arXiv subjects

Qinglan Wei

Publications and source records attributed to Qinglan Wei.

5 recordsLinked to original sources

arXiv2024

Casevo: A Cognitive Agents and Social Evolution Simulator

In this paper, we introduce a multi-agent simulation framework Casevo (Cognitive Agents and Social Evolution Simulator), that integrates large language models (LLMs) to simulate complex social phenomena and decision-making processes. Casevo is designed as a discrete-event simulator driven by agents with features such as Chain of Thoughts (CoT), Retrieval-Augmented Generation (RAG), and Customizable Memory Mechanism. Casevo enables dynamic social modeling, which can support various scenarios such as social network analysis, public opinion dynamics, and behavior prediction in complex social systems. To demonstrate the effectiveness of Casevo, we utilize one of the U.S. 2020 midterm election TV debates as a simulation example. Our results show that Casevo facilitates more realistic and flexible agent interactions, improving the quality of dynamic social phenomena simulation. This work contributes to the field by providing a robust system for studying large-scale, high-fidelity social behaviors with advanced LLM-driven agents, expanding the capabilities of traditional agent-based modeling (ABM). The open-source code repository address of casevo is https://github.com/rgCASS/casevo.

cs.SI↗

arXiv2024

Mimicking the Mavens: Agent-based Opinion Synthesis and Emotion Prediction for Social Media Influencers

Predicting influencers' views and public sentiment on social media is crucial for anticipating societal trends and guiding strategic responses. This study introduces a novel computational framework to predict opinion leaders' perspectives and the emotive reactions of the populace, addressing the inherent challenges posed by the unstructured, context-sensitive, and heterogeneous nature of online communication. Our research introduces an innovative module that starts with the automatic 5W1H (Where, Who, When, What, Why, and How) questions formulation engine, tailored to emerging news stories and trending topics. We then build a total of 60 anonymous opinion leader agents in six domains and realize the views generation based on an enhanced large language model (LLM) coupled with retrieval-augmented generation (RAG). Subsequently, we synthesize the potential views of opinion leaders and predicted the emotional responses to different events. The efficacy of our automated 5W1H module is corroborated by an average GPT-4 score of 8.83/10, indicative of high fidelity. The influencer agents exhibit a consistent performance, achieving an average GPT-4 rating of 6.85/10 across evaluative metrics. Utilizing the 'Russia-Ukraine War' as a case study, our methodology accurately foresees key influencers' perspectives and aligns emotional predictions with real-world sentiment trends in various domains.

cs.AI↗

arXiv2023

MSEVA : A System for Multimodal Short Videos Emotion Visual Analysis

YouTube Shorts, a new section launched by YouTube in 2021, is a direct competitor to short video platforms like TikTok. It reflects the rising demand for short video content among online users. Social media platforms are often flooded with short videos that capture different perspectives and emotions on hot events. These videos can go viral and have a significant impact on the public's mood and views. However, short videos' affective computing was a neglected area of research in the past. Monitoring the public's emotions through these videos requires a lot of time and effort, which may not be enough to prevent undesirable outcomes. In this paper, we create the first multimodal dataset of short video news covering hot events. We also propose an automatic technique for audio segmenting and transcribing. In addition, we improve the accuracy of the multimodal affective computing model by about 4.17% by optimizing it. Moreover, a novel system MSEVA for emotion analysis of short videos is proposed. Achieving good results on the bili-news dataset, the MSEVA system applies the multimodal emotion analysis method in the real world. It is helpful to conduct timely public opinion guidance and stop the spread of negative emotions. Data and code from our investigations can be accessed at: http://xxx.github.com.

cs.SI↗

arXiv2023

Public emotions on Internet: In case of AIGC

The proliferation of interactive AI like ChatGPT has fueled intense public discourse surrounding AI- generated content (AIGC). While some fear job displacement, others anticipate productivity gains. Social media provides a rich source of data reflecting public opinion, attitudes, and behaviors. By examining the factors influencing collective sentiment toward AIGC on various platforms, we can refine products, marketing, and AI models themselves. Our research utilized a novel system for real-time tracking and detailed visualization of public mood related to AIGC. This system enabled analysis of the dynamics shaping opinions on nine AIGC products across China's three leading social media sites. Our findings reveal a negative correlation between user demographics (age and education) and positive sentiment towards AIGC on Douyin, contrasting with Weibo's susceptibility to the rapid spread of extreme viewpoints. This work uniquely connects group dynamics theory with social media sentiment, offering valuable guidance for managing online opinion and tailoring targeted campaigns.

cs.SI↗

arXiv2022

FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference

In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality and outperforms the existing models' performance on both IEMOCAP and CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time; (3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.

cs.CV↗