup 2: Shonan Model for Content Selection & Analysis
Gerhard Heyer, Kazufumi Fukuda, Fabian Schäfer, Cathleen Kantner, Maciej
Piasecki, Chris Biemann, Hiroshi Yoshida, Stefan Jänicke
1. Introduction
In the NII Shonan meeting 132 on modelling cultural processes during March
10-14, 2019, a set of researchers discussed ways to better understand and model
cultural processes as complex, self-regulating, media based communication dynamics. In order to foster the discussion, we set up three working groups eachcombining the different backgrounds and countries of participants to allow for aninterdisciplinary and intercultural exchange about modeling cultural processes:• (WG1) identity in the information age;
• (WG 2) shifting contents, shifting meanings across media and across time;• (WG 3) constructions of culture.
The background of discussion in our working group 2 was the common practice of modelling data and processes by using meta-data on the one hand, andstandard text and data mining on the other. It was agreed that the goal of the
discussion was to find a unified approach that combines both practices of modelling contents, and how they change over time. From this discussion, a model
for content selection, enrichment and analysis emerged that subsumes a range
of use cases from the social sciences and the humanities, supported by computerscience with machine learning and visualization. This model assumes a model ofresearch as a hermeneutic circle. It breaks down datafied research into steps soas to allow us to develop guidelines for operationalization and automatization.
While following established setups of science, its value lies in the explication ofsteps and its recommendation on how to use automation within intellectually
driven research processes, as well as its wide applicability in many fields, including the digital humanities, the social sciences and investigative journalism.
The researcher with her evolving notion of a research project is placed at thecenter of this model. She could conceive of the research project as a question(s),hypothesis or a social concept/phenomenon to be studied. We assume that her
conception will evolve as she progresses. We also assume an iterative process
where she may return to earlier steps and revise decisions based on the evolution11

of the project. The model is represented as a research cycle of 5 steps, illustrated
and presented below. This is a simplification for purposes of interdisciplinary
collaboration in the development of workflows, tools, shared vocabulary and use
cases.
After defining the model and detailing the steps, playing attention to the potential of automation, we will describe a range of use cases that can be subsumed
under this model.
2. The Shonan Model
Now the parts of the process, which is also depicted in figure (1) below, will be
detailed.

To elaborate the concept and verify or falsify research hypotheses, the first stepis the selection of the dataset (1) , which could consist of a corpus of text, media
content or other primary data with associated metadata. Since any rough selection of source materials might produce unexpected results including unwanted
by-catch, the next step is the Exploration and Enrichment (2) of the data. For
this, we recommend the use of unsupervised clustering techniques coupled witha visualization to get an initial overview of the dataset and to quickly be able
to remove outliers (e.g. with topic model visualizations or other fuzzy clustering techniques). This results in a refined, in-domain selection of materials, on
which close reading and coding/annotation (3) is now performed to explicate
the phenomena of interest with respect to the research question. This needs
coffee. While the process of annotation/coding is manual and might involve theidentification of passages and the assignment of hierarchically organized labels,
it can be supported by supervised machine learning techniques that either propose labels based on previous assignments (iterative machine learning) or pickexamples that might likely be assigned the same label (active learning). This12

