arXiv subjects

Kathleen M. Carley

Publications and source records attributed to Kathleen M. Carley.

At least 19 recordsLinked to original sources

arXiv2026

Modeling Social Dynamics with an LLM-Enabled Agent Based Network-Dynamic (LAND) Model

Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance and coordinate behavior. This paper uses the the GhostField architecture, a hybrid LLM-Enabled Agent Based Network-Dynamic (LAND) model as a social simulation framework to build the AuraSight scenario. In the AuraSight scenario, 314,244 heterogeneous cyber social agents and human actors exchange 529,327 messages over 30 days surrounding a fictional international song-writing contest. We methodologically examine emergent social dynamics across four analytical layers: ego-network topology, semantic network evolution, coordination dynamics and influence dynamics. Our results show how generated social simulations do also produce social dynamics, and how the dynamics of coordination and influence emerge not from individual agents but from the recursive interaction between network topology and narrative exchange.

cs.AI↗

arXiv2026

Beyond Effectiveness: A Multi-Criteria Framework for Comparing Practical Socio-Technical Interventions

Designers and policymakers in sociotechnical domains like content moderation, privacy interfaces, recommender systems and beyond, must choose among a growing menu of proposed interventions, but typically lack a principled basis for comparing them. Prior work tends to evaluate interventions individually and mostly along the effectiveness criteria, while implementation constraints such as cost, effort and feasibility are often considered separately. We present a multi-criteria framework for evaluating sociotechnical interventions. This framework is instantiated through the case of misinformation, a domain of intense focus for proposed countermeasures. We survey $N=39$ researchers on 40 operationalized interventions across five evaluative criteria: political feasibility, effectiveness, user acceptance, cost, and implementation effort. We find that the interventions that experts judge to be the most effective are not always the most acceptable to the public or the most feasible to implement. We also discuss how this tension has implications for the design of sociotechnical interventions beyond misinformation, and offer a decision framework for practitioners navigating the trade-offs of sociotechnical interventions.

cs.AI↗

arXiv2026

Public support for misinformation interventions depends on perceived fairness, effectiveness, and intrusiveness

The proliferation of misinformation on social media has concerning possible consequences, such as the degradation of democratic norms. While recent research on countering misinformation has largely focused on analyzing the effectiveness of interventions, the factors associated with public support for these interventions have received little attention. We asked 1,010 American social media users to rate their support for and perceptions of ten misinformation interventions implemented by the government or social media companies. Our results indicate that the perceived fairness of the intervention is the most important factor associated with support, followed by the perceived effectiveness of that intervention and then the intrusiveness. Interventions that supported user agency and transparency, such as labeling content or fact-checking ads, were more popular than those that involved moderating or removing content or accounts. We found some demographic differences in support levels, with Democrats and women supporting interventions more and rating them as more fair, more effective, and less intrusive than Republicans and men do, respectively. It is critical to understand which interventions are supported and how they are perceived, as public opinion can play a key role in the rollout and effectiveness of policies.

cs.CY↗

arXiv2026

User-Reported Misinformation Exposure Across Social Media Platforms

In this study, we surveyed users for their perception of misinformation exposure across social media platforms. Such perceived exposure is important because individuals' beliefs about how often they encounter false information can shape their trust in institutions, platforms, and even their friends. In a survey of 1,010 United States residents, we found that perceived exposure to misinformation varies substantially across platforms and is only moderately correlated with the frequency of platform use. A much larger percentage of participants also reported being exposed to misinformation from the public feed than from known contacts. Based on these results, we propose governance strategies across three categories of platform types: discovery, interpersonal, and discourse. This work offers insight into users' perceptions of social media misinformation and a corresponding research agenda for governance.

cs.HC↗

arXiv2026

The Structural Influence of Low-Credibility Narratives During the COVID-19 Vaccine Rollout

This work examines the structural influence of low-credibility narratives and the comparative role of automated accounts (bots) versus human users on social media platforms. To more accurately quantify the structural influence of a narrative on social media, this study proposes two novel metrics: (1) Appeal, which measures the network-weighted popularity of a message; and (2) Scope, which measures an author's message popularity-weighted network penetration. Applying these metrics, this study analyzes 5.8 million messages from X that contain low-credibility narratives regarding COVID-19 vaccine across three distinct temporal stages: Pre-Vaccine, Vaccine Launch, and Post-Launch. The results demonstrate that across all timeframes, human-distributed low-credibility narratives achieved higher structural influence compared to those generated by automated accounts. Furthermore, statistical analysis reveals a significant conditional temporal effect: human-driven low-credibility narratives attained their highest Appeal and Scope during the focal Vaccine Launch week, whereas automated accounts maximized their Appeal and Scope during the highly uncertain Pre-Vaccine period. These findings highlight the distinct operational capacities of automated and organic accounts, illustrating how the Appeal and Scope of low-credibility narratives is moderated by the lifecycle stages of critical public events.

