arXiv subjects

Rishab Nithyanand

Publications and source records attributed to Rishab Nithyanand.

At least 19 recordsLinked to original sources

arXiv2026

Measuring the Semantic Structure and Evolution of Conspiracy Theories

Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings change over time. This gap persists partly because conspiracy-related terms are often treated as stable lexical markers, making it difficult to separate genuine semantic changes from surface-level vocabulary changes. In this paper, we measure the semantic structure and evolution of conspiracy theories in online political discourse. Using 169.9M comments from Reddit's r/politics subreddit spanning 2012--2022, we first demonstrate that conspiracy-related language forms coherent and semantically distinguishable regions of language space, allowing conspiracy theories to be treated as semantic objects. We then track how these objects evolve over time using aligned word embeddings, enabling comparisons of semantic neighborhoods across periods. Our analysis reveals that conspiracy theories evolve non-uniformly, exhibiting patterns of semantic stability, expansion, contraction, and replacement that are not captured by keyword-based approaches alone.

cs.CL↗

arXiv2026

Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies

The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.

cs.CL↗

arXiv2026

On the Suitability of LLM-Driven Agents for Dark Pattern Audits

As LLM-driven agents begin to autonomously navigate the web, their ability to interpret and respond to manipulative interface design becomes critical. A fundamental question that emerges is: can such agents reliably recognize patterns of friction, misdirection, and coercion in interface design (i.e., dark patterns)? We study this question in a setting where the workflows are consequential: website portals associated with the submission of CCPA-related data rights requests. These portals operationalize statutory rights, but they are implemented as interactive interfaces whose design can be structured to facilitate, burden, or subtly discourage the exercise of those rights. We design and deploy an LLM-driven auditing agent capable of end-to-end traversal of rights-request workflows, structured evidence gathering, and classification of potential dark patterns. Across a set of 456 data broker websites, we evaluate: (1) the ability of the agent to consistently locate and complete request flows, (2) the reliability and reproducibility of its dark pattern classifications, and (3) the conditions under which it fails or produces poor judgments. Our findings characterize both the feasibility and the limitations of using LLM-driven agents for scalable dark pattern auditing.

cs.CR↗

arXiv2026

Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy

YouTube has evolved into a powerful platform where creators monetize their influence through affiliate marketing, raising concerns about transparency and ethics, especially when creators fail to disclose their affiliate relationships. Although regulatory agencies like the US Federal Trade Commission (FTC) have issued guidelines to address these issues, non-compliance and consumer harm persist, and the extent of these problems remains unclear. In this paper, we introduce tools, developed with insights from recent advances in Web measurement and NLP research, to examine the state of the affiliate marketing ecosystem on YouTube. We apply these tools to a 10-year dataset of 2 million videos from nearly 540,000 creators, analyzing the prevalence of affiliate marketing on YouTube and the rates of non-compliant behavior. Our findings reveal that affiliate links are widespread, yet disclosure compliance remains low, with most videos failing to meet FTC standards. Furthermore, we analyze the effects of different stakeholders in improving disclosure behavior. Our study suggests that the platform is highly associated with improved compliance through standardized disclosure features. We recommend that regulators and affiliate partners collaborate with platforms to enhance transparency, accountability, and trust in the influencer economy.

cs.CY↗

arXiv2025

ProtoScan: Measuring censorship in IPv6

Internet censorship continues to impact billions of people worldwide, and measurement of it remains an important focus of research. However, most Internet censorship measurements have focused solely on the IPv4 Internet infrastructure. Yet, more clients and servers are available over IPv6: According to Google, over a third of their users now have native IPv6 access. Given the slow-but-steady rate of IPv6 adoption, it is important to understand its impact on censorship. In this paper, we measure and analyze how censorship differs over IPv6 compared to the well-studied IPv4 censorship systems in use today. We perform a comprehensive global study of censorship across an array of commonly censored protocols, including HTTP, DNS, and TLS, on both IPv4 and IPv6, and compare the results. We find that there are several differences in how countries censor IPv6 traffic, both in terms of IPv6 resources, and in where and what blocklists or technologies are deployed on IPv6 networks. Many of these differences are not all-or-nothing: we find that most censors have some capacity to block in IPv6, but are less comprehensive or less reliable compared to their IPv4 censorship systems. Our results suggest that IPv6 offers new areas for censorship circumvention researchers to explore, providing potentially new ways to evade censors. As more users gain access to IPv6 addresses and networks, there will be a need for tools that take advantage of IPv6 techniques and infrastructure to bypass censorship.