step corresponds to what is known as coding in the social sciences and annotation in linguistics and can be understood as a process that adds research project
specific metadata on data - be it on entire data items such as documents as wellas on parts such as passages, sentences, words, picture regions etc. These annotations/codes/subjective metadata form the basis for the subsequent synthesis
step (4), where findings are aggregated, visualized and interpreted. This step
feeds into the documentation of the results as well as of the process towards ascholarly publication (5). Further, following the hermeneutic cycle paradigm,
this re-shapes the research question/hypothesis/concept - a step done intellectually by the researcher, triggering another iteration of the entire process, startingwith an altered data selection, different exploration and enrichment, augmented
annotation/coding and a more advanced synthesis. Note that the four steps do
not have to be executed in sequence – it is always possible to revise previous
steps on the basis of such intellectual insight.
2.1 Data Set Building (Corpus Gathering)• Researcher starts with initial intuitions or a vague idea of the concepts
or phenomena to be investigated. She gathers a collection of data withwhich to study the phenomena. This may involve scraping, or selection
from larger collections, crawling the internet, or even creation of data.
Generally, there is a universe of data, where the dataset is selected fromon the basis of search queries or metadata ranges (bulk selection, notindividual selection, to be able to scale to large datasets).
• We assume there is a theory driving the framing of the research questionand hence building of the data set. This might be a naı̈ve theory thatwill be reformulated in the research (which may then lead to a rebuilding/altering of the data set.)
2.2. Exploration and Metadata Enrichment
The computational task at this stage is unsupervised clustering. Topic modelingas one such method can be used to define subsets relevant to a research community. These subsets should be public and contain metadata commonly agreed
upon. Metadata for such subcollections can be retrieved by search engines andaggregated by metadata harvesting.
• Researcher explores the data to understand it and begin to formulate
hypotheses that can be operationalized. Exploring the dataset is also a
way of exploring her own naı̈ve formulation of the project. She is focusedon better specification of the concepts/phenomena and the ways in which
they can be recognised in the data.
– Manual methods - close reading - browsing and looking into the data.
Also, surveying the metadata. For example she might skim the titles,
authors, and dates of the items gathered by some process or she might
randomly read some items closely.
– Automated methods, e.g. unsupervised clustering, possibly interactive and iterative, coupled with visualisation methods, for a targetedand quick bulk selection of relevant material.

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– Visualization aids at illustrating characteristic features of the data
set. Those might be statistical overviews like (interactive) charts andcontentual overviews like tag clouds. The purpose is to deliver the
shape of the current data set in a visual form and to guide the scholar
to feature dimensions with lacking or over-represented information.
Visualizations can further be designed to support annotation.
• Enrichment of the data items (of any granularity) with metadata. She
may be drawing on disciplinary research to add metadata to the items
that will allow her to ask certain questions.
– acquired from the original sources,– manually created following the comparison of concepts/phenomena
against the collected data
• Note that this is where one type of shift in content takes place as metadata
gets layered on content, explicating aspects relevant to the current researchquestion, which might or might not be shared with other researchers asadditional metadata.
2.3. Segmentation and Coding
The computational task at this stage is iterative supervised learning/classification.
Specific concepts (as defined by the researcher) are being applied by way of annotation to a fine grained layer of text (such as words or sentences). Such classes
can be considered to be metadata as well. The generation of annotated text canbe supported by machine learning such as active learning or bootstrapping approaches. - It needs to be noted that the researcher can always interact andcorrect the annotations• Now the researcher begins to analyze (in the sense of break into parts) andthen code or annotate things in the data set that are of research interest.
• She makes decisions about the granularity of data items to be coded (annotated) by concepts significant to the phenomena under investigation.
• Preparation of the coding (annotation) guidelines– iterative process with several experimental phases aimed at achieving
good inter-annotator agreement,– possibly supported by automatic tools, e.g. in a form of test training
and annotation, Active Learning, Bootstrapping, Iterative supervised
learning etc.
– Semi-automatic annotation editors: passage identification and classification / coded annotation. Iterative annotation: suggest codes andspans during the process of linearly annotating a set of documents.
Active learning annotation: use tools that suggests similar passages
in automatically chosen order.
2.4. Synthesis
The goal of this step is to find patterns in the annotated data. It can besupported by a number of approaches and tools, such as statistical analyses,
neural nets, or pattern based analyses. Examples are network analysis, time14

series analysis, anomaly detection. Many of those tools are linked to specificvisualizations and call for interactive fine tuning. Results of this stage may
also generate metadata that can be used for further annotation of text. Resultsof the text and data mining stage need to be interpreted by the researcher.
Visualization at this stage summarises data generated by the text and data
mining stage.
• At this point, the researcher begins to synthesize an interpretation of thephenomena as represented by the annotated data. She will be using toolsthat propose views on the annotated data that may prompt insights into
the phenomenon.
• Coded (annotated) data from the previous step are the basis for constructing (setting up or tuning the )• Distant reading vs quantitative analysis vs computational analysis, e.g.
by the Text and Data Mining methods• Human analysis: Interpretation & Visualization• Visualizations are designed to support hypothesis verification and generation. Distant reading visualizations show summaries of text/data mining
results. Close readings allow inspection of individual data/text elements.
A seamless transition between both perspectives is necessary to build trustin the underlying computational analysis methods: it is imperative thatsynthesized results allow accessing the underlying data to ensure full provenance.
2.5. Documentation
During the whole workflow, metadata have been generated semi-automaticallyfor (1) the definition of subcorpora, (2) the fine-grained annotation of text, and(3) the results of the text and data mining analysis. All of these metadata canbe used to evaluate the research workflow and adjust it to better fit the researchquestion where necessary.
• Finally, the researcher wants to document the insights (interpretations)in ways that can be shared with others. She may be exporting passages(with citations), statistical results, visualizations, and so on for use inpublications.
• She may also want to prepare a study dataset to be published with documentation for others to replicate her findings or for teaching.
• The enriched dataset can become a dataset gathered for another project
thus closing the loop.
• She may want to document the processes of the previous research steps soas to try the same research flow on a different dataset.
It must be emphasized again that this process is not strictly sequential and does
not follow a waterfall model. Rather, the researcher can always use intellectual
revising to enter another loop, or to go back to previous steps in the current
loop, in order to e.g. adjust the data set selection, revise the enrichment, alter
the coding scheme etc. This is symbolized by the dashed line between steps 4
and 1 in the figure above.
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3. Use Cases
Use Case 1: Humanities
1. Corpus Creation*Hathi Trust to study irony in Tudor drama
2. Exploration and enrichment
Adding metadata about the plays from my knowledge of Tudor drama
3. Segmentation and Coding
Searching for known ironic passages in known plays and coding them.
Using tools to find similar passages. Training a machine to propose ironic
passages. Eventually it might be possible for the machine to code the rest.
4. a. Text and Data Mining...
b. Visualization, Interpretation, Quotation
Visualize distribution of passages over time, over authors, and over comedies/tragedies. Begin to form interpretation about Tudor irony.
5. Documentation
Export the visualizations that make my point. Gather statistics and example quotes. Export a process visualization for the book. Export an“archive” that will be published online with the paper/book for others to
use in recapitulating my results.
6. Feedback Loop to research question, theory
Use Case 2: Social Science
1. Corpus Creation
Starting with a research question (rooted in a certain metatheoreticalparadigm and an ongoing scientific controversy in our discipline)• Select relevant data sources in order to trace down the “social fact”
you are interested in like “identity” or “power” (e.g. electronic text
archives, . . . )• Segmentation of texts, importing text in database, metadata, preprocessing• Cleaning the raw corpus (doublets, sampling errors / false positives)
2. Exploration and enrichment
Getting an overview of the corpus (e.g. frequencies, collocations, topic
models, first time series, . . . ) Adding notes or tags (new meta data)
about those preliminary first findings
3. Segmentation and Coding
Searching for highly interesting• Subcorpora• Text passages

