The paper presents experiences from the rapid introduction and deployment of a data crowdsourcing and data sharing system, motivated by an urgent civic need arising due to the appearance of jellyfish in the swimming coastal areas of western Greece during the summer season. The system was tailored for mobile use and although the pressing need for its deployment negated the time for thorough design, a rich set of lessons and findings emerge from its public use by 13,340 users, over a period of 2 months, reporting over 1,800 times on the condition of 189 local beaches, of which 157 were added to the system by the users themselves. This work touches on issues of usability, motivation, data reliability and public utility of mobile participatory systems and demonstrates that effective outcomes for pubic bodies may rise when systems are designed for the immediate benefit of citizens, by openly exposing the collected data. Most importantly, participation in mobile crowdsourced systems where the data is openly shared between participants is found to be strongly driven by altruistic motives and not by financial or ethical awards. Additionally, the altruistic motives behind participation overcome the added difficulty of participating from a purely mobile use context, and safeguard the quality of the contributed data, reducing the need for complex quality monitoring and safeguarding mechanisms. Finally, the paper identifies barriers and opportunities for the opportunistic participation in mobile crowdsourcing systems during leisure time.

Komninos, A.. (2018).  Pro-social Behaviour in Crowdsourcing Systems: Experiences from a field deployment for beach monitoring.. International Journal of Human-Computer Studies. online first, .. DOI:https://doi.org/10.1016/j.ijhcs.2018.12.001

A multitude of social media APIs from popular services such as Facebook, Twitter and Google, allow programmers access to user generated data that is pertinent to physical venues represented within these services. In our paper, we attempt to address the issue of automatically matching venue representations from these diverse APIs, in order to obtain a more complete representation of user cyber-physical interaction with these venues. We present our work comparing a neural network approach against Nearest Point and Longest Common Substring algorithms.

Kalavrouziotis, V., Komninos A., & Garofalakis J. (2018).  Venue Matching in Social Network APIs using Neural Networks. 22nd ACM Panhellenic Conference on Informatics. Athens, Greece, ACM. DOI:https://doi.org/10.1145/3291533.3291558