On network backbone extraction for modeling online collective behavior.

Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.

Debate on online social networks at the time of COVID-19: An Italian case study.

The COVID-19 pandemic is not only having a heavy impact on healthcare but also changing people's habits and the society we live in. Countries such as Italy have enforced a total lockdown lasting several months, with most of the population forced to remain at home. During this time, online social networks, more than ever, have represented an alternative solution for social life, allowing users to interact and debate with each other. Hence, it is of paramount importance to understand the changing use of social networks brought about by the pandemic. In this paper, we analyze how the interaction patterns around popular influencers in Italy changed during the first six months of 2020, within Instagram and Facebook social networks. We collected a large dataset for this group of public figures, including more than 54 million comments on over 140 thousand posts for these months. We analyze and compare engagement on the posts of these influencers and provide quantitative figures for aggregated user activity. We further show the changes in the patterns of usage before and during the lockdown, which demonstrated a growth of activity and sizable daily and weekly variations. We also analyze the user sentiment through the psycholinguistic properties of comments, and the results testified the rapid boom and disappearance of topics related to the pandemic. To support further analyses, we release the anonymized dataset.

Campus traffic and e-Learning during COVID-19 pandemic.

The COVID-19 pandemic led to the adoption of severe measures to counteract the spread of the infection. Social distancing and lockdown measures modified people's habits, while the Internet gained a major role in supporting remote working, e-teaching, online collaboration, gaming, video streaming, etc. All these sudden changes put unprecedented stress on the network. In this paper, we analyze the impact of the lockdown enforcement on the Politecnico di Torino campus network. Right after the school shutdown on the 25th of February, PoliTO deployed its own in-house solution for virtual teaching. Ever since, the university provides about 600 virtual classes daily, serving more than 16 000 students per day. Here, we report a picture of how the pandemic changed PoliTO's network traffic. We first focus on the usage of remote working and collaboration platforms. Given the peculiarity of PoliTO online teaching solution that is hosted in-house, we drill down on the traffic, characterizing both the audience and the network footprint. Overall, we present a snapshot of the abrupt changes seen on campus traffic due to COVID-19, and testify how the Internet has proved robust to successfully cope with challenges while maintaining the university operations.