A new, citizen science‐based, aurora observing and reporting platform has been developed with the primary aim of collecting auroral observations made by the general public to further improve the modeling of the aurora. In addition, the real‐time ability of this platform facilitates the combination of citizen science observations with auroral oval models to improve auroral visibility nowcasting. Aurorasaurus provides easily understandable aurora information, basic gamification, and real‐time location‐based notification of verified aurora activity to engage citizen scientists. The Aurorasaurus project is one of only a handful of space weather citizen science projects and can provide useful results for the space weather and citizen science communities. Early results are promising with over 2000 registered users submitting over 1000 aurora observations and verifying over 1700 aurora sightings posted on Twitter.

Twitter is a popular, publicly accessible, social media service that has proven useful in mapping large‐scale events in real time. In this study, for the first time, the use of Twitter as a measure of auroral activity is investigated. Peaks in the number of aurora‐related tweets are found to frequently coincide with geomagnetic disturbances (detection rate of 91%). Additionally, the number of daily aurora‐related tweets is found to strongly correlate with several auroral strength proxies (ravg≈0.7). An examination is made of the bias for location and time of day within Twitter data, and a first‐order correction of these effects is presented. Overall, the results suggest that Twitter can provide both specific details about an individual aurora and accurate real‐time indication of when, and even from where, an aurora is visible.

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In recent years, the notion of the Metaverse has become the focus of a growing body of work in the industry. However, there is no consensus on the conceptualization in academia. To date, much of this attention has revolved around technological challenges.However, what is notably missing from these discussions is a consideration of the human factors and social aspects that are considered more critical challenges within HCI. The aims of this SIG are as follows: Firstly, to provide a platform for researchers and practitioners to engage with the various definitions and the ways in which the Metaverse is developing. Secondly, to discuss the opportunities, challenges, and future possibilities in the context of HCI. This will lay the foundations to build a network for academics interested in the field for future multidisciplinary research relating to the Metaverse. CCS Concepts: • Human-centered computing → Human computer interaction (HCI).

The Aurorasaurus project harnesses volunteer crowdsourcing to identify sightings of an aurora (the "northern/southern lights") posted by citizen scientists on Twitter. Previous studies have demonstrated that aurora sightings can be mined from Twitter with the caveat that there is a large background level of non-sighting tweets, especially during periods of low auroral activity. Aurorasaurus attempts to mitigate this, and thus increase the quality of its Twitter sighting data, by using volunteers to sift through a pre-filtered list of geolocated tweets to verify real-time aurora sightings. In this study, the current implementation of this crowdsourced verification system, including the process of geolocating tweets, is described and its accuracy (which, overall, is found to be 68.4%) is determined. The findings suggest that citizen science volunteers are able to accurately filter out unrelated, spam-like, Twitter data but struggle when filtering out somewhat related, yet undesired, data. The citizen scientists particularly struggle with determining the real-time nature of the sightings, so care must be taken when relying on crowdsourced identification.