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LLMs & generative AIDetection methods
RAIDAR: Generative AI Detection via RewritingThis paper introduces Raidar, a method to detect machine-generated text by measuring how much LLMs modify text when asked to rewrite it. The key observation is that LLMs tend to preserve AI-generated text and modify human-written text more when rewriting.
LLMs & generative AIDetection methods
Can LLM-Generated Misinformation Be Detected?LLM-generated misinformation (from ChatGPT, Llama, Vicuna) is detectably harder for both humans and automated detectors to identify than human-written misinformation with identical semantics. The paper builds a taxonomy of LLM-generated misinformation across types, domains, and generation methods, then empirically demonstrates that…
Spread & networksPlatforms, policy & society
The Amplification Paradox in Recommender SystemsAlgorithmic audits find that blindly following YouTube recommendations leads to extreme content, yet real user logs show recommendations don't drive extreme content consumption. Using an agent-based model with realistic user preferences, the authors show this paradox arises because users rarely consume niche content even when recommended…
Platforms, policy & societyLLMs & generative AI
Disinformation 2.0 in the Age of AI: A Cybersecurity PerspectiveDisinformation should be treated as a cybersecurity threat, particularly as AI advances enable its creation and dissemination at unprecedented scale. The paper introduces "disinformation 2.0"—where generative AI, perturbation techniques, detection evasion, and optimized spread strategies make false content more persuasive and harder to…
LLMs & generative AIDetection methods
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and ExplainabilityA comprehensive survey of trustworthy Graph Neural Networks covering four critical dimensions: privacy attacks and defenses against membership/property inference and model extraction; robustness against adversarial perturbations; fairness mechanisms to prevent discrimination in graph-based predictions; and explainability methods to…
LLMs & generative AIDatasets & evaluation
A Survey on Evaluation of Large Language ModelsThis survey provides a comprehensive review of evaluation methodologies for large language models across three dimensions: what to evaluate, where to evaluate, and how to evaluate. The paper encompasses evaluation tasks spanning natural language processing, natural language generation, robustness, ethics, bias detection, trustworthiness,…
LLMs & generative AIDeepfakes & multimodal
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTThis is the first comprehensive survey of AI-Generated Content (AIGC) covering the history, techniques, and applications of generative models from GANs through ChatGPT. It addresses both the impressive capabilities of these models (text, image, audio, video generation) and critical concerns including factuality, security, privacy, and…
LLMs & generative AIDatasets & evaluation
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and PreventionLinkedIn's lack of verification enables fake accounts that harm legitimate users and the platform's integrity. This work introduces a dataset of 3600 profiles (legitimate, manually created fake, and ChatGPT-generated) and proposes SSTE, a method that detects fake profiles immediately after registration using only profile text.
LLMs & generative AIDetection methods
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability CurvatureThis paper identifies that text sampled from language models tends to occupy negative-curvature regions of the model's log-probability landscape, whereas human-written text occupies more neutral regions. DetectGPT leverages this observation to detect machine-generated text in a zero-shot manner by analyzing probability curvature, without…
Spread & networksDetection methods
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News SpreadersThis work is the first to systematically assess whether individuals spread fake news intentionally or unintentionally. The authors propose an influence graph that captures psychological (internal) and social (external) factors driving unintentional spreading, validate it on two datasets with manually-annotated spreader intent, and…
LLMs & generative AIDetection methods
All the News That's Fit to Fabricate: AI-Generated Text as a Tool of Media MisinformationThree experiments investigate whether AI-generated news text can be credible and influence public opinion. The authors find that GPT-2 models (355M to 1.5B parameters) generate text nearly indistinguishable from human-written baseline news, particularly when politically congruent stories are paired with partisan audiences.
Platforms, policy & societyDetection methods
A Comprehensive Survey on Trustworthy Recommender SystemsRecommender systems, while beneficial to users, can amplify harmful content, introduce discrimination, and enable adversarial attacks. This survey provides a comprehensive framework for trustworthy recommender systems across six critical dimensions: Safety & Robustness (defense against adversarial attacks), Non-discrimination & Fairness…
Deepfakes & multimodalDatasets & evaluation
Deepfake Detection: A Systematic Literature ReviewSystematic review of 112 deepfake detection studies (2018–2020) categorizing techniques into deep learning, machine learning, statistical, and blockchain methods. Deep learning approaches (77% of studies, primarily CNNs) significantly outperform non-deep methods, achieving ~89.7% mean accuracy.
