  Social media are digitalising massive amounts of users' cognitions in terms
of timelines and emotional content. Such Big Data opens unprecedented
opportunities for investigating cognitive phenomena like perception,
personality and information diffusion but requires suitable interpretable
frameworks. Since social media data come from users' minds, worthy candidates
for this challenge are cognitive networks, models of cognition giving structure
to mental conceptual associations. This work outlines how cognitive network
science can open new, quantitative ways for understanding cognition through
online media, like: (i) reconstructing how users semantically and emotionally
frame events with contextual knowledge unavailable to machine learning, (ii)
investigating conceptual salience/prominence through knowledge structure in
social discourse; (iii) studying users' personality traits like
openness-to-experience, curiosity, and creativity through language in posts;
(iv) bridging cognitive/emotional content and social dynamics via multilayer
networks comparing the mindsets of influencers and followers. These
advancements combine cognitive-, network- and computer science to understand
cognitive mechanisms in both digital and real-world settings but come with
limitations concerning representativeness, individual variability and data
integration. Such aspects are discussed along the ethical implications of
manipulating socio-cognitive data. In the future, reading cognitions through
networks and social media can expose cognitive biases amplified by online
platforms and relevantly inform policy making, education and markets about
massive, complex cognitive trends.
