Media technologies that primarily orchestrate and convey Facts, answers, meanings, and “knowledge” about things directly in media products, rather than lead people to other sources.
This special issue focuses on “Semantic Media,” which We define as media technologies that primarily orchestrate and convey facts, answers, meanings, and “knowledge” about things directly in media products, rather than lead people to other sources.
Search engines and virtual assistants respond directly to questions based on textual or verbal searches (e.g., “Things to do in Philadelphia?” or “What is the capital of Israel?”).
The special issue is thus dedicated to the often-invisible ways (to the non-specialist) that internet companies are now actively involved in constructing “knowledge” about the world. Organizations like Apple, Google, Microsoft, Facebook, and Amazon extract, curate, and store facts served to users in new and emerging media products. Such processes have significant implications for the politics of knowledge sharing in the future.
We seek papers that examine how design decisions “bake” these facts into the apps and platforms people use daily while focusing on the infrastructures dedicated to orchestrating and presenting this information.
The Goal is to understand the technologies that will drive social and political outcomes when large internet companies become a primary conduit through which people directly acquire an understanding of facts about the world.
We also seek to understand how governments, nonprofit, and nongovernmental organizations engage these media technologies. Semantic media are less about searching for keywords and matches on different websites that are then ranked for people to choose. Instead, they deal with identifying and describing entities (things like people, products, and places) and directing interactions with those entities (actions like purchasing, scheduling, and contacting).
* How do semantic media identify concepts and connect related information about them? * How do companies and organizations produce facts and organize the data? * From where does the data originate? * What do these semantic processes mean for web users and administrators? * What types of gatekeeping or safety checks do companies and organizations perform concerning these facts?
Today’s semantic media have a long history reaching back to the “Semantic Web” project initiated by web inventor Tim Berners-Lee. Yet, media researchers do not adequately cover how companies and organizations implement semantic technologies on platforms relative to their central role. These semantic technologies are in proprietary and open source products, and extensive media platforms are now using them to provide facts and represent knowledge to various publics.
Google’s Knowledge Graph is a database of facts that Google uses to provide quick answers to the public, and such graphs are in use at other companies. At the same time, Wikipedia has a product called Wikidata that similarly stores facts about the world in data formats through which various apps can retrieve the data. Researchers and journalists also use semantic technologies for search engine optimization, fact-checking practices, and data sharing and organization. This special issue thus focuses on such platformized versions of fact production and examines the underlying infrastructures, histories, and modeling techniques used in knowledge representation systems.
We are interested in quantitative, qualitative, and critical approaches and papers that propose new methods, theories, and frameworks. Areas of interest: • The creation or transmission of facts, answers, meanings, definitions, and “knowledge” across media systems and their platforms • Answers from virtual assistants such as Alexa, Siri, Cortana, Bixby, etc. • Answers from search engines such as Google, Bing, Baidu, Yandex, etc. • Products like knowledge panels, infoboxes, carousels, rich results, maps, etc. • Open-source semantic technologies such as Schema.org, Wikidata, etc. • Proprietary semantic technologies such as Google’s Knowledge Graph, etc. • Fact-checking practices for misinformation and disinformation across semantic media platforms • Search engine optimization and semantic search practices • Semantic infrastructure projects such as the semantic web, linked data, etc. • Semantic governance organizations such as the World Wide Web Consortium, etc. • Semantic technologies such as metadata, markup languages, knowledge bases, knowledge graphs, web schemas, applied ontologies, and enterprise semantic software • Semantic, linguistic, and conceptual theories involving rules and logic, theories of meaning, ontology, taxonomy, ideas of truth, social ontology, etc. Timeline: • Extended 1000-word abstracts due Fri July 15 • Decisions out to authors Fri Aug 19 • Full 8000-word manuscript due Fri Nov 18 • Final decisions January 2023 • Submit to journal February 2023 • Publication spring 2023
Send submissions to email@example.com and firstname.lastname@example.org with the subject header “Social Media + Society Special Issue: Semantic Media”
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Although we have to admit that at heart we are not programmers, but Dynabook fanatics and it's been emotionally difficult to see never-ending hacking that never seems to lead to a system that touches the dream. Zen and the Art of Motorcycle Maintenance talks about an ideal where static (conservative, traditional, keep things as they are) and dynamic (progress, experimentation, evolution) form a feedback loop where the good parts of the last evolution become fixed as a static foundation for the next dynamic leap. Unfortunately, in practice we seem to
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