State-sponsored trolls are the main actors of influence campaigns on social
media and automatic troll detection is important to combat misinformation at
scale. Existing troll detection models are developed based on training data for
known campaigns (e.g.\ the influence campaign by Russia's Internet Research
Agency on the 2016 US Election), and they fall short when dealing with {\em
novel} campaigns with new targets. We propose MetaTroll, a text-based troll
detection model based on the meta-learning framework that enables high
portability and parameter-efficient adaptation to new campaigns using only a
handful of labelled samples for few-shot transfer. We introduce
\textit{campaign-specific} transformer adapters to MetaTroll to ``memorise''
campaign-specific knowledge so as to tackle catastrophic forgetting, where a
model ``forgets'' how to detect trolls from older campaigns due to continual
adaptation. Our experiments demonstrate that MetaTroll substantially
outperforms baselines and state-of-the-art few-shot text classification models.
Lastly, we explore simple approaches to extend MetaTroll to multilingual and
multimodal detection. Source code for MetaTroll is available at:
this https URL.

MetaTroll 是一种基于元学习框架的文本模型，利用少量标注样本进行 few-shot 转移，通过引入 “特定于活动” 的转换器适配器以针对新的活动目标，可以防止模型因为不断适应而忘记如何检测老活动中的恶意用户。实验表明，MetaTroll 明显优于基线和最先进的几种少样本文本分类模型，并且可以扩展到多语言和多模态检测。

MetaTroll: 基于 Transformer Adapter 的国家支持的 Troll 的小样本检测

MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

Recent evidence has emerged linking coordinated campaigns by state-sponsored
actors to manipulate public opinion on the Web. Campaigns revolving around
major political events are enacted via mission-focused "trolls." While trolls
are involved in spreading disinformation on social media, there is little
understanding of how they operate, what type of content they disseminate, how
their strategies evolve over time, and how they influence the Web's information
ecosystem. In this paper, we begin to address this gap by analyzing 10M posts
by 5.5K Twitter and Reddit users identified as Russian and Iranian
state-sponsored trolls. We compare the behavior of each group of
state-sponsored trolls with a focus on how their strategies change over time,
the different campaigns they embark on, and differences between the trolls
operated by Russia and Iran. Among other things, we find: 1) that Russian
trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that
campaigns undertaken by such actors are influenced by real-world events; and 3)
that the behavior of such actors is not consistent over time, hence automated
detection is not a straightforward task. Using the Hawkes Processes statistical
model, we quantify the influence these accounts have on pushing URLs on four
social platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/),
and Gab. In general, Russian trolls were more influential and efficient in
pushing URLs to all the other platforms with the exception of /pol/ where
Iranians were more influential. Finally, we release our data and source code to
ensure the reproducibility of our results and to encourage other researchers to
work on understanding other emerging kinds of state-sponsored troll accounts on
Twitter.

本文通过分析来自 5.5k 个 Twitter 和 Reddit 用户的 1000 万个帖子，揭示了俄罗斯和伊朗政府组织实施的协调运动的本质，阐述了它们如何利用社交媒体进行舆论操纵，并通过 Hawkes 过程统计模型量化了它们在 Twitter、Reddit、4chan 和 Gab 上推送 URL 的影响力和效率。

谁放出了巨魔？探究国家支持巨魔的行为

Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls

Over the past couple of years, anecdotal evidence has emerged linking
coordinated campaigns by state-sponsored actors with efforts to manipulate
public opinion on the Web, often around major political events, through
dedicated accounts, or "trolls." Although they are often involved in spreading
disinformation on social media, there is little understanding of how these
trolls operate, what type of content they disseminate, and most importantly
their influence on the information ecosystem.
In this paper, we shed light on these questions by analyzing 27K tweets
posted by 1K Twitter users identified as having ties with Russia's Internet
Research Agency and thus likely state-sponsored trolls. We compare their
behavior to a random set of Twitter users, finding interesting differences in
terms of the content they disseminate, the evolution of their account, as well
as their general behavior and use of Twitter. Then, using Hawkes Processes, we
quantify the influence that trolls had on the dissemination of news on social
platforms like Twitter, Reddit, and 4chan. Overall, our findings indicate that
Russian trolls managed to stay active for long periods of time and to reach a
substantial number of Twitter users with their tweets. When looking at their
ability of spreading news content and making it viral, however, we find that
their effect on social platforms was minor, with the significant exception of
news published by the Russian state-sponsored news outlet RT (Russia Today).

本文对与俄罗斯互联网研究机构具有联系的 Twitter 用户发布的 27K 条推文进行了分析，并使用 Hawkes Process 对俄罗斯 trolls 的影响进行量化。发现俄罗斯 trolls 能够在 Twitter 上活跃很长一段时间，并影响大量用户，但当涉及到传播新闻内容并使其成为病毒式传播时，他们的影响力在社交媒体上较小，除了俄罗斯国家赞助的新闻机构 RT。