targeting and influence

Manual orchestration in cross-channel marketing leads to insufficient personalization in content, timing, frequency, and copy. Method: This paper proposes an agent-driven, causally enhanced sequential decision-making framework. It introduces a novel strategy optimization mechanism integrating Difference-in-Differences (DID) causal effect estimation with Thompson sampling, enabling interpretable and scalable personalized decisions; and designs a modular policy network supporting joint modeling and coordinated optimization across multi-touch channels—including email, push notifications, and in-app messaging. Contribution/Results: Evaluated in a production environment with 150 million users, the system significantly improves incremental engagement rates across all funnel stages and boosts target-event conversion rates. It establishes a new paradigm for large-scale, causally grounded personalized marketing.

This study addresses the low persuasive efficacy of anti-theft promotional content in mobile security applications. To enhance user engagement, it investigates how psychological traits, personality dimensions, and value orientations can inform personalized persuasive strategies. Through qualitative field observations, in-depth interviews, and practice-theoretical analysis, the research first systematically identifies seven user-specific moderating factors influencing digital persuasion effectiveness. Building on these findings, it proposes a personalized adaptation framework for conversational persuasive systems—enabling dynamic strategy adjustment by security advisors. Empirical evaluation demonstrates that adaptive persuasion significantly improves user acceptance and adoption of security behaviors compared to static technical scripts. The core contribution is the first theoretically grounded, mobile-context-specific persuasive framework that integrates user trait modeling with real-time, context-aware strategy generation—and validates its effectiveness in authentic security-related human–system interactions.

This study investigates demographic targeting patterns and fairness risks associated with microtargeting strategies in climate-related social media advertising. We propose the first post-hoc explainability framework leveraging large language models (LLMs), integrating prompt engineering, text classification, and fairness auditing to identify gender- and age-based targeting and attribute thematic content in Facebook climate ads. Our model achieves 88.55% accuracy in predicting targeted demographics. Results reveal that youth are significantly associated with activist narratives, while women are disproportionately exposed to caregiving-themed content. Systematic classification biases are identified against older adults and men. The key contribution lies in pioneering the joint application of LLMs for explainable and fairness-aware evaluation of climate communication microtargeting—establishing a methodological foundation and empirical evidence for equitable environmental information dissemination in digital environments.

Online behavioral advertising (OBA) suffers from conceptual ambiguity and fragmented empirical evidence, hindering theoretical advancement and practical guidance. Method: Through a systematic literature review and interdisciplinary theoretical integration, this study develops the first precise definition of OBA and a unified theoretical framework that jointly models advertiser-controllable variables (e.g., data transparency, personalization intensity) and consumer-level characteristics (e.g., privacy concern, digital literacy). Contribution/Results: The study maps the distribution of existing empirical findings and identifies three critical research gaps: (1) mechanistic black boxes, (2) contextual boundary conditions, and (3) cross-cultural variations. It proposes empirically testable future research directions and policy design principles that balance advertising efficacy with privacy protection. This work provides an integrative scholarly foundation for advancing OBA theory, optimizing precision advertising practices, and informing data governance policy.

Small and medium-sized enterprises (SMEs) lack the resources and technical expertise to deploy conventional media-mix modeling (MMM), particularly amid tightening privacy regulations that exacerbate attribution challenges. Method: This paper introduces Robyn—a novel, open-source m/MMM framework designed specifically for SME advertisers. It features a modular, “plug-and-play” architecture integrating Bayesian time-series modeling (via PyMC/Stan), automated hyperparameter optimization, Adstock response modeling, and a scalable Python implementation—ensuring both interpretability and organizational deployability. Contribution/Results: Robyn systematically addresses data sparsity, prior bias, and cross-functional collaboration barriers. It reduces modeling turnaround from weeks to hours, enabling multi-channel attribution and budget allocation optimization. The framework has been deployed at scale across over 1,000 SMEs and within Meta’s advertising ecosystem, undergoing continuous iteration and real-world validation.

Amid the proliferation of generative models and increasing platform restrictions on behavioral data access, traditional detection methods for malicious information manipulation—relying on content or network features—are becoming increasingly ineffective. This work proposes a platform-agnostic detection framework that, for the first time, models user activity as a sequential decision-making process and identifies manipulative accounts by learning behavioral policies rather than depending on content features. By leveraging behavioral policy as a stable discriminative signal, the approach enables cross-platform, evasion-resistant detection even in environments where content is easily forged and data access is limited. Evaluated on a dataset of 12,064 Reddit users including 99 known Russian IRA accounts, the proposed policy classifier achieves a macro F1-score of 94.9%, substantially outperforming text embedding–based methods while enabling earlier detection and greater robustness.

This study addresses a regulatory gap in the EU Digital Services Act (DSA) Article 28(2), which prohibits profiling-based advertising targeting minors but adopts a narrow definition of “advertising” that excludes undisclosed influencer marketing. Through an algorithmic audit on TikTok, the authors deployed simulated minor and adult accounts, combined with automated content annotation and statistical analysis, to empirically demonstrate that—despite nominal compliance—minors are exposed to substantial volumes of undisclosed commercial content driven by high-intensity interest-based profiling. The intensity of such profiling reaches five to eight times that of formal advertisements shown to adults. The findings underscore the need to broaden the legal definition of “advertising” to encompass emerging forms of commercial content, thereby closing critical enforcement loopholes in digital platform regulation.

This study addresses a critical privacy vulnerability in mainstream social media platforms, where advertisers are permitted to target users based on sensitive attributes and subsequently access their profiles following user interactions—such as likes or comments—thereby violating the platforms’ stated privacy commitments. Through empirical testing on TikTok, Facebook, and Instagram, combined with a detailed analysis of platform policies, this work demonstrates for the first time that advertisers can exploit user engagement behaviors to identify individuals possessing specific sensitive characteristics, revealing a significant gap in current privacy protections. To mitigate the risk of inadvertent privacy disclosure, the paper proposes interface design enhancements that improve user transparency and control over their data in targeted advertising contexts.

This study addresses the unintended long-term consequences of user interventions—such as “sleep reminders”—on recommender systems that dynamically adapt to user feedback. Through a large-scale field experiment on a short-video platform, combined with causal inference, log analysis, and dynamic policy modeling, the authors demonstrate that such interventions can inadvertently “retrain” the recommendation algorithm, inducing systemic shifts in content delivery. Contrary to expectations, the sleep reminder not only failed to reduce usage but increased late-night viewing duration by 14.75% and overall usage by 2.18%, with effects persisting for several weeks. These findings challenge the conventional paradigm of evaluating interventions under static assumptions and underscore the necessity of accounting for algorithmic adaptation in digital well-being policies.

This study systematically evaluates the efficacy and underlying mechanisms of AI-driven influence operations on social networks. By constructing a synthetic social network platform that integrates multi-agent simulation, natural language generation, and belief dynamics modeling, the work quantifies for the first time the impact of three core strategies—narrative dissemination, information amplification, and counter-messaging—on audience beliefs. The findings reveal that information amplification achieves the broadest reach, counter-messaging is most effective in shifting opinions, and narrative dissemination requires substantially greater adversarial investment to yield measurable effects. Furthermore, the research uncovers an intrinsic relationship between the behavioral footprints of influence actors and the resulting effectiveness of their campaigns.