Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Abstract

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to banning, or dismantling, strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs. unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of antisocial content in online social platforms.

#Scamdemic, #Plandemic, or #Scaredemic: What Parler Social Media Platform Tells Us about COVID-19 Vaccine

“…Further, when moderation is present on social media platforms, dysfunctional conversations cannot easily survive filtration processes. Within any unmoderated network, this filter is less effective but social media platform policies are somewhat responsible for generating negative conversations and networks [62]. While this study shows legitimate concerns of the social media platforms in spreading misinformation, it is also important to highlight its potential as a tool to make individuals more aware of health issues such as vaccine effectiveness.…”

“…This may be due to the unmoderated content policies of Parler as a social media platform. A recent study shows that unmoderated content on social media sites have larger impact than moderated content [62]. In response to concerns about COVID-19 vaccine hesitancy, Facebook, Google, and Twitter began moderating misinformation more actively related to vaccines [63].…”

Hidden order across online extremist movements can be disrupted by nudging collective chemistry

“…The sociological reasons for why and how social movements grow, offline and online, can likely be explained in part by individual-level factors, such as poverty and personality traits, as well as movement-level factors such as ideology, culture, and political aims [10][11][12][13][14][15][16][17][18][19][20] . Yet system-level factors are also crucial, and recent work suggests that these play important roles in the development and countering of online extremism 22,28,29 . We are motivated by the notion that, despite their differences, extremist movements may share common system-level dynamics.…”

Percolation on feature-enriched interconnected systems

“…In this context, the fraction of removed nodes are usually thought of as failures or attacks, and the largest connected component after the perturbation has a functional interpretation assumed to be the part of the network that is still operative. Therefore, percolation in this type of topologies has brought a deeper understanding of the robustness and resilience of real-world networked systems 10 – 13 , as well as, from a fundamental perspective, it has provided new analytical techniques 14 , 15 and interesting phenomenology from the standpoint of statistical physics 16 – 19 .…”

“…Moreover, nodes could be characterized by non-topological properties as well, such as age 29 , biomass 30 , or bank credibility 31 . Hence, similar network attacks can be implemented to test percolation properties of the system when a group of nodes with certain characteristics is removed, for instance, those users of online social platforms generating or spreading hateful content 10 .…”