cs.SI↗

arXiv2026

Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems

Agentic AI systems are increasingly deployed not in isolation, but inside social environments populated by other agents and humans, such as in social media platforms, multi-agent LLM pipelines or autonomous robotics fleets. In these settings, system behavior emerges not from individual agents alone, but from the multi-agent interactions over time. Emergent dynamics of individuals in a social group have been long studied by social scientists in human contexts. \textbf{This position paper argues that agentic AI systems must be modeled with social theory as a structural prior, and formalizes a Multi-Agent Social Systems (MASS) framework for how agents interact and influence to generate system-level outcomes.} We represent MASS as a class of dynamical system of information generation, local influence and interaction structure, formulated by four structural priors anchored in social theory: strategic heterogeneity, networked-constrained dependence, co-evolution and distributional instability. We demonstrate the importance of each structural prior through formal propositions, and articulate a research agenda for how MASS should be modeled, evaluated and governed.

cs.MA↗

arXiv2026

Automated versus Human Engagement: Mapping Cognitive Bias Triggers in Online Discourse

In the digital environment, human attention is frequently guided by cognitive heuristics rather than deliberate evaluation. Since low-credibility narratives often lack substantive factual evidence, their diffusion disproportionally relies on activating these mental shortcut to simulate credibility and capture attention. This study presents a computational framework designed to detect computational triggers through observable data proxies for eight distinct cognitive biases across 3.5 million posts of contested COVID-19 narratives. We demonstrate that automated accounts (bots) embed these triggers more frequently than human users, yielding distinctly source-dependent associations with audience interaction. In bot-authored posts, affective and cognitive dissonance (stance-shifting) triggers are strongly associated with higher engagement, while the deployment of authority and availability (repetition) cues correlates with reduced audience interaction. Furthermore, we identify limits to heuristic compounding: positive engagement correlations with bot-authored content declines when multiple biases are stacked within a single post, whereas human-authored communication remains structurally resilient to high trigger density. By operationalizing psychological heuristics into scalable, measurable data, this work bridges computational social science and cognitive psychology to reveal how source identity (bot/human) shapes the mechanics of information diffusion in digital networks.

cs.SI↗

arXiv2026

FATe of Bots: Ethical Considerations of Social Bot Detection

A growing suite of research illustrates the negative impact of social media bots in amplifying harmful information with widespread social implications. Social bot detection algorithms have been developed to help identify these bot agents efficiently. While such algorithms can help mitigate the harmful effects of social media bots, they operate within complex socio-technical systems that include users and organizations. As such, ethical considerations are key while developing and deploying these bot detection algorithms, especially at scales as massive as social media ecosystems. In this article, we examine the ethical implications for social bot detection systems through three pillars: training datasets, algorithm development, and the use of bot agents. We do so by surveying the training datasets of existing bot detection algorithms, evaluating existing bot detection datasets, and drawing on discussions of user experiences of people being detected as bots. This examination is grounded in the FATe framework, which examines Fairness, Accountability, and Transparency in consideration of tech ethics. We then elaborate on the challenges that researchers face in addressing ethical issues with bot detection and provide recommendations for research directions. We aim for this preliminary discussion to inspire more responsible and equitable approaches towards improving the social media bot detection landscape.

cs.CY↗

arXiv2026

Generative AI collective behavior needs an interactionist paradigm

In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in terms of risks and benefits, impacting us as a society at many levels. We claim that the distinctive nature of LLMs--namely, their initialization with extensive pre-trained knowledge and implicit social priors, together with their capability of adaptation through in-context learning--motivates the need for an interactionist paradigm consisting of alternative theoretical foundations, methodologies, and analytical tools, in order to systematically examine how prior knowledge and embedded values interact with social context to shape emergent phenomena in multi-agent generative AI systems. We propose and discuss four directions that we consider crucial for the development and deployment of LLM-based collectives, focusing on theory, methods, and trans-disciplinary dialogue.

cs.AI↗

arXiv2026

BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots

Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiracies in an automated fashion. Currently, organizations combat the spread of conspiracies through manual fact-checking processes and the dissemination of counter-narratives. However, the effects of harnessing the same automation to create useful bots are not well explored. To address this, we create BotSim, an Agent-Based Model of a society in which useful bots are introduced into a small world network. These useful bots are: Info-Correction Bots, which correct bad information into good, and Good Bots, which put out good messaging. The simulated agents interact through generating, consuming and propagating information. Our results show that, left unchecked, Bad Bots can create a conspiratorial society, and this can be mitigated by either Info-Correction Bots or Good Bots; however, Good Bots are more efficient and sustainable than Info-Correction Bots . Proactive good messaging is more resource-effective than reactive information correction. With our observations, we expand the concept of bots as a malicious social media agent towards automated social media agent that can be used for both good and bad purposes. These results have implications for designing communication strategies to maintain a healthy social cyber ecosystem.

cs.CY↗

arXiv2025

Local News Hijacking: A Review of International Instances

In the rise of the digital era, it's easier than ever to create nefarious websites to spread misinformation. A more recent phenomenon in the United States has been the creation of inauthentic local news websites to further an information operation campaign. This paper is a review of the 7 instances in which local news websites were created to influence residents of a region between 2007 and 2024. By breaking down the ways in which these sites operated, we discovered commonalities in the approach - resurrecting "zombie" papers that were previously established authentic local news organizations, sharing these sites on social media, and using website templates from WordPress. By analyzing these commonalities, we propose ways to mitigate the occurrence of these campaigns in the future.

