A股上市公司雪球投资者发帖评论数据

互联网时代最显著的特征之一是互联网用户产生的大数据，大数据中包含的信息通常被认为可以为金融和实体部门提供附加值。在各式各样的大数据中，我们经常看到各种社交媒体平台上表达观点或分享经验的帖子，尤其是金融行业，由于金融行业从业者对信息的高度依赖，金融社交媒体上的帖子往往对投资者的金融决策产生重大影响。   发帖是线上平台投资者进行社交互动的主要方式。投资者发帖主要是分享自己的观点、评论其他人的帖子、提出问题并回答询问。据研究表明，投资者在社交平台上发帖的主要原因有三个。首先，投资者发帖进行有效的信息互动。在社交平台上发帖鼓励更多投资者参与讨论和回应，让他们获得有价值的反馈。其次，投资者发帖可能是为了实现自我实现，因为发帖者希望通过分享有吸引力的帖子来吸引关注者，并希望成为意见领袖，展示自己扩大影响力的投资能力。第三，投资者发帖是为了表达和煽动情绪，尤其是当他们的投资业绩不佳时。

One of the most prominent features of the Internet era is the vast volumes of data generated by internet users. The information embedded in such big data is widely recognized to deliver added value to both the financial and real economy sectors. Among diverse big data sources, posts conveying opinions or sharing experiences across various social media platforms are frequently observed. Particularly within the financial industry, given the high reliance of practitioners on information, posts on financial social media platforms often exert a substantial impact on investors' financial decision-making.

Posting is the primary mode of social interaction for investors on online platforms. When posting on these platforms, investors primarily share their own viewpoints, comment on others' posts, raise questions, and respond to inquiries. Empirical studies have identified three core motivations for investors to post on social media platforms. First, investors post to facilitate effective information interaction. Posting on social media encourages greater investor participation in discussions and responses, allowing them to obtain valuable feedback. Second, investors may post to achieve self-actualization: posters aim to attract followers by sharing compelling content, aspire to become opinion leaders, and demonstrate their investment capabilities to expand their influence. Third, investors post to express and stoke emotions, particularly when their investment performance is poor.

搜集汇总

背景与挑战

背景概述

该数据集收集了雪球网上A股上市公司个人投资者的发帖评论数据，包含股票代码、帖子内容、发布者信息及点赞、转发、评论等互动指标，时间截止2025年11月并年度更新。它通过文本信息直观展现投资者观点和情绪，适用于金融行为分析、市场情绪研究和另类数据应用。