UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization

• URL: http://arxiv.org/abs/2306.06851v2
• Date: Thu, 05 Dec 2024 02:43:36 GMT
• Title: UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
• Authors: Yixia Li, Rong Xiang, Yanlin Song, Jing Li,
• Abstract summary: We introduce UniPoll, a framework designed to automatically generate polls from social media posts using sophisticated natural language generation (NLG) techniques. Unlike traditional methods that struggle with social media's informal and context-sensitive nature, UniPoll leverages enriched contexts from user comments. To tackle the inherently noisy nature of social media data, UniPoll incorporates Retrieval-Augmented Generation (RAG) and synthetic data generation.
• Score: 2.345893274447675
• License:
• Abstract: Social media platforms are vital for expressing opinions and understanding public sentiment, yet many analytical tools overlook passive users who mainly consume content without engaging actively. To address this, we introduce UniPoll, an advanced framework designed to automatically generate polls from social media posts using sophisticated natural language generation (NLG) techniques. Unlike traditional methods that struggle with social media's informal and context-sensitive nature, UniPoll leverages enriched contexts from user comments and employs multi-objective optimization to enhance poll relevance and engagement. To tackle the inherently noisy nature of social media data, UniPoll incorporates Retrieval-Augmented Generation (RAG) and synthetic data generation, ensuring robust performance across real-world scenarios. The framework surpasses existing models, including T5, ChatGLM3, and GPT-3.5, in generating coherent and contextually appropriate question-answer pairs. Evaluated on the Chinese WeiboPolls dataset and the newly introduced English RedditPolls dataset, UniPoll demonstrates superior cross-lingual and cross-platform capabilities, making it a potent tool to boost user engagement and create a more inclusive environment for interaction.

Related papers

• Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance [18.23326023737371]
  We propose a novel framework for identifying astroturf campaigns on Twitter. The proposed framework does not require any training or fine-tuning of the language model. Our framework achieves 2x-3x improvements in terms of precision, recall and F1 scores.
  arXiv  Detail & Related papers  (2025-01-21T03:07:21Z)
• IOHunter: Graph Foundation Model to Uncover Online Information Operations [8.532129691916348]
  Social media platforms have become vital spaces for public discourse, serving as modern agor'as where a wide range of voices influence societal narratives. The spread of misinformation, false news, and misleading claims threatens democratic processes and societal cohesion. We introduce a methodology designed to identify users orchestrating information operations, a.k.a. textitIO drivers, across various influence campaigns.
  arXiv  Detail & Related papers  (2024-12-19T09:14:24Z)
• Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues [66.69453609603875]
  Sociocultural norms serve as guiding principles for personal conduct in social interactions. We propose a scalable approach for constructing a Sociocultural Norm (SCN) Base using Large Language Models (LLMs) We construct a comprehensive and publicly accessible Chinese Sociocultural NormBase.
  arXiv  Detail & Related papers  (2024-10-04T00:08:46Z)
• FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG [5.5997926295092295]
  The system is designed to seamlessly aggregate and curate diverse social media data sources. The GPT model is trained on decentralized data sources to ensure privacy and security.
  arXiv  Detail & Related papers  (2024-08-06T22:28:13Z)
• Leveraging GPT for the Generation of Multi-Platform Social Media Datasets for Research [0.0]
  Social media datasets are essential for research on disinformation, influence operations, social sensing, hate speech detection, cyberbullying, and other significant topics. Access to these datasets is often restricted due to costs and platform regulations. This paper explores the potential of large language models to create lexically and semantically relevant social media datasets across multiple platforms.
  arXiv  Detail & Related papers  (2024-07-11T09:12:39Z)
• KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces [55.00702535694059]
  KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI.
  arXiv  Detail & Related papers  (2024-04-22T15:01:39Z)
• SoMeLVLM: A Large Vision Language Model for Social Media Processing [78.47310657638567]
  We introduce a Large Vision Language Model for Social Media Processing (SoMeLVLM) SoMeLVLM is a cognitive framework equipped with five key capabilities including knowledge & comprehension, application, analysis, evaluation, and creation. Our experiments demonstrate that SoMeLVLM achieves state-of-the-art performance in multiple social media tasks.
  arXiv  Detail & Related papers  (2024-02-20T14:02:45Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
  Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
  arXiv  Detail & Related papers  (2022-12-27T16:08:49Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation [42.34923623457615]
  Bias in Open-Ended Language Generation dataset consists of 23,679 English text generation prompts. An examination of text generated from three popular language models reveals that the majority of these models exhibit a larger social bias than human-written Wikipedia text.
  arXiv  Detail & Related papers  (2021-01-27T22:07:03Z)

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.