SciCom Wiki: Fact-Checking and FAIR Knowledge Distribution for Scientific Videos and Podcasts

TL;DR

This work tackles the challenge of misinformation in science communication by non-textual media (videos and podcasts) and the lack of scalable FAIR infrastructure. It introduces the SciCom Wiki, a Wikibase-based, open-source platform with a Linked Data Wiki and a Full Text Wiki to curate research media and enable collaborative fact-checking, reinforced by a neurosymbolic fact-checking pipeline that converts media into knowledge graphs aligned to ground truth such as IPCC statements. The approach is validated through extensive requirements gathering (53 participants and 11 interviews) and a dual evaluation of the wiki (14 stakeholders) plus a separate fact-checking tool evaluation (10 experts and 43 users), revealing strong demand and usable design while highlighting scalability and interoperability gaps. Collectively, the work demonstrates that a FAIR, collaborative digital library for non-textual media can support scalable ground-truth synthesis and veracity assessment, forming a central knowledge node for the SciCom KI and underscoring the need for broad, ongoing participation to counter the information flood.

Abstract

Democratic societies need accessible, reliable information. Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI) curating non-textual media is still fragmented and not adequately equipped to scale against the content flood. Our work sets out to support the SciCom KI with a central, collaborative platform, the SciCom Wiki, to facilitate FAIR (findable, accessible, interoperable, reusable) media representation and the fact-checking of their content, particularly for videos and podcasts. Building an open-source service system centered around Wikibase, we survey requirements from 53 stakeholders, refine these in 11 interviews, and evaluate our prototype based on these requirements with another 14 participants. To address the most requested feature, fact-checking, we developed a neurosymbolic computational fact-checking approach, converting heterogenous media into knowledge graphs. This increases machine-readability and allows comparing statements against equally represented ground-truth. Our computational fact-checking tool was iteratively evaluated through 10 expert interviews, a public user survey with 43 participants verified the necessity and usability of our tool. Overall, our findings identified several needs to systematically support the SciCom KI. The SciCom Wiki, as a FAIR digital library complementing our neurosymbolic computational fact-checking framework, was found suitable to address the raised requirements. Further, we identified that the SciCom KI is severely underdeveloped regarding FAIR knowledge and related systems facilitating its collaborative creation and curation. Our system can provide a central knowledge node, yet a collaborative effort is required to scale against the imminent (mis-)information flood.

Figures (10)

• Figure 1: Extension of the Science Communication KI (SciCom KI) with our systems.
• Figure 2: Results of 53 participants assessing the importance of Sci KI media criteria (above) and features (below), ranked by their importance averaged over all responses.
• Figure 3: Knowledge graph representation of a media item on wikibase (left), accessed by the Dashboard and displayed as a detail page (right).
• Figure 4: View when searching for climate-related media, incl. filter recommendation.
• Figure 5: UEQ benchmark results across six UX scales (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty). The colored bands represent qualitative rating categories ("Bad" to "Excellent"). The black dots indicate the mean scores for each scale, the whiskers indicate the $95\%$ confidence intervals for the scores.