On the morning of November 9th 2016, the world woke up to the shocking
outcome of the US Presidential elections: Donald Trump was the 45th President
of the United States of America. An unexpected event that still has tremendous
consequences all over the world. Today, we know that a minority of social bots,
automated social media accounts mimicking humans, played a central role in
spreading divisive messages and disinformation, possibly contributing to
Trump's victory. In the aftermath of the 2016 US elections, the world started
to realize the gravity of widespread deception in social media. Following
Trump's exploit, we witnessed to the emergence of a strident dissonance between
the multitude of efforts for detecting and removing bots, and the increasing
effects that these malicious actors seem to have on our societies. This paradox
opens a burning question: What strategies should we enforce in order to stop
this social bot pandemic? In these times, during the run-up to the 2020 US
elections, the question appears as more crucial than ever. What stroke social,
political and economic analysts after 2016, deception and automation, has been
however a matter of study for computer scientists since at least 2010. In this
work, we briefly survey the first decade of research in social bot detection.
Via a longitudinal analysis, we discuss the main trends of research in the
fight against bots, the major results that were achieved, and the factors that
make this never-ending battle so challenging. Capitalizing on lessons learned
from our extensive analysis, we suggest possible innovations that could give us
the upper hand against deception and manipulation. Studying a decade of
endeavours at social bot detection can also inform strategies for detecting and
mitigating the effects of other, more recent, forms of online deception, such
as strategic information operations and political trolls.

本文对社交媒体聊天机器人检测的第一个十年进行了概述，分析了该领域的主要趋势和取得的主要成果，并提出了可能的创新方法以对抗欺骗和操纵。

十年社交机器人检测

A Decade of Social Bot Detection

Cryptocurrencies represent one of the most attractive markets for financial
speculation. As a consequence, they have attracted unprecedented attention on
social media. Besides genuine discussions and legitimate investment
initiatives, several deceptive activities have flourished. In this work, we
chart the online cryptocurrency landscape across multiple platforms. To reach
our goal, we collected a large dataset, composed of more than 50M messages
published by almost 7M users on Twitter, Telegram and Discord, over three
months. We performed bot detection on Twitter accounts sharing invite links to
Telegram and Discord channels, and we discovered that more than 56% of them
were bots or suspended accounts. Then, we applied topic modeling techniques to
Telegram and Discord messages, unveiling two different deception schemes -
"pump-and-dump" and "Ponzi" - and identifying the channels involved in these
frauds. Whereas on Discord we found a negligible level of deception, on
Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved
in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we
observed that 93% of the invite links shared by Twitter bots point to Telegram
pump-and-dump channels, shedding light on a little-known social bot activity.
Charting the landscape of online cryptocurrency manipulation can inform
actionable policies to fight such abuse.

本文研究了加密货币在社交媒体平台上的欺诈行为，发现大量 Twitter 账户是机器人或被停止使用的账户，同时通过 Telegram 和 Discord 的信息进行主题建模，发现了 “Pump-and-dump” 和 “庞氏骗局” 两种不同的欺诈方案。鉴于 Telegram 中有超过 20％的频道与这些骗局有关，该研究揭示了一个鲜为人知的社交机器人活动，为打击加密货币网络操纵制定了可操作的政策。