GPT-4V(ision) as A Social Media Analysis Engine

• URL: http://arxiv.org/abs/2311.07547v1
• Date: Mon, 13 Nov 2023 18:36:50 GMT
• Title: GPT-4V(ision) as A Social Media Analysis Engine
• Authors: Hanjia Lyu, Jinfa Huang, Daoan Zhang, Yongsheng Yu, Xinyi Mou, Jinsheng Pan, Zhengyuan Yang, Zhongyu Wei, Jiebo Luo
• Abstract summary: This paper explores GPT-4V's capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge.
• Score: 77.23394183063238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information.

Related papers

• Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey [66.166184609616]
  ChatGPT has opened up immense potential for applying large language models (LLMs) to text-centric multimodal tasks. It is still unclear how existing LLMs can adapt better to text-centric multimodal sentiment analysis tasks.
  arXiv  Detail & Related papers  (2024-06-12T10:36:27Z)
• MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms [25.73585435351771]
  This paper introduces MM-Soc, a benchmark designed to evaluate Multimodal Large Language Models' understanding of social media content. MM-Soc compiles prominent multimodal datasets and incorporates a novel large-scale YouTube tagging dataset. Our analysis reveals that, in a zero-shot setting, various types of MLLMs generally exhibit difficulties in handling social media tasks.
  arXiv  Detail & Related papers  (2024-02-21T22:27:40Z)
• 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)
• Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication [68.40865217231695]
  This study examines the behavior of GPT-4V in replicating human-like use of emojis. The findings reveal a discernible discrepancy between human and GPT-4V behaviors, likely due to the subjective nature of human interpretation.
  arXiv  Detail & Related papers  (2024-01-16T08:56:52Z)
• A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering [53.70661720114377]
  multimodal large models (MLMs) have significantly advanced the field of visual understanding, offering remarkable capabilities in realm of visual question answering (VQA) Yet, the true challenge lies in the domain of knowledge-intensive VQA tasks, which necessitate deep comprehension of the visual information in conjunction with a vast repository of learned knowledge. To uncover such capabilities, we provide an in-depth evaluation from three perspectives: 1) Commonsense Knowledge, which assesses how well models can understand visual cues and connect to general knowledge; 2) Fine-grained World Knowledge, which tests the model's skill in reasoning out specific knowledge from images, showcasing
  arXiv  Detail & Related papers  (2023-11-13T18:22:32Z)
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [121.42924593374127]
  We analyze the latest model, GPT-4V, to deepen the understanding of LMMs. GPT-4V's unprecedented ability in processing arbitrarily interleaved multimodal inputs makes it a powerful multimodal generalist system. GPT-4V's unique capability of understanding visual markers drawn on input images can give rise to new human-computer interaction methods.
  arXiv  Detail & Related papers  (2023-09-29T17:34:51Z)
• A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks [2.048226951354646]
  Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions. The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications. Our study focuses on the field of multimodal sentiment analysis, which examines visual and textual data posted on social media networks.
  arXiv  Detail & Related papers  (2022-07-05T16:28:47Z)
• Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities [5.4482836906033585]
  Social media platforms are evolving from text-based forums into multi-modal environments. Misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. We analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection.
  arXiv  Detail & Related papers  (2022-03-25T19:45:33Z)

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.