cs.NI↗

arXiv2025

Mind the IP Gap: Measuring the impact of IPv6 on DNS censorship

Internet censorship impacts large segments of the Internet, but so far, prior work has focused almost exclusively on performing measurements using IPv4. As the Internet grows, and more users connect, IPv6 is increasingly supported and available to users and servers alike. But despite this steady growth, it remains unclear if the information control systems that implement censorship (firewalls, deep packet inspection, DNS injection, etc) are as effective with IPv6 traffic as they are with IPv4. In this paper, we perform the first global measurement of DNS censorship on the IPv6 Internet. Leveraging a recent technique that allows us to discover IPv6-capable open resolvers (along with their corresponding IPv4 address), we send over 20 million A and AAAA DNS requests to DNS resolvers worldwide, and measure the rate at which they block, at the resolver, network, and country level as well examine the characteristics of blocked domains. We observe that while nearly all censors support blocking IPv6, their policies are inconsistent with and frequently less effective than their IPv4 censorship infrastructure. Our results suggest that supporting IPv6 censorship is not all-or-nothing: many censors support it, but poorly. As a result, these censors may have to expend additional resources to bring IPv6 censorship up to parity with IPv4. In the meantime, this affords censorship circumvention researchers a new opportunity to exploit these differences to evade detection and blocking.

cs.NI↗

arXiv2025

YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets

Personalized recommendation algorithms, like those on YouTube, significantly shape online content consumption. These systems aim to maximize engagement by learning users' preferences and aligning content accordingly but may unintentionally reinforce impulsive and emotional biases. Using a sock-puppet audit methodology, this study examines YouTube's capacity to recognize and reinforce emotional preferences. Simulated user accounts with assigned emotional preferences navigate the platform, selecting videos that align with their assigned preferences and recording subsequent recommendations. Our findings reveal reveal that YouTube amplifies negative emotions, such as anger and grievance, by increasing their prevalence and prominence in recommendations. This reinforcement intensifies over time and persists across contexts. Surprisingly, contextual recommendations often exceed personalized ones in reinforcing emotional alignment. These findings suggest the algorithm amplifies user biases, contributing to emotional filter bubbles and raising concerns about user well-being and societal impacts. The study emphasizes the need for balancing personalization with content diversity and user agency.

cs.SI↗

arXiv2024

C3PA: An Open Dataset of Expert-Annotated and Regulation-Aware Privacy Policies to Enable Scalable Regulatory Compliance Audits

The development of tools and techniques to analyze and extract organizations data habits from privacy policies are critical for scalable regulatory compliance audits. Unfortunately, these tools are becoming increasingly limited in their ability to identify compliance issues and fixes. After all, most were developed using regulation-agnostic datasets of annotated privacy policies obtained from a time before the introduction of landmark privacy regulations such as EUs GDPR and Californias CCPA. In this paper, we describe the first open regulation-aware dataset of expert-annotated privacy policies, C3PA (CCPA Privacy Policy Provision Annotations), aimed to address this challenge. C3PA contains over 48K expert-labeled privacy policy text segments associated with responses to CCPA-specific disclosure mandates from 411 unique organizations. We demonstrate that the C3PA dataset is uniquely suited for aiding automated audits of compliance with CCPA-related disclosure mandates.

cs.CL↗

arXiv2024

Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations

Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experiences and boosts engagement. Among various modalities through which this algorithmically curated content may be delivered, the homepage feed is the most prominent. This paper presents a comprehensive study of how prior user interactions influence the content presented on users' homepage feeds across three major platforms: YouTube, Reddit, and X (formerly Twitter). We use a series of carefully designed experiments to gather data capable of uncovering the influence of specific user interactions on homepage content. This study provides insights into the behaviors of the content curation algorithms used by each platform, how they respond to user interactions, and also uncovers evidence of deprioritization of specific topics.

cs.SI↗

arXiv2024

Towards Multi-Stakeholder Vulnerability Notifications in the Ad-Tech Supply Chain

Online advertising relies on a complex and opaque supply chain that involves multiple stakeholders, including advertisers, publishers, and ad-networks, each with distinct and sometimes conflicting incentives. Recent research has demonstrated the existence of ad-tech supply chain vulnerabilities such as dark pooling, where low-quality publishers bundle their ad inventory with higher-quality ones to mislead advertisers. We investigate the effectiveness of vulnerability notification campaigns aimed at mitigating dark pooling. Prior research on vulnerability notifications have primarily explored single-stakeholder contexts, leaving multi-stakeholder scenarios understudied. There is limited attention to complex multi-stakeholder supply chain ecosystems such as ad-tech supply chain, where resolving vulnerabilities often requires coordinated action across entities with misaligned incentives and interdependent roles. We address this gap by implementing the first online advertising supply chain vulnerability notification pipeline to systematically evaluate the responsiveness of various stakeholders in ad-tech supply chain, including publishers, ad-networks, and advertisers to vulnerability notifications by academics and activists. Our nine-month long automated multi-stakeholder notification study shows that notifications are an effective method for reducing dark pooling vulnerabilities in the online advertising ecosystem, especially when targeted towards ad-networks. Further, the sender reputation does not impact responses to notifications from activists and academics in a statistically different way. Overall, our research fosters industry-scale solution to combat ad inventory fraud and fosters future research on feasibility of multi-stakeholder vulnerability notifications in other supply chain ecosystems.