Spread & networksPlatforms, policy & society
Right-Wing YouTube: A Supply and Demand PerspectiveYouTube has become the dominant platform for right-wing political content in the US, driven by supply-side affordances (low creation barriers, monetization, algorithmic recommendation) and demand-side audience appetite for alternative media. Using longitudinal analysis of creator clusters (Conservative, Alt-Lite, Alt-Right), the authors…
Psychology & interventionsFact-checking & verification
The psychological drivers of misinformation belief and its resistance to correctionThis comprehensive review establishes that misinformation's power lies not in exposure but in how cognitive, social, and affective factors shape belief formation and persist even after correction. False beliefs arise from intuitive thinking, memory failures, emotional content, in-group bias, and source credibility judgments; corrections…
Psychology & interventionsPlatforms, policy & society
Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USARandomized controlled trial on 8,001 respondents (4,000 UK, 4,001 USA) measuring causal impact of exposure to COVID-19 vaccine misinformation. Exposure to misinformation reduced vaccination intent by 6.2 percentage points in the UK and 6.4 in the USA relative to exposure to factual information.
Spread & networksDatasets & evaluation
Prevalence of Health Misinformation on Social Media: Systematic ReviewSystematic review of 69 studies examining health misinformation prevalence across social media platforms. Vaccines, drugs, and smoking are the most-studied topics; Twitter is the primary platform for misinformation about smoking and substances, while vaccines are discussed broadly across platforms.
Psychology & interventionsPlatforms, policy & society
Shifting attention to accuracy can reduce misinformation onlineThe paper shows through six online experiments and a Twitter field trial that simply drawing people's attention to accuracy can substantially reduce their sharing of misinformation. When asked to rate the accuracy of headlines before deciding whether to share them, participants share more accurate content; a Twitter field experiment…
Deepfakes & multimodalDetection methods
Multimodal Emergent Fake News Detection via Meta Neural Process NetworksExisting fake news detectors trained on labeled data perform poorly on novel, emergent events due to domain shift. The paper proposes MetaFEND, which combines meta-learning and neural processes to leverage a handful of labeled posts from a new event to quickly adapt a detection model.
Spread & networksDatasets & evaluation
A Survey of Information Cascade Analysis: Models, Predictions, and Recent AdvancesThis comprehensive survey systematizes research on predicting information cascade growth and structure across social networks, covering feature-based models, generative models, and deep learning approaches. It provides a taxonomy of prediction methods across temporal, structural, user/item, and content features; evaluation metrics and…
Spread & networksPlatforms, policy & society
Prevalence of Health Misinformation on Social Media: Systematic ReviewThis systematic review of 69 studies characterizes health misinformation topics and their prevalence across social media platforms. Health misinformation was most prevalent on Twitter regarding smoking and drugs; vaccines, noncommunicable diseases, and pandemics were also common topics.
Spread & networksPsychology & interventions
The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical InformationThis paper systematically studies the QAnon conspiracy theory by collecting and analyzing 4,961 unique Q drops from six aggregation sites. The authors find that aggregation sites show poor agreement on what constitutes a canonical Q drop, that Q drops are incoherent and low-quality relative to other social media platforms, and that…
Detection methodsDatasets & evaluation
MDFEND: Multi-domain Fake News DetectionMost fake news detection methods focus on single-domain data, which leads to poor performance across different domains due to domain shift (varying word usage and propagation patterns). This paper introduces Weibo21, the first multi-domain fake news dataset from a single platform with 9 domains, and proposes MDFEND, a model using…
Platforms, policy & societyBots & coordinated campaigns
The Threat of Offensive AI to OrganizationsA comprehensive survey identifying 33 offensive AI capabilities that adversaries can use to attack organizations, organized into seven categories: automation, campaign resilience, credential theft, exploit development, information gathering, social engineering, and stealth. Through a user study with 22 experts from academia and industry,…