BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor

• URL: http://arxiv.org/abs/2503.01880v1
• Date: Wed, 26 Feb 2025 18:18:37 GMT
• Title: BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor
• Authors: Mohammed-Khalil Ghali, Abdelrahman Farrag, Sarah Lam, Daehan Won,
• Abstract summary: Thematic analysis of social media posts provides a major understanding of public discourse.<n>Traditional methods often struggle to capture the complexity and nuance of unstructured, large-scale text data.<n>This study introduces a novel methodology for thematic analysis that integrates tweet embeddings from pre-trained language models.
• Score: 2.699900017799093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thematic analysis of social media posts provides a major understanding of public discourse, yet traditional methods often struggle to capture the complexity and nuance of unstructured, large-scale text data. This study introduces a novel methodology for thematic analysis that integrates tweet embeddings from pre-trained language models, dimensionality reduction using and matrix factorization, and generative AI to identify and refine latent themes. Our approach clusters compressed tweet representations and employs generative AI to extract and articulate themes through an agentic Chain of Thought (CoT) prompting, with a secondary LLM for quality assurance. This methodology is applied to tweets from the autistic community, a group that increasingly uses social media to discuss their experiences and challenges. By automating the thematic extraction process, the aim is to uncover key insights while maintaining the richness of the original discourse. This autism case study demonstrates the utility of the proposed approach in improving thematic analysis of social media data, offering a scalable and adaptable framework that can be applied to diverse contexts. The results highlight the potential of combining machine learning and Generative AI to enhance the depth and accuracy of theme identification in online communities.

Related papers

• A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content [2.3543188414616534]
  Advances in AI-generated content have led to wide adoption of large language models, diffusion-based visual generators, and synthetic audio tools. These developments raise concerns about misinformation, copyright infringement, security threats, and the erosion of public trust. This paper explores an extensive range of methods designed to detect and mitigate AI-generated textual, visual, and audio content.
  arXiv  Detail & Related papers  (2025-04-02T23:27:55Z)
• Early Detection of Mental Health Issues Using Social Media Posts [0.0]
  Social media platforms, like Reddit, represent a rich source of user-generated content. We propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises.
  arXiv  Detail & Related papers  (2025-03-06T23:08:08Z)
• A Multimodal Framework for Topic Propagation Classification in Social Networks [2.189314262079322]
  This paper proposes a predictive model for topic dissemination in social networks. We introduce two novel indicators, user relationship breadth and user authority, into the PageRank algorithm. We refine the measurement of user interaction traces with topics, replacing traditional topic view metrics with a more precise communication characteristics measure.
  arXiv  Detail & Related papers  (2025-03-05T02:12:23Z)
• SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention [50.07342730395946]
  We introduce a novel multi-agent framework that effectively. imulates $underlinetextbfC$ontent $underlinetextbfA$nalysis via. underlinetextbfL$arge language model (LLM) agunderlinetextbfE$nts. It imitates key phases of content analysis, including text coding, collaborative discussion, and dynamic codebook evolution.
  arXiv  Detail & Related papers  (2025-02-16T00:19:07Z)
• Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
  Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science. Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords. We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
  arXiv  Detail & Related papers  (2024-10-08T05:13:27Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs [22.976609127865732]
  We introduce a novel approach to uncovering latent themes in social media messaging. Our work sheds light on the dynamic nature of social media, revealing the shifts in the thematic focus of messaging in response to real-world events.
  arXiv  Detail & Related papers  (2024-03-15T21:54:00Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Emergent Explainability: Adding a causal chain to neural network inference [0.0]
  This position paper presents a theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom) We explore the novel integration of EmCom into AI systems, offering a paradigm shift from conventional associative relationships between inputs and outputs to a more nuanced, causal interpretation. The paper discusses the theoretical underpinnings of this approach, its potential broad applications, and its alignment with the growing need for responsible and transparent AI systems.
  arXiv  Detail & Related papers  (2024-01-29T02:28:39Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence [0.0]
  Large language models (LLMs) have been shown to present an unprecedented opportunity to scale up data analytics in the humanities and social sciences. We build on mixed methods quantitizing and converting design principles, and feature analysis from linguistics, to transparently integrate human expertise and machine scalability. The approach is discussed and demonstrated in over a dozen LLM-assisted case studies, covering 9 diverse languages, multiple disciplines and tasks.
  arXiv  Detail & Related papers  (2023-09-24T14:21:50Z)
• Structured Like a Language Model: Analysing AI as an Automated Subject [0.0]
  We argue the intentional fictional projection of subjectivity onto large language models can yield an alternate frame through which AI behaviour can be analysed. We trace a brief history of language models, culminating in the releases of systems that realise state-of-the-art natural language processing performance. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.
  arXiv  Detail & Related papers  (2022-12-08T21:58:43Z)
• Aggression and "hate speech" in communication of media users: analysis of control capabilities [50.591267188664666]
  Authors studied the possibilities of mutual influence of users in new media. They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting. Results can be useful for developing media content in a modern digital environment.
  arXiv  Detail & Related papers  (2022-08-25T15:53:32Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.