arXiv subjects

Tanvi Bajpai

Publications and source records attributed to Tanvi Bajpai.

7 recordsLinked to original sources

arXiv2025

Towards a Better Modqueue: Designing for Diversity Across Moderator Objectives and Workflows

Reddit relies on volunteer moderators to enforce community rules, configure tools, and review flagged content. This labor is substantial, worth millions in unpaid effort, and increasingly hard to sustain as communities grow. While recent updates to Reddit's modqueue emphasize efficiency and reducing redundancy, recent research shows that moderators use the interface in varied ways, value objectives beyond throughput (such as fairness and accuracy), and often resist features that disrupt workflows. In this paper, we survey 106 active Reddit moderators to examine the objectives they bring to their modqueue work and the kinds of interventions they consider helpful. Our findings highlight wide variation in values and workflows, with no single objective beyond accuracy dominating, and different perspectives on which interventions are useful. To address this diversity, we introduce a simulation-based approach that can complement empirical findings by probing tradeoffs and testing potential interventions, and provide design recommendations based on our findings.

cs.HC↗

arXiv2025

"Think about it like you're a firefighter": Understanding How Reddit Moderators Use the Modqueue

On Reddit, the moderation queue (modqueue) is a primary interface for moderators to review reported content. Despite its central role in Reddit's community-reliant moderation model, little is known about how moderators actually use it in practice. To address this gap, we surveyed 110 moderators, who collectively oversee more than 400 unique subreddits, and asked them about their usage of the modqueue. Modqueue practices vary widely: some moderators approach it as a daily checklist, others as a hub to infer community-wide patterns, and many still find the queue insufficient to inform their moderation decisions. We also identify persistent challenges around review coordination, inconsistent interface signals, and reliance on third-party tools. Taken together, we show the modqueue is neither a one-size-fits-all solution nor sufficient on its own for supporting moderator review. Our work highlights design opportunities for more modular, integrated, and customizable platform infrastructures that better support the diversity of moderator workflows.

cs.HC↗

arXiv2025

Covering a Few Submodular Constraints and Applications

We consider the problem of covering multiple submodular constraints. Given a finite ground set $N$, a cost function $c: N \rightarrow \mathbb{R}_+$, $r$ monotone submodular functions $f_1,f_2,\ldots,f_r$ over $N$ and requirements $b_1,b_2,\ldots,b_r$ the goal is to find a minimum cost subset $S \subseteq N$ such that $f_i(S) \ge b_i$ for $1 \le i \le r$. When $r=1$ this is the well-known Submodular Set Cover problem. Previous work \cite{chekuri2022covering} considered the setting when $r$ is large and developed bi-criteria approximation algorithms, and approximation algorithms for the important special case when each $f_i$ is a weighted coverage function. These are fairly general models and capture several concrete and interesting problems as special cases. The approximation ratios for these problem are at least $Ω(\log r)$ which is unavoidable when $r$ is part of the input. In this paper, motivated by some recent applications, we consider the problem when $r$ is a \emph{fixed constant} and obtain two main results. For covering multiple submodular constraints we obtain a randomized bi-criteria approximation algorithm that for any given integer $α\ge 1$ outputs a set $S$ such that $f_i(S) \ge$ $(1-1/e^α-ε)b_i$ for each $i \in [r]$ and $\mathbb{E}[c(S)] \le (1+ε)α\cdot \sf{OPT}$. Second, when the $f_i$ are weighted coverage functions from a deletion-closed set system we obtain a $(1+ε)$ $(\frac{e}{e-1})$ $(1+β)$-approximation where $β$ is the approximation ratio for the underlying set cover instances via the natural LP. These results show that one can obtain nearly as good an approximation for any fixed $r$ as what one would achieve for $r=1$. We mention some applications that follow easily from these general results and anticipate more in the future.

