BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Eshwar Chandrasekharan

Eshwar Chandrasekharan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Human-Computer InteractionSocial and Information NetworksComputation and Languagecs.CYArtificial Intelligence

Trust snapshot

Quick read

Trust 17 - UnverifiedVerification L1Unclaimed author
4works
0followers
5topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

4 published item(s)

preprint2026arXiv

Algorithmic Cultivation: How Social Media Feeds Shape User Language

Algorithmic feeds have become primary environments for encountering information online, yet while they shape what people see, less is known about how sustained feed exposure shapes how people write. Drawing on Cultivation Theory, we examine whether algorithmic feeds function as online environments that leave measurable traces in users' language. We leverage a large-scale longitudinal dataset of 235M posts by 4M users on Bluesky, and conduct a quasi-experimental study matching an initial pool of 368,513 users exposed to one of three feeds -- News, Science, and Blacksky -- with a pool of 2,001,915 active control users who did not engage with any of these feeds. We examine linguistic evolution across three dimensions: lexico-semantics, psycholinguistics, and topics. We find that users exposed to these feeds show significantly greater stylistic accommodation, semantic alignment, and register formalization than matched controls. These effects vary markedly by feed identity -- Blacksky produces the deepest psycholinguistic restructuring, with significant shifts in cognitive processing, affective expression, and pronoun use, while News and Science effects are largely confined to register and topical focus. Regression models reveal that reposting is the most consistent predictor of linguistic convergence across all feeds, whereas posting and bookmarking show feed-dependent effects, with effects differing more than fourfold across feeds. Our work extends Cultivation Theory beyond belief formation to linguistic behavior, demonstrating that feeds function as persistent linguistic environments that gradually shape what and how users write online. Our work has implications for studying algorithmic influence, online identity formation, and the design and governance of feed-based platforms that mediate online interactions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

VASTU: Value-Aligned Social Toolkit for Online Content Curation

Detecting what content communities value is a foundational challenge for social computing systems -- from feed curation and content ranking to moderation tools and personalized recommendation systems. Yet existing approaches remain fragmented across methodological paradigms, and it remains unclear which methods best capture community-specific notions of value. We introduce VASTU (Value-Aligned Social Toolkit for Online Content Curation), a benchmark and evaluation framework for systematically comparing approaches to detecting community-valued content. VASTU includes a dataset of 75,000 comments from 15 diverse Reddit communities, annotated with community approval labels and rich linguistic features. Using VASTU, we evaluate feature-based models, transformers, prompted and fine-tuned language models under global versus community-specific training regimes. We find that community-specific models consistently outperform global approaches, with fine-tuned transformers achieving the strongest performance (0.72 AUROC). Notably, fine-tuned SLMs (0.65 AUROC) substantially outperform prompted LLMs (0.60 AUROC) despite being 100 times smaller. Counterintuitively, chain-of-thought prompting provides no benefit, and reasoning models perform the worst (0.53 AUROC), suggesting this task requires learning community norms rather than test-time reasoning. By releasing VASTU, we provide a standardized benchmark to advance research on value-aligned sociotechnical systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Harmonizing the Cacophony with MIC: An Affordance-aware Framework for Platform Moderation

Social platforms, and the online communities that use them, are evolving at a rapid pace. As a result, research and development regarding how to moderate online communities is being out-paced. In this paper, we present a novel framework that will allow moderation researchers and practitioners to not only keep-up with the diverse landscape of available platforms and affordances, but also comprehensively represent and analyze moderation on these platforms. The MIC framework represents a social platform's moderation ecosystem using a base-set of 12 platform-level affordances, along with a notion of the inter-affordance relationships that can exist between them. These affordances fall into the three categories -- Members, Infrastructure, and Content -- that are derived from Grimmelmann's taxonomy of moderation, a framework that is already widely accepted and used by the moderation research community. To show how MIC serves as an insightful augmentation of Grimmelmann's lens, we begin by describing how its components have already been shown to impact Grimmelmann's techniques for moderation. Then, we demonstrate the advantages of using an affordance-aware framework like MIC by analyzing several social platforms over the course of two case studies. First, we analyze individual platforms using MIC and demonstrate how MIC can be used to examine the effects of platform changes on the moderation ecosystem and identify potential new challenges in moderation. Next, use MIC to systematically compare three platforms and propose potential moderation mechanisms that each can adapt. Moderation researchers and stakeholders can use such comparisons to uncover where platforms can emulate established, successful and better-studied platforms, as well as learn from the pitfalls other platforms have encountered.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations

Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them-is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.

0 citations0 reviewsNo code linkedOpen access