Link recommendation algorithms and dynamics of polarization in online social networks

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., “People you may know” or “Whom to follow” suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive–systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity—defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

Online social networks are increasingly used to access political information (1), engage with political elites, and discuss politics (2). These new communication platforms can benefit democratic processes in several ways: They reduce barriers to information and, subsequently, increase citizen engagement, allow individuals to voice their concerns, help debunk false information, and improve accountability and transparency in political decision-making (3). In principle, individuals can use social media to access ideologically diverse viewpoints and make better-informed decisions (4, 5).At the same time, internet and online social networks reveal a dark side. There are mounting concerns over possible linkages between social media and affective polarization (6, 7). Other than healthy political deliberation, social networks can foster so-called “echo chambers” (8, 9) and “information cocoons” (3, 10) where individuals are only exposed to like-minded peers and homogeneous sources of information, which polarizes attitudes (for counterevidence, see ref. 5). As a result, social media can trigger political sectarianism (6, 7, 11–13) and fuel misinformation (14, 15). Averting the risks of online social networks for political institutions, and potentiating their advantages, requires multidisciplinary approaches and novel methods to understand long-term dynamics on social platforms.That is not an easy task. As pointed out by Woolley and Howard, “to understand contemporary political communication we must now investigate the politics of algorithms and automation” (16). While traditional media outlets are curated by humans, online social media resorts to computer algorithms to personalize contents through automatic filtering. To understand information dynamics in online social networks, one needs to take into account the interrelated subtleties of human decision making e.g., only share specific contents (17), actively engage with other users, follow or befriend particular individuals, interact offline] and the outcomes of automated decisions (e.g., news sorting and recommendation systems) (18, 19). In this regard, much attention has been placed on the role of news filters and sorting (1, 18, 19). Shmargad and Klar (20) provide evidence that algorithms sorting news impact the way users engage with and evaluate political news, likely exacerbating political polarization. Likewise, Levy (21) notes that social media algorithms can substantially affect users’ news consumption habits.While past studies have examined how algorithms may affect which information appears on a person’s newsfeed, and subsequent polarization, social matching (22) or link recommendation (23) algorithms also called user, contact, or people recommender systems (24, 25)] constitute another class of algorithms that can affect the way users engage in (and with) online social networks (examples of such systems in SI Appendix, Fig. S13). These algorithms are implemented to recommend new online connections—“friends” or “followees”—to social network users, based on supposed offline familiarity, likelihood of establishing a future relation, similar interests, or the potential to serve as a source of useful information. Current data provide evidence that link recommendation algorithms impact network topologies and increase network clustering: Daly et al. (26) show that an algorithm recommending friends-of-friends, in an IBM internal social network platform, increases clustering and network modularity. Su et al. (27) analyzed the Twitter graph before and after this platform implemented link recommendation algorithms and show that the “Who To Follow” feature led to a sudden increase in edge growth and the network clustering coefficient. Similarly, Zignani et al. (28) show that, on a small sample of the Facebook graph, the introduction of the “People You May Know” (PYMK) feature led to a sudden increase in the number of links and triangles i.e., motifs comprising three nodes (A, B, C) where the links AB, AC, and BC exist] in the network. The fact that PYMK is responsible for a significant fraction of link creations is alluded to in other works (29). Furthermore, recent work shows, through experiments with real social media users (30) and simulations (31), that link recommendation algorithms can effectively be used as an intervention mechanism to increase networks’ structural diversity (30, 31) and minimize disagreements (32). It is thereby relevant to understand, 1) How do algorithmic link recommendations interplay with opinion formation? and 2) What are the long-term impacts of such algorithms on opinion polarization?Here, we tackle the previous questions from a complex adaptive–systems perspective (33), designing and analyzing a simple model where individuals interact in a dynamic social network. While several models explain the emergence of polarization through link formation based on opinion similarity (34–41) and information exchange (42), here we focus instead on rewiring based on “structural similarity,” which is defined as similarity based on common features that exclusively depend on the network structure (43). This contrasts with the broader concept of homophily, which typically refers to similarity based on common characteristics besides network properties (e.g., opinions, taste, age, background). Compared with rewiring based on homophily—which can also contribute to network fragmentation—rewiring based on structural similarity can be less restrictive in contexts where information about opinions and beliefs is not readily available to individuals before the connection is established. Furthermore, rewiring based on structural similarity is a backbone of link recommendation algorithms e.g., “People you may know” or “Whom to follow” (25) suggestions], which rely on link prediction methods to suggest connections to users (43, 44). Importantly, our model combines three key ingredients: 1) Links are formed according to structural similarity, based on common neighbors, which is one of the simplest link prediction methods (43); this way, we do not assume a priori that individuals with similar opinions are likely to become connected as recent works underline, sorting can be incidental to politics (45, 46)]. 2) Then, to examine opinion updating, we adapt a recent model that covers the interplay of social reinforcement and issue controversy to promote radicalization on social networks (39). 3) Last, we explicitly consider that nodes can react differently to out-group links, either converging in their opinions (10, 47) or polarizing further (48–50).We find that establishing links based on structural similarity alone a process that is likely to be reinforced by link recommendation algorithms—see SI Appendix, Fig. S10 and previous work pointing that such algorithms affect a social network topology and increase their clustering coefficient (26–28)] contributes to opinion polarization. While our model sheds light on the effect of link recommendation algorithms on opinion formation and polarization dynamics, we also offer a justification for polarization to emerge through structural similarity-based rewiring, in the absence of explicit opinion-similarity rewiring (34, 36, 39, 51), confidence-bounds (37, 38, 40), or rewiring based on concordant messages (42).^* Second, we find that the effects of structural similarity-based rewiring are exacerbated if even moderate opinions have high social influence. Finally, we combine nodes that react differently to out-group contacts: “converging” nodes, which converge if exposed to different opinions (10, 21, 52), and “polarizing” nodes, which diverge when exposed to different viewpoints (48–50). We observe that the coexistence of both types of nodes can contribute to moderate opinions. Polarizing nodes develop radical opinions, and converging nodes, influenced by opposing viewpoints, yield more temperate ones. However, again, link recommendation algorithms impact this process: Given the existence of communities isolated to a greater degree through link recommendation, converging nodes may find it harder to access diverse viewpoints, which, in general, contributes to increasing the adoption of extreme opinions.