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Manual coding / annotation of a (random or layered random) sample oftexts• Coding interface would be helpful• ti-marking function would be helpful (also in order to link it to learning algorithms in the future)• Functionality for continuous intercoder-reliability (on different levels)
would be helpful (compare e-Identity, RECON)• For us also output functions for smaller subcorpora were helpful inorder to process them further in commercial software tools like Atlas.ti or Provalis (not everything can / needs to be reprogrammed in
the project specific tools)
4. a. Text and Data Mining
Yes
b. Visualization, Interpretation, Quotation
Yes.
• Nice visualization helps, however, certain fancy functions cannot be
printed in scientific articles or books (obstacles: moving depictions,
3D, copyright issues for pictures . . . )• Data output functions are helpful in order to allow for the statisticalanalysis of the data generated from the text data in common statistical analysis (e.g. descriptive statistics, regressions, ARIMA timeseries, . . . ) output not just of the graphs but also the relevant coefficients, tests of the preconditions that allow to use a certain statistics. . . .(necessary for documentation of the whole)• Partly statistical analysis gains value from combination with otherdata sources (surveys, event data, demographic or macro-economic)
5. Documentation• Export visualizations• Find the respective typical and / or exceptional quotations• Process visualisation is helpful (however, the whole research process/ cycle usually is not just in one tool)• If done with SPSS or R in highly quantitative studies it becomes
more an more common to publish also the syntax• Publication in scientific journals, papers books nice, if for bothproject partners (IT, SocSc) it results in innovative contributions
to their resp. fields (here it is also good to have agreed in advanceon a common publication strategy: e.g. in which types of journals
which authors will be named first, . . . how to cite each others earlier
work. . . )• Of course, we write our social science text ourselfs :-), however forinterdisciplinary projects it is important to have time for that. Software creation, data work etc. cannot last up to the end of the project
duration. There must be in between results to work with.

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6. Feedback Loop to research question, theory• Wrap up, what worked and what not• Usually a possible reframing of theory, research questions, or consideration of new corpora is not done within one project, but rather inthe planning of the next projects building on recently made experiences
Use Case 3: Disaster Archive
1. Corpus Creation
Collecting photographs and other various resources for the future use by
local governments, schools and research institutions
2. Exploration and enrichment
Analyze the type of collected materials collecting know-hows for metadata
creation Create metadata for each collected material Subject headings,
thesauri don’t exist. Folksonomy? Need resources such as geographicalnames and their changes over time
3. Segmentation and Coding
Keywords, location, temporal information analysis Photographic image
analysis — seems difficult without contextual information
4. a. Text and Data Mining
Yes Technologies required: Image analysis Topic detection Metadata aggregationb. Visualization, Interpretation, Quotation
Metadata visualization to help access
5. Documentation
Annotations for linking the resources with community memory Geographic/temporalannotations...
6. Feedback Loop to research question, theory
Use Case 4: Investigative Journalism (new/s/leak project) www.newsleak.io
1. Corpus Creation
Background corpus given by a set of leaked documents. Subcorpora are
selected by metadata and fulltext search.
2. Exploration and enrichment
Tool shows network of named entities and keywords from sub-corpus.
Users can iteratively refine sub-corpus by including/excluding selectors.
3. Segmentation and Coding
Documents can be tagged, thus marked as belonging to a specific caseunder investigation. Entity labels can be altered, added, allowing for the
annotation of relevant keywords.

