Opportunistic networks can provide an alternative way to support the diffusion of information in special locations within a city, particularly in crowded spaces, where current wireless technologies can exhibit congestion issues. The efficiency of this diffusion relies mainly on user mobility. In fact, mobility creates the opportunities for contacts and, therefore, for data forwarding. This paper is, therefore, mainly focused on evaluating the dissemination of information in urban scenarios with different crowd densities and renewal rates. Through observation, we obtained real data from a local subway station and a plaza. These data were used, in combination with a pedestrian mobility simulator, to generate people mobility traces. We evaluated the diffusion of messages in these scenarios using the direct and the epidemic protocols. Experimental results show that content diffusion is mainly affected by two factors: degree of mobility and message size. Although it is well known that increasing the node density increases the diffusion rate, we show that, when keeping node density fixed, higher renewal rates cause the delivery ratio to drop. Moreover, we found that the relation between message size and contact duration is also a key factor, demonstrating that large messages can lead to a very low overall performance. Finally, with the aim of increasing the diffusion effectiveness of large messages, we propose an improvement over the Epidemic protocol, named
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