Investigating the dissemination of STEM content on social media with computational tools
Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSentiment AnalysisSimilar Papers 제목 키워드 기반
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate …
ChatbotFederated LearningInformation RetrievalRAG+1MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with…
MisinformationY Social: an LLM-powered Social Media Digital Twin
In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform. Digital twins are virtual replicas of physical systems that allow for advanced analyses and experimentati…
MVP: Winning Solution to SMP Challenge 2025 Video Track
Social media platforms serve as central hubs for content dissemination, opinion expression, and public engagement across diverse modalities. Accurately predicting the popularity of social media videos enables valuable ap…
ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting
Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In t…
Community DetectionManagementMisinformation