How Political Campaigns Weaponize Social Media Bots

Analysis of computational propaganda in the 2016 U.S. presidential election reveals the reach of bots

11 min read
Opening illustration for this feature article.
Illustration: Jude Buffum

In the summer of 2017, a group of young political activists in the United Kingdom figured out how to use the popular dating app Tinder to attract new supporters. They understood how Tinder’s social networking platform worked, how its users tended to use the app, and how its algorithms distributed content, and so they built a bot to automate flirty exchanges with real people. Over time, those flirty conversations would turn to politics—and to the strengths of the U.K.’s Labour Party.

To send its messages, the bot would take over a Tinder profile owned by a Labour-friendly user who’d agreed to the temporary repurposing of his or her account. Eventually, the bot sent somewhere between 30,000 and 40,000 messages, targeting 18- to 25-year-olds in constituencies where the Labour candidates were running in tight races. It’s impossible to know precisely how many votes are won through social media campaigns, but in several targeted districts, the Labour Party did prevail by just a few votes. In celebrating their victory, campaigners took to Twitter to thank their team—with a special nod to the Tinder election bot.

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Video Friday: Turkey Sandwich

Your weekly selection of awesome robot videos

4 min read
A teleoperated humanoid robot torso stands in a kitchen assembling a turkey sandwich from ingredients on a tray

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

CoRL 2022: 14–18 December 2022, AUCKLAND, NEW ZEALAND

Enjoy today's videos!

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New AI Speeds Computer Graphics by Up to 5x

Neural rendering harnesses machine learning to paint pixels

5 min read
Four examples of Nvidia's Instant NeRF 2D-to-3D machine learning model placed side-by-side.

Nvidia Instant NeRF uses neural rendering to generate 3D visuals from 2D images.

NVIDIA

On 20 September, Nvidia’s Vice President of Applied Deep Learning, Bryan Cantanzaro, went to Twitter with a bold claim: In certain GPU-heavy games, like the classic first-person platformer Portal, seven out of eight pixels on the screen are generated by a new machine-learning algorithm. That’s enough, he said, to accelerate rendering by up to 5x.

This impressive feat is currently limited to a few dozen 3D games, but it’s a hint at the gains neural rendering will soon deliver. The technique will unlock new potential in everyday consumer electronics.

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Learn How Global Configuration Management and IBM CLM Work Together

In this presentation we will build the case for component-based requirements management

2 min read

This is a sponsored article brought to you by 321 Gang.

To fully support Requirements Management (RM) best practices, a tool needs to support traceability, versioning, reuse, and Product Line Engineering (PLE). This is especially true when designing large complex systems or systems that follow standards and regulations. Most modern requirement tools do a decent job of capturing requirements and related metadata. Some tools also support rudimentary mechanisms for baselining and traceability capabilities (“linking” requirements). The earlier versions of IBM DOORS Next supported a rich configurable traceability and even a rudimentary form of reuse. DOORS Next became a complete solution for managing requirements a few years ago when IBM invented and implemented Global Configuration Management (GCM) as part of its Engineering Lifecycle Management (ELM, formerly known as Collaborative Lifecycle Management or simply CLM) suite of integrated tools. On the surface, it seems that GCM just provides versioning capability, but it is so much more than that. GCM arms product/system development organizations with support for advanced requirement reuse, traceability that supports versioning, release management and variant management. It is also possible to manage collections of related Application Lifecycle Management (ALM) and Systems Engineering artifacts in a single configuration.

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