cs.CY↗

arXiv2025

Trends of Pink Slime Journalism Advertisement Expenditure and Spread on Facebook from 2019-2024

Pink slime journalism is a practice where news outlets publish low-quality or inflammatory partisan articles, claiming to be local news networks. This paper examines the spread of pink slime sites on Facebook using public posts from Pages and Groups. We evaluate the trends of sharing pink slime sites on Facebook and patterns regarding the advertisements purchased by the parent organizations of the pink slime news networks. Our analysis discovers that while the number of pink slime posts on Facebook pages have decreased over the years, advertising dollars have increased. The increase in advertising dollars influences an increase in Facebook group posts. Further, the advertising expenditure increases during election years, but contentious topics are still discussed during non-election years. By illustrating the patterns and themes from US election years of 2020, 2022, and 2024, this research offers insights into potentially dangerous journalism tactics, and provides predictions for future US Presidential Elections.

cs.CY↗

arXiv2025

Extending the BEND Framework to Webgraphs

Attempts to manipulate webgraphs can have many downstream impacts, but analysts lack shared quantitative metrics to characterize actions taken to manipulate information environments at this level. We demonstrate how the BEND framework can be used to characterize attempts to manipulate webgraph information environments, and propose quantitative metrics for BEND community maneuvers. We demonstrate the face validity of our proposed Webgraph BEND metrics by using them to characterize two small web-graphs containing SEO-boosted Kremlin-aligned websites. We demonstrate how our proposed metrics improve BEND scores in webgraph settings and demonstrate the usefulness of our metrics in characterizing webgraph information environments. These metrics offer analysts a systematic and standardized way to characterize attempts to manipulate webgraphs using common Search Engine Optimization tactics.

cs.SI↗

arXiv2025

AuraSight: Generating Realistic Social Media Data

This document details the narrative and technical design behind the process of generating a quasi-realistic set X data for a fictional multi-day pop culture episode (AuraSight). Social media post simulation is essential towards creating realistic training scenarios for understanding emergent network behavior that formed from known sets of agents. Our social media post generation pipeline uses the AESOP-SynSM engine, which employs a hybrid approach of agent-based and generative artificial intelligence techniques. We explicate choices in scenario setup and summarize the fictional groups involved, before moving on to the operationalization of these actors and their interactions within the SynSM engine. We also briefly illustrate some outputs generated and discuss the utility of such simulated data and potential future improvements.

cs.CY↗

arXiv2025

Are LLM-Powered Social Media Bots Realistic?

As Large Language Models (LLMs) become more sophisticated, there is a possibility to harness LLMs to power social media bots. This work investigates the realism of generating LLM-Powered social media bot networks. Through a combination of manual effort, network science and LLMs, we create synthetic bot agent personas, their tweets and their interactions, thereby simulating social media networks. We compare the generated networks against empirical bot/human data, observing that both network and linguistic properties of LLM-Powered Bots differ from Wild Bots/Humans. This has implications towards the detection and effectiveness of LLM-Powered Bots.

cs.SI↗

arXiv2025

The Dual Personas of Social Media Bots

Social media bots are AI agents that participate in online conversations. Most studies focus on the general bot and the malicious nature of these agents. However, bots have many different personas, each specialized towards a specific behavioral or content trait. Neither are bots singularly bad, because they are used for both good and bad information dissemination. In this article, we introduce fifteen agent personas of social media bots. These personas have two main categories: Content-Based Bot Persona and Behavior-Based Bot Persona. We also form yardsticks of the good-bad duality of the bots, elaborating on metrics of good and bad bot agents. Our work puts forth a guideline to inform bot detection regulation, emphasizing that policies should focus on how these agents are employed, rather than collectively terming bot agents as bad.

cs.CY↗

arXiv2025

Star Network Motifs on X during COVID-19

Social network motifs are recurring patterns of small subgraphs that indicate fundamental patterns of social communication. In this work, we study the simple star network motifs that recur on X during the COVID-19 discourse. We study the profile of the manifestation of the star network among bot and human users. There are six primary patterns of the star motif, differentiating by the bots and humans being either egos and alters. We describe the presentation of each of these six patterns in our data, demonstrating how the motif patterns can inform social media behavioral analysis.

cs.SI↗

arXiv2025

Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains

Proactive content moderation requires platforms to rapidly and continuously evaluate the credibility of websites. Leveraging the direct and indirect paths users follow to unreliable websites, we develop a website credibility classification and discovery system that integrates both webgraph and large-scale social media contexts. We additionally introduce the concept of dredge words, terms or phrases for which unreliable domains rank highly on search engines, and provide the first exploration of their usage on social media. Our graph neural networks that combine webgraph and social media contexts generate to state-of-the-art results in website credibility classification and significantly improves the top-k identification of unreliable domains. Additionally, we release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.

cs.SI↗