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4. a. Text and Data Mining
The entire tool is an interactive data mining environmentb. Visualization, Interpretation, Quotation.. which has an interactive visualization.
5. Documentation
Views and selectors can be saved for later use. In investigative journalism, leaks are the sources for further investigations that typically happen
outside of the data collection, so underpinning of results is not suo muchof an issue in this use case.
6. Feedback Loop to research question, theory
This use-case integrates the feedback loop very tightly. A larger instantiation of feedback loop would be to add more background data on the basisof findings, but this does not match the reality of document leaks, which
are typically one-time events.
Use Case 5: Creating Subject Data for Video Games
1. Corpus Creation
Gathering text and/or metadata from the source of the records. (e.g.
Wikipedia, Wikidata, Mobygames or any type of references)
2. Exploration and enrichment
Getting an overview of the data, Analysing the type of the components(chapter).
3. Segmentation and Coding
Making structured data from the analysis.
4. a. Text and Data Mining
Find the keywords (subjects/topics) for works of video game through theautomatization (e.g. text mining, topic model)b. Visualization, Interpretation, Quotation• Interpretation of the result from the mining is needed for the creating
useful data.
• Some type of visualization (e.g. network or cluster) will support tothe analysis.
5. Documentation• Publish the created data on the online catalogue• Write the text of guideline for explain the spec of the describing
elements (items)• Published the research paper of this analysis (if we can)
6. Feedback Loop to research question, theory
Collect user’s response on online catalog. Evaluate/Critique publisheddata for generating new data or research.
Seems to be another instantiation: Text Mining for Qualitative Data Analysis
CK: Gregor Wiedemann worked with Gerhard Heyer in Leipzig in the eHumanities Project “Postdemokratie und Neoliberalismus”, constructing the Leipzig
Corpus Miner
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Workgroup 3: Tracing constructions of culture throughLeague of Legends
Mitsuyuki Inaba, Peter Chan, Verena Klemm, Thomas Efer, Christian Kahmann
Principal reflections on preconditions for working on and with culture/s: Ethics of researchWorking on and with culture/s has strong ethical implications that we have toreflect at the beginning and during the work-process. The following questions
are crucial:
1. Whose culture do we construct? How and why am I related (e.g. involved,
separated etc) to this culture?
2. What is my own notion of culture? There is a multitude of definitions,some of them ahistorical and “culturalist”, i.e. fixing people inside their culture
and community (ethnicity, religion, traditions, nation state), not regarding theindividual and the possibility of change and development in the course of historical/political processes. We have to avoid “Othering” (classifying human beings
with other characteristics as different, strange) in comparison to one owns person/group.
3. Are hierarchies, power relations (between actor and his object) involved?
(e.g. postcolonial, gender, west-/east etc.) 4. Which motivation is behind myresearch? Which interests and actors are involved (research interest, economicalinterests)?
How can we, in our case study, put these principles into effect?
Use Case: Creation of a set of rules inside League of Legends
Background: Introducing the Game
League of Legends (LoL) is multiplayer online battle arena (MOBA) video game.
The game is played with 2 teams competing versus each other. Each teams is
consisting of 5 players. The goal (win condition) of the game, is destroying theenemy team’s base. Before every game, which takes between 20 to 60 minutes,
each player has to choose a certain character (champion) from a pool of over
100 different champions, with unique look and abilities. During the game theplayers of a team need to communicate their strategy via Chat.

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Instance of Champions
Impact of the gameVideo gaming is an increasingly popular activity in contemporary society, especially among young people. By July 2012, League of Legends was the most
played PC game in North America and Europe in terms of the number of hours
played. In January 2014, over 67 million people played League of Legends permonth. The 2017 World Championship had 60 million unique viewers and a
total prize pool of over 4 million USD. The revenue of the game was: 2018:
1.4; 2017: 2.1 (billion USD). LoL has found impact in fan fictions, Cosplay andmany other areas.
Research Questions
1. How does the creation of a set of rules and punishments influence the behaviour of players?
LoL is a game based on team work. Therefore it is necessary that all members ofa team work jointly together. But very often this is not the case. People harassother players, blame them, insult them or just play bad on purpose, which iscalled “being toxic”, in order to make their own team lose. It’s called toxic,21because an act of of misbehaving seemed to be an infectious action, spreading
towards other players, who were more likely to also misbehave in one of the next
games after having played together with a toxic player. So there was a needfor establishing a set of rules and punishments for misbehaving. This constructof rules was introduced incrementally. After each game every player was allow
to report another player for misbehaviour, specifying at least one the forbidden
things he did. After that, a tribunal known “good behaving” players had tojudge in an anonymised interface on whether and if so how to punish a reported
player.

Interface for reporting a player for misbehaving
We are interested in measuring how players behaviour was influenced over timeby the establishment of the set of rules? Was it possible to re-educate players or
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did the rules just “filter” the toxic players? What are the dynamics of spreading
misbehaviour from one player to another?

