AI is used in social media to analyze user behavior, personalize content recommendations, and detect trends or sentiment. It employs machine learning algorithms and natural language processing to understand and categorize user-generated content, optimize ad targeting, and enhance user experiences on social media platforms.
Researchers introduced SID-Set, a large-scale dataset for deepfake detection, and SIDA, a groundbreaking AI framework that detects, localizes, and explains manipulated images, setting new benchmarks for combating misinformation.
AI systems, like humans, exhibit social identity biases, favoring "ingroup" and disparaging "outgroup." New research reveals that data curation can reduce these biases, offering hope for fairer AI.
Researchers introduced the GETAE framework, integrating text analysis and social dynamics, achieving breakthroughs in fake news detection accuracy on social media datasets.
Real-time fake news detection system, FANDC, uses advanced AI to help social media users identify misinformation and prevent unintentional sharing.
A new study introduces the YesBut dataset, designed to evaluate how well vision-language models comprehend satire, highlighting significant gaps in current model capabilities.
AI-generated images and videos significantly increase false memories, with AI-generated videos causing the strongest memory distortions. This raises concerns for legal, ethical, and societal impacts.
Researchers explore how generative AI models like ChatGPT and DALL·E2 capture the unique identities of global cities through text and imagery, revealing both strengths and limitations in AI's understanding of urban environments.
Researchers evaluated various machine learning methods for false news detection, highlighting the strengths and limitations of passive-aggressive classifiers, SVMs, and random forests, while introducing a novel ChatGPT-generated dataset.
Researchers propose revisions to trust models, highlighting the complexities introduced by generative AI chatbots and the critical role of developers and training data.
Researchers developed a machine learning model to detect high-risk suicide users in online forums by analyzing social interaction patterns. The study showed that specific network features could predict suicidal behavior, underscoring the value of network-based analysis in suicide prevention.
This article explores the dual role of large language models (LLMs) in generating and detecting fake news. It highlights the effectiveness of LLMs in identifying misinformation and discusses their potential applications across various platforms to maintain digital integrity.
Researchers identified basic human values via Twitter activity using graph clustering and proposed behavior-based group recommendations. The study achieved superior clustering accuracy and validation, highlighting significant intra-cluster correlations among users with hedonistic values, thus enhancing understanding of value-based social media interactions.
The European project SIGNIFICANCE, using AI and deep learning, developed a platform to combat the illegal trafficking of cultural heritage goods. By identifying, tracking, and blocking illegal online activities, the platform increased the detection of illegal artifacts by 10-15%, aiding law enforcement in safeguarding cultural heritage.
A review in Materials & Design explores how big data, machine learning (ML), and digital twin technologies enhance additive manufacturing (AM). These technologies improve AM by optimizing design, material properties, and process efficiency, offering significant advancements in quality, efficiency, and sustainability.
AudioSeal introduces a novel watermarking method tailored for pinpointing AI-generated speech in audio files, ensuring robust detection and localization capabilities down to the sample level, thus bolstering audio authenticity and security.
Researchers used AI models to analyze Flickr images from global protected areas, identifying cultural ecosystem services (CES) activities. Their study reveals distinct regional patterns and underscores the value of social media data for conservation management.
Researchers analyzed 3.8 million tweets to uncover how users engage with ChatGPT for tasks like coding and content creation, highlighting its versatile applications. The study underscores ChatGPT's potential to revolutionize business processes and services across multiple domains.
Researchers utilized machine learning algorithms to predict life satisfaction with high accuracy (93.80%) using data from a Danish government survey. By identifying 27 key questions and employing models such as KNN, SVM, and Bayesian networks, the study highlighted the significant impact of health conditions on life satisfaction and made the best predictive model publicly available.
Researchers employed AI techniques to analyze Reddit discussions on coronary artery calcium (CAC) testing, revealing diverse sentiments and concerns. The study identified 91 topics and 14 discussion clusters, indicating significant interest and engagement. While sentiment analysis showed predominantly neutral or slightly negative attitudes, there was a decline in sentiment over time.
This study delves into earthquake response dynamics using XGBoost, unraveling the interplay between environmental cues and human behavior through meticulous video analysis. With superior predictive accuracy, it offers invaluable insights for emergency management, signaling a paradigm shift in disaster response strategies.
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