It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring out-party community engagement, which is likely part of a more general dynamic of feedback-boosted activity.

Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure.Although multiple factors contribute to the participation in activism, their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism.In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).Our framework, based on Stochastic Variational Inference applied to Bayesian Networks, learns the causal pathways over multiple time periods.Distinct from previous studies, our approach uses large-scale and fine-grained longitudinal data (2016 to 2022) to jointly model the roles of sociodemographic makeup, experience of extreme weather events, exposure to climate-related news, and social influence through online interactions.We find that among users interested in climate change, participation in online activist communities is indeed influenced by direct interactions with activists and largely by recent exposure to media coverage of climate protests.Among people aware of climate change, left-leaning people from lower socioeconomic backgrounds are particularly represented in online activist groups.Our findings offer empirical validation for theories of media influence and critical mass, and lay the foundations to inform interventions and future studies to foster public participation in collective action.

We show that a maximum likelihood approach for parameter estimation in agent-based models (ABMs) of opinion dynamics outperforms the typical simulation-based approach. Simulation-based approaches simulate the model repeatedly in search of a set of parameters that generates data similar enough to the observed one. In contrast, likelihood-based approaches derive a likelihood function that connects the unknown parameters to the observed data in a statistically principled way. We compare these two approaches on the well-known bounded-confidence model of opinion dynamics.

Individuals suffering from Opioid Use Disorder and other socially stigmatized conditions often rely on peer support groups to find comfort and motivation while treating their condition. Many may face barriers in accessing peer support treatment, such as shame and social stigma, seclusion, or mobility restrictions. In this study, we quantitatively characterize the potential of the Reddit community in offering these individuals an online alternative to receiving peer support. By analyzing the social interactions of thousands of users during the start of opioid use recovery, we uncover that a particular Reddit community exhibits many characteristics similar to in-person peer support groups, featuring the exchange of support, trust, status, and similar experiences. We find that the supportive behavior of this community nudges users to change their personal behavior, and promotes abandoning opioid-related communities in favor of recovery-oriented relationships. Finally, we find that recognition, acknowledgment, and knowledge exchange are the most relevant factors in sustained engagement with the recovery community. Given this evidence, we suggest that this online community may constitute a complement or a surrogate to peer support groups when in-person meetings are not desirable or possible. Our work might inspire harm reduction policies and interventions to favor successful rehabilitation and is fundamental for future research about the use of digital media for recovery support.

While much attention has been devoted to the causes of opinion change, little is known about its consequences. Our study moves a first step in this direction by looking at Reddit, and in particular to the subreddit r/ChangeMyView, a community dedicated to debating one’s own opinions on a wide array of topics. We analyze changes in online information consumption behavior that arise after a self-reported opinion change, by looking at the participation to a set of sociopolitical communities. We find that people who self-report an opinion change are significantly more likely to change their future participation in a specific subset of those communities. Specifically, there is a significant association (Pearson r = 0.46) between using propaganda-like language in a community and the increase in chances of leaving it. Comparable results (Pearson r = 0.39) hold for the opposite direction, i.e., joining these same communities. In addition, the textual content of the post associated with opinion change is indicative of which communities will be joined or left: a predictive model based only on the text of this post can pinpoint these communities with an average precision@5 of 0.20. Our results establish a link between opinion change and information consumption, and highlight how online propagandistic communities act as a first gateway to internalize a shift in one’s sociopolitical opinion.

Social media has been an important tool in the expansion of the populist message, and it is thought to have contributed to the electoral success of populist parties in the past decade. This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. In particular, we examine commonalities and differences in which audiences they reach and on which issues they focus. By using data from Meta (previously Facebook) Ad Library, we analyze 45k ad campaigns by 39 parties, both populist and mainstream, in Germany, United Kingdom, Italy, Spain, and Poland. While populist parties represent just over 20% of the total expenditure on political ads, they account for 40% of the total impressions—most of which from Eurosceptic and far-right parties—thus hinting at a competitive advantage for populist parties on Facebook. We further find that ads posted by populist parties are more likely to reach male audiences, and sometimes much older ones. In terms of issues, populist politicians focus on monetary policy, state bureaucracy and reforms, and security, while the focus on EU and Brexit is on par with non-populist, mainstream parties. However, issue preferences are largely country-specific, thus supporting the view in political science that populism is a “thin ideology”, that does not have a universal, coherent policy agenda. This study illustrates the usefulness of publicly available advertising data for monitoring the populist outreach to, and engagement with, millions of potential voters, while outlining the limitations of currently available data.

We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a carefully-crafted network null model, we establish which pairs of attributes foster interactions and which ones inhibit them.

Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints---i.e., social network structure and propagations of information---through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. Specifically, echo chambers are modeled as communities that are permeable to pieces of information with similar ideological polarity, and impermeable to information of opposed leaning: this allows discriminating echo chambers from communities that lack a clear ideological alignment.

Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects.

Studying information propagation in social media is an important task with plenty of applications for business and science. Generating realistic synthetic information cascades can help the research community in developing new methods and applications, testing sociological hypotheses and different what-if scenarios by simply changing few parameters. We demonstrate womg, a synthetic data generator which combines topic modeling and a topic-aware propagation model to create realistic information-rich cascades, whose shape depends on many factors, including the topic of the item and its virality, the homophily of the social network, the interests of its users and their social influence.

Opinion dynamics the research field dealing with how people's opinions form and evolve in a social context? traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks.