Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors' Motivations

Abstract

Structured data peer production (SDPP) platforms like Wikidata play an important role in knowledge production. Compared to traditional peer production platforms like Wikipedia, Wikidata data is more structured and intended to be used by machines, not (directly) by people; end-user interactions with Wikidata often happen through intermediary "invisible machines." Given this distinction, we wanted to understand Wikidata contributor motivations and how they are affected by usage invisibility caused by the machine intermediaries. Through an inductive thematic analysis of 15 interviews, we find that: (i) Wikidata editors take on two archetypes---Architects who define the ontological infrastructure of Wikidata, and Masons who build the database through data entry and editing; (ii) the structured nature of Wikidata reveals novel editor motivations, such as an innate drive for organizational work; (iii) most Wikidata editors have little understanding of how their contributions are used, which may demotivate some. We synthesize these insights to help guide the future design of SDPP platforms in supporting the engagement of different types of editors.

Wikidata as Semantic Infrastructure: Knowledge Representation, Data Labor, and Truth in a More-Than-Technical Project

“…Working on Wikidata, for example, is constrained by database and ontology logics. In contrast, Wikipedia work “is constrained by meeting Wikipedia’s standards for formatting, quality, notability, neutrality, citations, etc., and the results are viewed primarily by humans” (Zhang et al, 2022, p. 2). On Wikidata, much of the work to populate fields is conducted by bots.…”

“…Most of the work to populate content on Wikipedia is conducted by human editors, even though many tasks are performed by bots (Geiger & Halfaker, 2017). Zhang et al (2022) thus classify two types of Wikidata workers: Architects and Masons, who differ on their level of desired cognitive engagement, their preference for collaboration or solo work, and their degree of domain expertise. Whereas Architects develop the ontological infrastructure, “modeling and proposing properties,” Masons “perform data entry work [.…”

“….] e.g., adding or editing items, references, and properties, or linking to external databases” (Zhang et al, 2022, p. 10). Zhang et al found that, unlike Wikipedia editors, Wikidata editors in their study did not know how their contributions were used:End-users often interact with Wikidata contributions through other software-based tools that use all or part of Wikidata as a database.…”

Investigating the potential of the semantic web for education: Exploring Wikidata as a learning platform

“…While reviewing the literature, it was found that the majority of existing research focuses on either technological aspects or ontological aspects of the platform. More Specifically, review of the literature illustrated that researchers use Wikidata to conduct new types of research (Amaral et al, 2021;Colla et al, 2021;Ferradji & Benchikha, 2021;Good et al, 2016;Kaffee, 2016;Konieczny & Klein, 2018;Lemus-Rojas & Odell, 2018;Li et al, 2022;Meier, 2022;Mietchen et al, 2015;Morshed, 2021;Neelam et al, 2022;Rasberry & Mietchen, 2021;Shenoy et al, 2022;Taveekarn et al, 2019;Waagmeester et al, 2020Waagmeester et al, , 2021Zhang et al, 2022). Researchers also use Wikidata to conduct new types of academic analysis in a variety of disciplines (Arnaout et al, 2021;Burgstaller-Muehlbacher et al, 2016;Kaffee et al, 2017;Klein et al, 2016;Lemus-Rojas, n.d.;Pfundner et al, 2015;Putman et al, 2017;Rutz et al, 2021;Scharpf et al, 2021a, b;Turki et al, 2019Turki et al, , 2022a.…”