Bot activity on social media platforms is a pervasive problem, undermining
the credibility of online discourse and potentially leading to cybercrime. We
propose an approach to bot detection using Generative Adversarial Networks
(GAN). We discuss how we overcome the issue of mode collapse by utilizing
multiple discriminators to train against one generator, while decoupling the
discriminator to perform social media bot detection and utilizing the generator
for data augmentation. In terms of classification accuracy, our approach
outperforms the state-of-the-art techniques in this field. We also show how the
generator in the GAN can be used to evade such a classification technique.

利用生成对抗网络（GAN）的方法来检测社交媒体平台上的机器人活动，克服模式崩溃问题，通过将多个鉴别器与一个生成器训练并解耦鉴别器以进行社交媒体机器人检测，利用生成器进行数据增强，我们的方法在分类准确性方面优于该领域的最先进技术。我们还展示了 GAN 中的生成器如何用于逃避这种分类技术。

利用 Dropout-GAN 进行社交媒体机器人检测

Social Media Bot Detection using Dropout-GAN

We study the problem of predicting the future performance of cryptocurrencies
using social media data. We propose a new model to measure the engagement of
users with topics discussed on social media based on interactions with social
media posts. This model overcomes the limitations of previous volume and
sentiment based approaches. We use this model to estimate engagement
coefficients for 48 cryptocurrencies created between 2019 and 2021 using data
from Twitter from the first month of the cryptocurrencies' existence. We find
that the future returns of the cryptocurrencies are dependent on the engagement
coefficients. Cryptocurrencies whose engagement coefficients are too low or too
high have lower returns. Low engagement coefficients signal a lack of interest,
while high engagement coefficients signal artificial activity which is likely
from automated accounts known as bots. We measure the amount of bot posts for
the cryptocurrencies and find that generally, cryptocurrencies with more bot
posts have lower future returns. While future returns are dependent on both the
bot activity and engagement coefficient, the dependence is strongest for the
engagement coefficient, especially for short-term returns. We show that simple
investment strategies which select cryptocurrencies with engagement
coefficients exceeding a fixed threshold perform well for holding times of a
few months.

研究加密货币市场中社交媒体数据对其价格预测的影响，提出一种测量用户对社交媒体上讨论的话题互动的新模型并根据此模型测算 48 种加密货币未来投资回报，结果表明加密货币的回报与其关注度及人为动态的自动化账户有关，而对具有一定阈值的加密货币进行选择的简单投资策略可在短期内获得较好收益。