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On 30 October 2020, an earthquake of Mw 6.9 hit the Aegean coasts of Turkey and Greece. The epicentre was some 14 km northeast of Avlakia on Samos Island, and 25 km southwest of Seferihisar, Turkey, triggering also a tsunami. The event has been followed by >4,000 aftershocks up to Mw 5.2 The Earthquake Engineering Field Investigation Team (EEFIT) has immediately gathered a team to conduct a hybrid reconnaissance study, bringing together remote and field investigation techniques. The mission took place between 16 November and 17 December, inclusive of three sets of field study carried out by the field crews for building damage assessment in the affected areas in Turkey and Greece under the coordination of the remote team. The mission also aimed to assess the viability of alternative data sources for an appraisal of the future viability of hybrid missions. This paper summarises the mission setup and findings, and discusses the benefits of and difficulties encountered during this hybrid reconnaissance activity.

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquake-related studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: “Felt.” The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III–VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI.