Daniel M. Romero

Aparna Ananthasubramaniam, Yufei 'Louise' Zhu, David Jurgens et al.

The diffusion of culture online is theorized to be influenced by many interacting social factors (e.g., network and identity). However, most existing computational cascade models consider just a single factor (e.g., network or identity). This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades. We curate a new dataset of 1,337 hashtags representing cultural innovation online, develop a 10-factor evaluation framework for comparing empirical and simulated cascades, and show that a combined network+identity model better simulates hashtag cascades than network- or identity-only counterfactuals. We also explore heterogeneity in performance: While a combined network+identity model best predicts the popularity of cascades, a network-only model best predicts cascade growth and an identity-only model best predicts adopter composition. The network+identity model has the highest comparative advantage among hashtags used for expressing racial or regional identity and talking about sports or news. In fact, we are able to predict what combination of network and/or identity best models each hashtag and use this to further improve performance. Our results show the utility of models incorporating the interactions of network, identity, and other social factors in the diffusion of hashtags in social media.

Sidney Xiang, Nicholas David, Dallas Card et al.

Scientific innovation often comes from researchers who pivot across disciplines. However, prior work found that established researchers face productivity penalties when pivoting. Here, we investigate the consequences of pivoting at the beginning of a research career -- doctoral admissions -- when the benefits of importing new ideas might outweigh the switching costs. Using applications to all PhD programs at a large research-intensive university between 2013-2023, we find that pivoters (those applying to programs outside their prior disciplinary training) have lower GPAs and standardized test scores than non-pivoters. Yet even conditional on these predictors of admission, pivoters are 1.3 percentage points less likely to be admitted. Examining applicants who applied to multiple programs in the same admissions cycle provides suggestive evidence that the admissions pivot penalty is causal. This penalty is significantly smaller for applicants who secure a recommendation from someone within the target discipline. Among those admitted and enrolled, pivoters are 12.9 percentage points less likely to graduate and do not show superior publication performance on average or at the tail. Our results reveal the substantial costs of disciplinary pivoting even at the outset of research careers, which constrain the flow of new ideas into research communities.

Aparna Ananthasubramaniam, David Jurgens, Daniel M. Romero

Adoption of cultural innovation (e.g., music, beliefs, language) is often geographically correlated, with adopters largely residing within the boundaries of relatively few well-studied, socially significant areas. These cultural regions are often hypothesized to be the result of either (i) identity performance driving the adoption of cultural innovation, or (ii) homophily in the networks underlying diffusion. In this study, we show that demographic identity and network topology are both required to model the diffusion of innovation, as they play complementary roles in producing its spatial properties. We develop an agent-based model of cultural adoption, and validate geographic patterns of transmission in our model against a novel dataset of innovative words that we identify from a 10% sample of Twitter. Using our model, we are able to directly compare a combined network + identity model of diffusion to simulated network-only and identity-only counterfactuals -- allowing us to test the separate and combined roles of network and identity. While social scientists often treat either network or identity as the core social structure in modeling culture change, we show that key geographic properties of diffusion actually depend on both factors as each one influences different mechanisms of diffusion. Specifically, the network principally drives spread among urban counties via weak-tie diffusion, while identity plays a disproportionate role in transmission among rural counties via strong-tie diffusion. Diffusion between urban and rural areas, a key component in innovation diffusing nationally, requires both network and identity. Our work suggests that models must integrate both factors in order to understand and reproduce the adoption of innovation.

Edward L. Platt, Daniel M. Romero

Targeted attacks against network infrastructure are notoriously difficult to guard against. In the case of communication networks, such attacks can leave users vulnerable to censorship and surveillance, even when cryptography is used. Much of the existing work on network fault-tolerance focuses on random faults and does not apply to adversarial faults (attacks). Centralized networks have single points of failure by definition, leading to a growing popularity in decentralized architectures and protocols for greater fault-tolerance. However, centralized network structure can arise even when protocols are decentralized. Despite their decentralized protocols, the Internet and World-Wide Web have been shown both theoretically and historically to be highly susceptible to attack, in part due to emergent structural centralization. When single points of failure exist, they are potentially vulnerable to non-technological (i.e., coercive) attacks, suggesting the importance of a structural approach to attack-tolerance. We show how the assumption of partial trust transitivity, while more realistic than the assumption underlying webs of trust, can be used to quantify the effective redundancy of a network as a function of trust transitivity. We also prove that the effective redundancy of the wrap-around butterfly topology increases exponentially with trust transitivity and describe a novel concurrent multipath routing algorithm for constructing paths to utilize that redundancy. When portions of network structure can be dictated our results can be used to create scalable, attack-tolerant infrastructures. More generally, our results provide a theoretical formalism for evaluating the effects of network structure on adversarial fault-tolerance.

Daniel M. Romero, Roderick I. Swaab, Brian Uzzi et al.

The current research used the contexts of U.S. presidential debates and negotiations to examine whether matching the linguistic style of an opponent in a two-party exchange affects the reactions of third-party observers. Building off communication accommodation theory (CAT), interaction alignment theory (IAT), and processing fluency, we propose that language style matching (LSM) will improve subsequent third-party evaluations because matching an opponent's linguistic style reflects greater perspective taking and will make one's arguments easier to process. In contrast, research on status inferences predicts that LSM will negatively impact third-party evaluations because LSM implies followership. We conduct two studies to test these competing hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between 1976 and 2012 and found that candidates who matched their opponent's linguistic style increased their standing in the polls. Study 2 demonstrated a causal relationship between LSM and third-party observer evaluations using negotiation transcripts.