Since the advent of the COVID-19 pandemic, face-to-face outreach has been gradually replaced by dissemination through social networks. However, the use of social networks for outreaching is not easy since there is an important risk of biasing the communication by converting it into an exclusively one-way channel. In this contribution, we explain how a transmedia perspective can transform a regular activity, as a photo contest for the International Day of Light 2021, into an interactive, comprehensive and functional event in Twitter, Instagram and Twitch.

Polarimetric images are used for the characterization of biological tissues as well as for the early detection of some diseases. Recently, it has been demonstrated that accurate classification models can be constructed based on polarimetric data, such as the Mueller matrix (MM) or different polarimetric metrics resulting from combinations of different MM elements. The choice of polarimetric observables to be used for classifying is usually arbitrary, but mathematical transformations from MM elements to other metrics may benefit or impair the accuracy of the final models. This work presents a thorough comparison of different classification models based on typical machine learning algorithms trained according to different polarimetric metrics, in the search of the most efficient polarimetric basis. The classification models are tested on different biological tissues obtained from a collection of ex-vivo chickens.

In this contribution we show how, in times of pandemic, outreach activities driven by social networks can be a successful alternative to face-to-face events. Designing each activity to properly suit the host platform ensures success.