The detection of automated accounts, also known as "social bots", has been an
increasingly important concern for online social networks (OSNs). While several
methods have been proposed for detecting social bots, significant research gaps
remain. First, current models exhibit limitations in detecting sophisticated
bots that aim to mimic genuine OSN users. Second, these methods often rely on
simplistic profile features, which are susceptible to manipulation. In addition
to their vulnerability to adversarial manipulations, these models lack
generalizability, resulting in subpar performance when trained on one dataset
and tested on another.
To address these challenges, we propose a novel framework for social Bot
detection with Self-Supervised Contrastive Learning (BotSSCL). Our framework
leverages contrastive learning to distinguish between social bots and humans in
the embedding space to improve linear separability. The high-level
representations derived by BotSSCL enhance its resilience to variations in data
distribution and ensure generalizability. We evaluate BotSSCL's robustness
against adversarial attempts to manipulate bot accounts to evade detection.
Experiments on two datasets featuring sophisticated bots demonstrate that
BotSSCL outperforms other supervised, unsupervised, and self-supervised
baseline methods. We achieve approx. 6% and approx. 8% higher (F1) performance
than SOTA on both datasets. In addition, BotSSCL also achieves 67% F1 when
trained on one dataset and tested with another, demonstrating its
generalizability. Lastly, BotSSCL increases adversarial complexity and only
allows 4% success to the adversary in evading detection.

我们提出了一种基于自监督对比学习的社交机器人检测框架（BotSSCL），通过利用对比学习在嵌入空间中区分社交机器人和人类，改善了线性可分性。BotSSCL 的高级表示增强了对数据分布变化的鲁棒性并确保了泛化性能。实验证明，BotSSCL 在两个数据集上的性能较其他监督、无监督和自监督基准方法都要高，并且在不同数据集间的测试也表现出较好的泛化性能。此外，BotSSCL 还提高了对抗复杂性，使对手在逃避检测方面的成功率只有 4%。

BotSSCL: 自监督对比学习中的社交机器人检测

BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning

Online Social Networks have revolutionized how we consume and share
information, but they have also led to a proliferation of content not always
reliable and accurate. One particular type of social accounts is known to
promote unreputable content, hyperpartisan, and propagandistic information.
They are automated accounts, commonly called bots. Focusing on Twitter
accounts, we propose a novel approach to bot detection: we first propose a new
algorithm that transforms the sequence of actions that an account performs into
an image; then, we leverage the strength of Convolutional Neural Networks to
proceed with image classification. We compare our performances with
state-of-the-art results for bot detection on genuine accounts / bot accounts
datasets well known in the literature. The results confirm the effectiveness of
the proposal, because the detection capability is on par with the state of the
art, if not better in some cases.

本论文从 Twitter 账号出发，提出了使用卷积神经网络的新型算法，将账号执行的操作序列转化为图片，并进行图像分类，从而进行 bot 检测。结果证实了该方法的有效性，因为它与现有技术处在同一水平，并在某些情况下更好。

从网络行为到图像：一种新的社交机器人检测方法

From Online Behaviours to Images: A Novel Approach to Social Bot  Detection

Online trolls increase social costs and cause psychological damage to
individuals. With the proliferation of automated accounts making use of bots
for trolling, it is difficult for targeted individual users to handle the
situation both quantitatively and qualitatively. To address this issue, we
focus on automating the method to counter trolls, as counter responses to
combat trolls encourage community users to maintain ongoing discussion without
compromising freedom of expression. For this purpose, we propose a novel
dataset for automatic counter response generation. In particular, we
constructed a pair-wise dataset that includes troll comments and counter
responses with labeled response strategies, which enables models fine-tuned on
our dataset to generate responses by varying counter responses according to the
specified strategy. We conducted three tasks to assess the effectiveness of our
dataset and evaluated the results through both automatic and human evaluation.
In human evaluation, we demonstrate that the model fine-tuned on our dataset
shows a significantly improved performance in strategy-controlled sentence
generation.

针对网络恶意评论的问题，本研究提出了一种新的数据集用于自动生成反击评论，经过实验验证，该数据集可以有效地改善对抗策略控制的句子生成。