cs.DS↗

arXiv2023

Bicriteria Approximation Algorithms for Priority Matroid Median

Fairness considerations have motivated new clustering problems and algorithms in recent years. In this paper we consider the Priority Matroid Median problem which generalizes the Priority $k$-Median problem that has recently been studied. The input consists of a set of facilities $\mathcal{F}$ and a set of clients $\mathcal{C}$ that lie in a metric space $(\mathcal{F} \cup \mathcal{C},d)$, and a matroid $\mathcal{M}=(\mathcal{F},\mathcal{I})$ over the facilities. In addition each client $j$ has a specified radius $r_j \ge 0$ and each facility $i \in \mathcal{F}$ has an opening cost $f_i$. The goal is to choose a subset $S \subseteq \mathcal{F}$ of facilities to minimize the $\sum_{i \in \mathcal{F}} f_i + \sum_{j \in \mathcal{C}} d(j,S)$ subject to two constraints: (i) $S$ is an independent set in $\mathcal{M}$ (that is $S \in \mathcal{I}$) and (ii) for each client $j$, its distance to an open facility is at most $r_j$ (that is, $d(j,S) \le r_j$). For this problem we describe the first bicriteria $(c_1,c_2)$ approximations for fixed constants $c_1,c_2$: the radius constraints of the clients are violated by at most a factor of $c_1$ and the objective cost is at most $c_2$ times the optimum cost. We also improve the previously known bicriteria approximation for the uniform radius setting ($r_j := L$ $\forall j \in \mathcal{C}$).

cs.DS↗

arXiv2022

Revisiting Priority $k$-Center: Fairness and Outliers

In the Priority $k$-Center problem, the input consists of a metric space $(X,d)$, an integer $k$, and for each point $v \in X$ a priority radius $r(v)$. The goal is to choose $k$-centers $S \subseteq X$ to minimize $\max_{v \in X} \frac{1}{r(v)} d(v,S)$. If all $r(v)$'s are uniform, one obtains the $k$-Center problem. Plesník [Plesník, Disc. Appl. Math. 1987] introduced the Priority $k$-Center problem and gave a $2$-approximation algorithm matching the best possible algorithm for $k$-Center. We show how the problem is related to two different notions of fair clustering [Harris et al., NeurIPS 2018; Jung et al., FORC 2020]. Motivated by these developments we revisit the problem and, in our main technical contribution, develop a framework that yields constant factor approximation algorithms for Priority $k$-Center with outliers. Our framework extends to generalizations of Priority $k$-Center to matroid and knapsack constraints, and as a corollary, also yields algorithms with fairness guarantees in the lottery model of Harris et al [Harris et al, JMLR 2019].

cs.DS↗

arXiv2022

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.

cs.HC↗

arXiv2019

A new system-wide diversity measure for recommendations with efficient algorithms

Recommender systems often operate on item catalogs clustered by genres, and user bases that have natural clusterings into user types by demographic or psychographic attributes. Prior work on system-wide diversity has mainly focused on defining intent-aware metrics among such categories and maximizing relevance of the resulting recommendations, but has not combined the notions of diversity from the two point of views of items and users. In this work, (1) we introduce two new system-wide diversity metrics to simultaneously address the problems of diversifying the categories of items that each user sees, diversifying the types of users that each item is shown, and maintaining high recommendation quality. We model this as a subgraph selection problem on the bipartite graph of candidate recommendations between users and items. (2) In the case of disjoint item categories and user types, we show that the resulting problems can be solved exactly in polynomial time, by a reduction to a minimum cost flow problem. (3) In the case of non-disjoint categories and user types, we prove NP-completeness of the objective and present efficient approximation algorithms using the submodularity of the objective. (4) Finally, we validate the effectiveness of our algorithms on the MovieLens-1m and Netflix datasets, and show that algorithms designed for our objective also perform well on sales diversity metrics, and even some intent-aware diversity metrics. Our experimental results justify the validity of our new composite diversity metrics.

cs.IR↗