Message shown to a player who infringed the set of rules
2. (How) does gaming affect your behaviour in everyday life?
Is there an influence of gaming on your everyday life? Does it make players
more aggressive, more relaxed, more communicative...?!
3. (How) does the image of male and female characters inside the game affectthe perception of genders in the real world?
The characters played in the game are created on a very stereotype basis. A
male character is created to be very masculine most of the time, having lots ofmuscles, carrying heavy weapons, smoking etc. . Whereas a female character isvery attractive, showing lots of skin.

Female chamption:Nidalee

23Male champion: Graves
4. (How) does culture mentality affect dynamics between players/ how to play?
Does culture affect the way of playing?
Is there a correlation between measurable metadata of players like: Nationality,
age , sex, experience in playing. . . and the ingame behaviour. Or is culture completely irrelevant in gaming? Why is it then the case that on competitive levelsouth korean team win most of the time? Is there a culture conflict? Do people
with a certain cultural background have a different way of playing (aggressive,
defensive, relaxed, kind..). We need to keep in mind though, that culture does
not cause behaviour. One is not forced to behave in a certain way, just because
he has a particular nationality.
Further research ideas• What are the incentives of people to play
• How to combine group of Champions? =¿ Balance behaviour• What makes players pay just for the look of virtual avatar? =¿ Happy
community will pay• coop and co gaming to develop social skills? =¿ Games forcing families/people to communicate/ go out?!
What methods appropriate?
We need to know: How to measure behaviour? What are the rules? What arepunishments? Do punishments affect ingame behaviour?
• Data and correlation analysis• Factor analysis• Bayesian statistics
24• machine learning and text mining to measure behaviour• Close reading on chat logs (edited videos from competitions)
What data is needed?
• Chat Logs
• ingame commands + game replay• Data from Forums, magazines, twitch livestreams• interviews, questionnaires
Feasibility?
Which methods are already available?
• Advanced Machine Learning (e.g. Neural Networks) to identify rulebreaking configurations in replay data (3D situational data) and textual
log files (via Text Mining)• Interviews and methods from the social sciences
Which tools resources and libraries are there?
• Twitch stream / youtube replays (collect resources for audience reactions)• In-Game replay files can be obtained (a cooperation with the game studiowould be desirable)• API access and pre-existing datasets– API Explanation:
http://tercenya.github.io/compendium/introduction.html– Static File API “Data Dragon” (e.g. user-created fan images):
https://developer.riotgames.com/static-data.html– Tool for duplication of Static File Contents:
https://github.com/emreloper/serverless-datadragon– Historical Matches dataset:
https://www.kaggle.com/chuckephron/leagueoflegends• On the financial success and business impact:
https://www.crunchbase.com/organization/riot-games• Sandbox Game Server:
https://github.com/LeagueSandbox/GameServer• Game outcome analysis in R:
https://www.kaggle.com/jaytegge/league-of-legends-data-analysis/
notebook25

List of Participants• Prof. Gerhard Heyer, Leipzig University• Prof. Mitsuyuki Inaba, Ritsumeikan University• Prof. Martin Roth, Leipzig University• Dr. Thomas Efer, Leipzig University• Prof. Akito Inoue, Ritsumeikan University• Dr. Kazufumi Fukuda, Ritsumeikan University• Prof. Steffi Richter, Leipzig University• Prof. Fabian Schäfer, University of Erlangen-Nuernberg• Prof. Cathleen Kantner, Stuttgart University• Prof. Gerik Scheuermann, Leipzig University• Prof. Maciej Piasecki, Wroclaw University of Science and Technology• Prof. Chris Biemann, University of Hamburg• Prof. Hiroshi Yoshida, Ritsumeikan University• Prof. Verena Klemm, Leipzig University• Dr. Stefan Jänicke, Leipzig University• Mr. Peter Chan, Stanford University• Prof. Martin Potthast, Leipzig University• Prof. Shigeo Sugimoto, Tsukuba University• Prof. Geoffery Rockwell, University of Alberta• Mr. Peter Mühleder, Leipzig University Library• Mr. Christian Kahmann, Leipzig University

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