标题： EVOLVE-X：社交媒体用户演变预测的嵌入融合与语言提示 显示英文标题

标题： EVOLVE-X: Embedding Fusion and Language Prompting for User Evolution Forecasting on Social Media

摘要： 社交媒体平台作为分享个人情绪、日常活动和各种生活事件的重要媒介，确保个人了解最新动态。 从创建账户开始，用户逐步扩展他们的朋友或关注者圈子，通过发布、评论和分享内容积极参与。 随着时间的推移，用户在这些平台上的行为会发生变化，受到人口统计特征和他们形成网络的影响。 在本研究中，我们提出了一种新方法，利用开源模型 Llama-3-Instruct、Mistral-7B-Instruct、Gemma-7B-IT 通过提示工程，结合 GPT-2、BERT 和 RoBERTa 使用联合嵌入技术，以分析和预测用户在社交媒体上的行为演变。 我们的实验表明，这些模型在预测用户社会演变的未来阶段方面具有潜力，包括网络变化、未来联系和用户活动的变化。 实验结果突显了我们方法的有效性，在跨模态配置中 GPT-2 实现了最低的困惑度（8.21），优于 RoBERTa（9.11）和 BERT，并强调了利用跨模态配置以获得卓越性能的重要性。 这种方法解决了社交媒体中的关键挑战，如好友推荐和活动预测，提供了对用户行为轨迹的见解。 通过预测未来的互动和活动，这项研究旨在提供潜在负面结果的早期预警，使用户能够做出明智的决策并长期降低风险。 显示英文摘要

摘要： Social media platforms serve as a significant medium for sharing personal emotions, daily activities, and various life events, ensuring individuals stay informed about the latest developments. From the initiation of an account, users progressively expand their circle of friends or followers, engaging actively by posting, commenting, and sharing content. Over time, user behavior on these platforms evolves, influenced by demographic attributes and the networks they form. In this study, we present a novel approach that leverages open-source models Llama-3-Instruct, Mistral-7B-Instruct, Gemma-7B-IT through prompt engineering, combined with GPT-2, BERT, and RoBERTa using a joint embedding technique, to analyze and predict the evolution of user behavior on social media over their lifetime. Our experiments demonstrate the potential of these models to forecast future stages of a user's social evolution, including network changes, future connections, and shifts in user activities. Experimental results highlight the effectiveness of our approach, with GPT-2 achieving the lowest perplexity (8.21) in a Cross-modal configuration, outperforming RoBERTa (9.11) and BERT, and underscoring the importance of leveraging Cross-modal configurations for superior performance. This approach addresses critical challenges in social media, such as friend recommendations and activity predictions, offering insights into the trajectory of user behavior. By anticipating future interactions and activities, this research aims to provide early warnings about potential negative outcomes, enabling users to make informed decisions and mitigate risks in the long term.