25 research outputs found
Bot Electioneering Volume: Visualizing Social Bot Activity During Elections
It has been widely recognized that automated bots may have a significant
impact on the outcomes of national events. It is important to raise public
awareness about the threat of bots on social media during these important
events, such as the 2018 US midterm election. To this end, we deployed a web
application to help the public explore the activities of likely bots on Twitter
on a daily basis. The application, called Bot Electioneering Volume (BEV),
reports on the level of likely bot activities and visualizes the topics
targeted by them. With this paper we release our code base for the BEV
framework, with the goal of facilitating future efforts to combat malicious
bots on social media.Comment: 3 pages, 3 figures. In submissio
Scalable and Generalizable Social Bot Detection through Data Selection
Efficient and reliable social bot classification is crucial for detecting
information manipulation on social media. Despite rapid development,
state-of-the-art bot detection models still face generalization and scalability
challenges, which greatly limit their applications. In this paper we propose a
framework that uses minimal account metadata, enabling efficient analysis that
scales up to handle the full stream of public tweets of Twitter in real time.
To ensure model accuracy, we build a rich collection of labeled datasets for
training and validation. We deploy a strict validation system so that model
performance on unseen datasets is also optimized, in addition to traditional
cross-validation. We find that strategically selecting a subset of training
data yields better model accuracy and generalization than exhaustively training
on all available data. Thanks to the simplicity of the proposed model, its
logic can be interpreted to provide insights into social bot characteristics.Comment: AAAI 202
Uncovering Coordinated Networks on Social Media
Coordinated campaigns are used to influence and manipulate social media
platforms and their users, a critical challenge to the free exchange of
information online. Here we introduce a general network-based framework to
uncover groups of accounts that are likely coordinated. The proposed method
construct coordination networks based on arbitrary behavioral traces shared
among accounts. We present five case studies of influence campaigns in the
diverse contexts of U.S. elections, Hong Kong protests, the Syrian civil war,
and cryptocurrencies. In each of these cases, we detect networks of coordinated
Twitter accounts by examining their identities, images, hashtag sequences,
retweets, and temporal patterns. The proposed framework proves to be broadly
applicable to uncover different kinds of coordination across information
warfare scenarios
Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem
Simulating and predicting planetary-scale techno-social systems poses heavy
computational and modeling challenges. The DARPA SocialSim program set the
challenge to model the evolution of GitHub, a large collaborative
software-development ecosystem, using massive multi-agent simulations. We
describe our best performing models and our agent-based simulation framework,
which we are currently extending to allow simulating other planetary-scale
techno-social systems. The challenge problem measured participant's ability,
given 30 months of meta-data on user activity on GitHub, to predict the next
months' activity as measured by a broad range of metrics applied to ground
truth, using agent-based simulation. The challenge required scaling to a
simulation of roughly 3 million agents producing a combined 30 million actions,
acting on 6 million repositories with commodity hardware. It was also important
to use the data optimally to predict the agent's next moves. We describe the
agent framework and the data analysis employed by one of the winning teams in
the challenge. Six different agent models were tested based on a variety of
machine learning and statistical methods. While no single method proved the
most accurate on every metric, the broadly most successful sampled from a
stationary probability distribution of actions and repositories for each agent.
Two reasons for the success of these agents were their use of a distinct
characterization of each agent, and that GitHub users change their behavior
relatively slowly
BotSlayer: DIY Real-Time Influence Campaign Detection
BotSlayer is an application that helps track and detect potential manipulation of information spreading on Twitter. It can be used by journalists, corporations, political candidates, and civil society organizations to discover online coordinated campaigns in real time. BotSlayer uses an anomaly detection algorithm to flag hashtags, links, accounts, phrases, and media that are trending and amplified in a coordinated fashion by likely bots. A Web dashboard lets users explore the tweets and accounts associated with suspicious campaigns, visualize their spread, and search related content on multiple search engines and social media platforms. BotSlayer is easily installed and configured in the cloud. It will aid in the study and early detection of social media manipulation phenomena
The Hoaxy Misinformation and Fact-Checking Diffusion Network
Massive amounts of misinformation flood social media like Twitter and Facebook. Digital misinformation includes articles about hoaxes, conspiracy theories, fake news, and other misleading claims. This content has been alleged to disrupt the public debate, leading to questions about its impact on the real world. A number of research questions have been formulated around the ways misinformation spreads, who are its main purveyors, and whether fact-checking efforts can be helpful at mitigating its diffusion. Here we release a large longitudinal dataset from Twitter, consisting of retweeted messages with links to misinformation and fact-checking articles. These data have been collected using Hoaxy (hoaxy.iuni.iu.edu), an open social media analytics platform whose goal is to provide a comprehensive picture of how digital misinformation spreads and competes with fact-checking efforts. The released dataset contains over 20 million retweets, spanning the period from May 2016 to the end of 2017. We provide basic statistics about the data and the associated diffusion networks
Anatomy of an online misinformation network
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network