cs.CR↗

arXiv2024

Algorithmic amplification of biases on Google Search

The evolution of information-seeking processes, driven by search engines like Google, has transformed the access to information people have. This paper investigates how individuals' preexisting attitudes influence the modern information-seeking process, specifically the results presented by Google Search. Through a comprehensive study involving surveys and information-seeking tasks focusing on the topic of abortion, the paper provides four crucial insights: 1) Individuals with opposing attitudes on abortion receive different search results. 2) Individuals express their beliefs in their choice of vocabulary used in formulating the search queries, shaping the outcome of the search. 3) Additionally, the user's search history contributes to divergent results among those with opposing attitudes. 4) Google Search engine reinforces preexisting beliefs in search results. Overall, this study provides insights into the interplay between human biases and algorithmic processes, highlighting the potential for information polarization in modern information-seeking processes.

cs.CY↗

arXiv2023

The Inventory is Dark and Full of Misinformation: Understanding the Abuse of Ad Inventory Pooling in the Ad-Tech Supply Chain

Ad-tech enables publishers to programmatically sell their ad inventory to millions of demand partners through a complex supply chain. Bogus or low quality publishers can exploit the opaque nature of the ad-tech to deceptively monetize their ad inventory. In this paper, we investigate for the first time how misinformation sites subvert the ad-tech transparency standards and pool their ad inventory with unrelated sites to circumvent brand safety protections. We find that a few major ad exchanges are disproportionately responsible for the dark pools that are exploited by misinformation websites. We further find evidence that dark pooling allows misinformation sites to deceptively sell their ad inventory to reputable brands. We conclude with a discussion of potential countermeasures such as better vetting of ad exchange partners, adoption of new ad-tech transparency standards that enable end-to-end validation of the ad-tech supply chain, as well as widespread deployment of independent audits like ours.

cs.CR↗

arXiv2023

Making Sense of Constellations: Methodologies for Understanding Starlink's Scheduling Algorithms

Starlink constellations are currently the largest LEO WAN and have seen considerable interest from the research community. In this paper, we use high-frequency and high-fidelity measurements to uncover evidence of hierarchical traffic controllers in Starlink -- a global controller which allocates satellites to terminals and an on-satellite controller that schedules transmission of user flows. We then devise a novel approach for identifying how satellites are allocated to user terminals. Using data gathered with this approach, we measure the characteristics of the global controller and identify the factors that influence the allocation of satellites to terminals. Finally, we use this data to build a model which approximates Starlink's global scheduler. Our model is able to predict the characteristics of the satellite allocated to a terminal at a specific location and time with reasonably high accuracy and at a rate significantly higher than baseline.

cs.NI↗

arXiv2023

How Auditing Methodologies Can Impact Our Understanding of YouTube's Recommendation Systems

Data generated by audits of social media websites have formed the basis of our understanding of the biases presented in algorithmic content recommendation systems. As legislators around the world are beginning to consider regulating the algorithmic systems that drive online platforms, it is critical to ensure the correctness of these inferred biases. However, as we will show in this paper, doing so is a challenging task for a variety of reasons related to the complexity of configuration parameters associated with the audits that gather data from a specific platform. Focusing specifically on YouTube, we show that conducting audits to make inferences about YouTube's recommendation systems is more methodologically challenging than one might expect. There are many methodological decisions that need to be considered in order to obtain scientifically valid results, and each of these decisions incur costs. For example, should an auditor use (expensive to obtain) logged-in YouTube accounts while gathering recommendations from the algorithm to obtain more accurate inferences? We explore the impact of this and many other decisions and make some startling discoveries about the methodological choices that impact YouTube's recommendations. Taken all together, our research suggests auditing configuration compromises that YouTube auditors and researchers can use to reduce audit overhead, both economically and computationally, without sacrificing accuracy of their inferences. Similarly, we also identify several configuration parameters that have a significant impact on the accuracy of measured inferences and should be carefully considered.

cs.IR↗

arXiv2022

Glowing in the Dark Uncovering IPv6 Address Discovery and Scanning Strategies in the Wild

