Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand their network of friends or followers, freely interacting with others by posting, commenting, and sharing content. Over time, user behavior evolves based on demographic attributes and the networks they establish. In this research, we propose a predictive method to understand how a user evolves on social media throughout their life and to forecast the next stage of their evolution. We fine-tune a GPT-like decoder-only model (we named it E-GPT: Evolution-GPT) to predict the future stages of a user's evolution in online social media. We evaluate the performance of these models and demonstrate how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media, which also addresses other social media challenges such as recommendation systems.

翻译：社交媒体平台被广泛用于分享个人情感、日常活动及各类生活事件，使人们能够及时了解最新动态。自用户创建账户起，他们便持续扩展其好友或关注者网络，通过发布内容、评论和分享实现自由互动。随着时间推移，用户行为会依据人口统计属性及其建立的社交网络发生演化。本研究提出一种预测方法，旨在理解用户在社交媒体中的生命周期演化轨迹，并预测其下一阶段的演化方向。我们微调了一个仅含解码器的GPT类模型（命名为E-GPT：Evolution-GPT），用于预测用户在在线社交媒体中的未来演化阶段。我们评估了这些模型的性能，并通过预测未来社交连接及用户行为变化，论证了用户属性如何影响其网络内部动态，该方法同时可应对推荐系统等其他社交媒体挑战。