Digital research methods (i.e. the use of digital technology to collect and/or analyze research data) have become increasingly relevant over the past decade, allowing humanities researchers to formulate new questions, to find new connections in their data, and/or to synthesize very large amounts of data. Similarly, the use of digital media has grown in relevance for the transmission of research results. Given these advances, the editors at Encounters are creating a new section for digital methods and media, which will make its first appearance in the November 2017 issue.
We are see seeking submissions from researchers that integrate digital methods and/or digital media in their work. Digital methods include (but are not limited to) text analytics, text topic modeling, computer-vision image analysis, GIS analysis, or any network analysis. Digital media include (but are not limited to) software, websites, interactive displays, or data visualizations. We are accepting either paper submissions or digital submissions. We encourage the submissions of papers that feature links to digital works or project websites, and we especially encourage the submission of digital works that can be published as web pages on our website. Digital submissions may include (but are not limited to) interactive narratives and/or interactive data visualizations.
Guidelines for paper submissions: up to 7500 words in pdf or doc or docx format.
Guidelines for digital submissions: must run on an html platform.
Deadline: June 1st, 2017.
For further information on submissions please contact Ana Jofre (firstname.lastname@example.org).
(excerpt from a working document by James Abello, Lev Manovich, Jianbo Gao, Katy Börner, and Tina Eliassi-Rad)
The Arrowhead Problems in Culture Analytics
- Metrics for the study of culture(s)
- Identifying, defining and measuring cultural complexities
- Is culture automatically the result of the evolution of groups (see Hilbert Problem #5)
- What are the fundamental mechanisms for cultural network formation?
- Find algorithms to detect the culturally meaningful topical structure of heterogeneous cultural data (see Hilbert Problem #10)
- Find algorithms to detect culturally meaningful phase transitions in heterogeneous cultural data (see Hilbert Problem #10)
- Identify invariance of offline and online culture(s) to understand their co-evolution
- Measure the impact of culture on health (e.g., there are different narratives/reasons why groups of people do not vaccinate their kids), social conflict, inequality, and the environment (social, env, public goods)
- Identify the densities and velocities of changing areas in culture(s) both online and offline
- Scaling algorithms to all heterogeneous cultural data
- Can one develop a calculus of culture?
- Are there axioms of culture and can one develop a mathematical treatment of these? (see Hilbert Problem #6)
- How do we measure the cultural impact of globalization?
- Properties of cultural systems (what correlations, statistical models, laws exist?)
- Phase transitions (e.g., perception of tattoos, viral spread)
- How to measure, model, and promote cultural diversity
- Hypothesis testing of cultural assumption (e.g., acceleration of culture=perception of time is compressing as time progresses, need listing), validate humanistic approaches for understanding culture
- Validate cultural paradoxes
- AB testing for XXX
- Creation of an open source software package that is accessible, module, adaptable and that allows for reproducibility.
- Scalability (to trillions of records/PB of data)
- Long tail?
- Improve health: Use social media data to predict and prevent episodes of depression
- Reduce substance abuse: Contextualize people’s experiences and behaviors in overall contexts
- Promote global peace by analyzing media reported events and identifying bifurcation points
- Promote stability by decreasing (education, economic, and health) inequalities
- Reduce education inequality: Teach diverse cultures