In this work we identify scanning strategies of IPv6 scanners on the Internet. We offer a unique perspective on the behavior of IPv6 scanners by conducting controlled experiments leveraging a large and unused /56 IPv6 subnet. We selectively make parts of the subnet visible to scanners by hosting applications that make direct or indirect contact with IPv6- capable servers on the Internet. By careful experiment design, we mitigate the effects of hidden variables on scans sent to our /56 subnet and establish causal relationships between IPv6 host activity types and the scanner attention they evoke. We show that IPv6 host activities e.g., Web browsing, membership in the NTP pool and Tor network, cause scanners to send a magnitude higher number of unsolicited IP scans and reverse DNS queries to our subnet than before. DNS scanners focus their scans in narrow regions of the address space where our applications are hosted whereas IP scanners broadly scan the entire subnet. Even after the host activity from our subnet subsides, we observe persistent residual scanning to portions of the address space that previously hosted applications

cs.NI↗

arXiv2022

The Morbid Realities of Social Media: An Investigation into the Misinformation Shared by the Deceased Victims of COVID-19

Social media platforms have had considerable impact on the real world especially during the Covid-19 pandemic. Misinformation related to Covid-19 might have caused significant impact on the population specifically due to its association with dangerous beliefs such as anti-vaccination and Covid denial. In this work, we study a unique dataset of Facebook posts by users who shared and believed in Covid-19 misinformation before succumbing to Covid-19 often resulting in death. We aim to characterize the dominant themes and sources present in the victim's posts along with identifying the role of the platform in handling deadly narratives. Our analysis reveals the overwhelming politicization of Covid-19 through the prevalence of anti-government themes propagated by right-wing political and media ecosystem. Furthermore, we highlight the failures of Facebook's implementation and completeness of soft moderation actions intended to warn users of misinformation. Results from this study bring insights into the responsibility of political elites in shaping public discourse and the platform's role in dampening the reach of harmful misinformation.

cs.CY↗

arXiv2022

ATOM: A Generalizable Technique for Inferring Tracker-Advertiser Data Sharing in the Online Behavioral Advertising Ecosystem

Data sharing between online trackers and advertisers is a key component in online behavioral advertising. This sharing can be facilitated through a variety of processes, including those not observable to the user's browser. The unobservability of these processes limits the ability of researchers and auditors seeking to verify compliance with regulations which require complete disclosure of data sharing partners. Unfortunately, the applicability of existing techniques to make inferences about unobservable data sharing relationships is limited due to their dependence on protocol- or case-specific artifacts of the online behavioral advertising ecosystem (e.g., they work only when client-side header bidding is used for ad delivery or when advertisers perform ad retargeting). As behavioral advertising technologies continue to evolve rapidly, the availability of these artifacts and the effectiveness of transparency solutions dependent on them remain ephemeral. In this paper, we propose a generalizable technique, called ATOM, to infer data sharing relationships between online trackers and advertisers. ATOM is different from prior work in that it is universally applicable -- i.e., independent of ad delivery protocols or availability of artifacts. ATOM leverages the insight that by the very nature of behavioral advertising, ad creatives themselves can be used to infer data sharing between trackers and advertisers -- after all, the topics and brands showcased in an ad are dependent on the data available to the advertiser. Therefore, by selectively blocking trackers and monitoring changes in the characteristics of ads delivered by advertisers, ATOM is able to identify data sharing relationships between trackers and advertisers. The relationships discovered by our implementation of ATOM include those not found using prior approaches and are validated by external sources.

cs.SI↗

arXiv2022

Making a Radical Misogynist: How online social engagement with the Manosphere influences traits of radicalization

The algorithms and the interactions facilitated by online platforms have been used by radical groups to recruit vulnerable individuals to their cause. This has resulted in the sharp growth of violent events and deteriorating online discourse. The Manosphere, a collection of radical anti-feminist communities, is one such group which has attracted attention due to their rapid growth and increasingly violent real world outbursts. In this paper, we examine the social engagements between Reddit users who have participated in feminist discourse and the Manosphere communities on Reddit to understand the process of development of traits associated with the adoption of extremist ideologies. By using existing research on the psychology of radicalization we track how specific types of social engagement with the Manosphere influence the development of traits associated with radicalization. Our findings show that: (1) participation, even by the simple act of joining the Manosphere, has a significant influence on the language and outlook traits of a user, (2) Manosphere elites are extremely effective propagators of radical traits and cause their increase even outside the Manosphere, and (3) community perception can heavily influence a user's behavior. Finally, we examine how our findings can help draft community and platform moderation policies to help mitigate the problem of online radicalization.

cs.SI↗