arXiv subjects

Ahana Biswas

Publications and source records attributed to Ahana Biswas.

5 recordsLinked to original sources

arXiv2026

Regimes of Influence under Trust-Distrust Gating

In many social communities, individuals can simultaneously trust and distrust the same source, a feature standard opinion-dynamics models often ignore. We formalize this ambivalence with Gated Network Credence, in which each directed relationship encodes distinct trust and distrust assessments. These jointly determine "net trust" -- the willingness to rely on a source -- and "uncertainty" -- the conflict between trust and distrust within the same relationship. Agents update beliefs only when net trust exceeds a threshold and uncertainty falls below another, yielding an effective influence graph whose topology drives long-run belief states. Sweeping both thresholds uncovers four regimes -- Accommodating, Evaluative, Friction-averse, and Guarded -- that differ in openness to trust and conflict. Under the modeled family of trust-distrust coupling and prestige-biased trust allocation, the model exhibits a hub-periphery reversal: in the Evaluative regime, high-degree agents contribute more strongly to the limiting belief, whereas in the Friction-averse regime, stringent uncertainty filtering can disproportionately remove hub-directed influence channels and reduce their contribution to the collective equilibrium. This conditional pattern recurs across synthetic network topologies and two empirical network evaluations. Our results show that belief dynamics depend not only on network structure but also on how relational ambivalence between trust and distrust gates interpersonal influence.

physics.soc-ph↗

arXiv2026

Modeling AI Overreliance as a Complex Adaptive System

Whether AI assistance helps or harms a population depends less on the model's accuracy than on whether people rely on it appropriately trusting it when it is right and checking it when it is not. Yet reliance is usually studied one user at a time. We model it as a population process: agents repeatedly solve a task alone, accept an AI answer, or verify it, updating a Bayesian belief about AI quality and, when networked, learning from peers. Four results form one story. The environment sets the baseline: task difficulty and AI quality fix both overreliance and calibration regret. Social learning creates consensus, not overreliance: a mean-preservation theorem, confirmed by a 2*2 topology*tagging design, shows connectivity moves the aggregate only when influence transmits beliefs. Social proof turns reliance into a feedback cascade: visible unverified use suppresses verification and tips the population into collective overreliance. Feedback design can prevent collapse: making verification visible or dampening social proof reverses it. Together, the results frame AI reliance as a computational social dynamics problem, where individual learning, peer observation, and feedback exposure jointly shape whether a population remains calibrated.

cs.CY↗

arXiv2026

From Attention to Dialogue: Does Audience Engagement Reinforce Constructive Cross-Party Communication?

While existing works have emphasized how elites shape mass opinion, we ask whether the reverse also holds: do audience reactions on social media actively shape elite behavior? We examine this question through the lens of cross-partisan interactions (CPIs), which can either foster deliberation or deepen polarization. Using a dataset of over 1.1 million cross-party retweets, replies, and mentions between U.S. state legislators and their audiences on Twitter/X (2020-2021), we first establish baseline patterns of engagement: Democrats gain modest engagement in replies and mentions, while Republicans often face penalties in direct cross-party interactions. Building on this, we show that audience engagement produces a feedback loop that conditions future elite behavior. Following highly visible CPIs, legislators are not only more likely to engage again in cross-talk, but also shift their rhetorical strategies. Engagement consistently promotes causal reasoning, subjective language, and positive-emotion framing in subsequent CPIs. These findings suggest a positive association between audience engagement and constructive cross-party discourse among elites, challenging overly simplified interpretations in the literature that emphasize social media as a primary driver of rising or falling polarization.

cs.SI↗

arXiv2024

Political Elites in the Attention Economy: Visibility Over Civility and Credibility?

Elected officials have privileged roles in public communication. In contrast to national politicians, whose posting content is more likely to be closely scrutinized by a robust ecosystem of nationally focused media outlets, sub-national politicians are more likely to openly disseminate harmful content with limited media scrutiny. In this paper, we analyze the factors that explain the online visibility of over 6.5K unique state legislators in the US and how their visibility might be impacted by posting low-credibility or uncivil content. We conducted a study of posting on Twitter and Facebook (FB) during 2020-21 to analyze how legislators engage with users on these platforms. The results indicate that distributing content with low-credibility information attracts greater attention from users on FB and Twitter for Republicans. Conversely, posting content that is considered uncivil on Twitter receives less attention. A noticeable scarcity of posts containing uncivil content was observed on FB, which may be attributed to the different communication patterns of legislators on these platforms. In most cases, the effect is more pronounced among the most ideologically extreme legislators. Our research explores the influence exerted by state legislators on online political conversations, with Twitter and FB serving as case studies. Furthermore, it sheds light on the differences in the conduct of political actors on these platforms. This study contributes to a better understanding of the role that political figures play in shaping online political discourse.

cs.SI↗

arXiv2023

The Dynamics of Political Narratives During the Russian Invasion of Ukraine

The Russian invasion of Ukraine has elicited a diverse array of responses from nations around the globe. During a global conflict, polarized narratives are spread on social media to sway public opinion. We examine the dynamics of the political narratives surrounding the Russia-Ukraine war during the first two months of the Russian invasion of Ukraine (RU) using the Chinese Twitter space as a case study. Since the beginning of the RU, pro-Chinese-state and anti-Chinese-state users have spread divisive opinions, rumors, and conspiracy theories. We investigate how the pro- and anti-state camps contributed to the evolution of RU-related narratives, as well as how a few influential accounts drove the narrative evolution. We identify pro-state and anti-state actors on Twitter using network analysis and text-based classifiers, and we leverage text analysis, along with the users' social interactions (e.g., retweeting), to extract narrative coordination and evolution. We find evidence that both pro-state and anti-state camps spread propaganda narratives about RU. Our analysis illuminates how actors coordinate to advance particular viewpoints or act against one another in the context of global conflict.

cs.SI↗