Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never been systematically investigated before. Here, we study 9M tweets related to stocks of the five main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice—referred to as cashtag piggybacking —perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot-detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam- and bot-detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.

中文翻译：

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Cashtag 捎带

微博越来越多地被用于预测金融市场股票的价格和交易量。然而，事实证明，在微博平台上共享的大部分内容都是由机器人和垃圾邮件发送者创建和发布的。然而，以前从未系统地调查过假股票微博的存在（或缺乏）和影响。在这里，我们研究了与美国五个主要金融市场的股票相关的 900 万条推文。通过将推文与来自 Google Finance 的财务数据进行比较，我们突出了 Twitter 股票微博的重要特征。更重要的是，我们发现了一种恶意行为——称为现金标签捎带- 由协作的机器人组实施，可能旨在通过利用高价值股票的流行来推销低价值股票。我们的研究发现，多达 71% 的可疑金融推文作者被最先进的垃圾邮件检测算法归类为机器人。此外，在我们调查几个月后，其中 37% 被 Twitter 暂停。我们的结果要求在所有利用用户生成的内容来预测股市的研究和应用程序中采用垃圾邮件和机器人检测技术。