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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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Paying Tribute to 1997 IEEE President Charles K. Alexander

The Life Fellow was a professor at Cleveland State University

4 min read
portrait of man smiling against a light background
The Alexander Family

Charles K. Alexander, 1997 IEEE president, died on 17 October at the age of 79.

The active volunteer held many high-level positions throughout the organization, including 1991–1992 IEEE Region 2 director. He was also the 1993 vice president of the IEEE United States Activities Board (now IEEE-USA).

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Robot Learns Human Trick for Not Falling Over

Humanoid limbs are useful for more than just manipulation

3 min read
A black and white humanoid robot with a malfunctioning leg supports itself with one arm against a wall

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Humanoid robots are a lot more capable than they used to be, but for most of them, falling over is still borderline catastrophic. Understandably, the focus has been on getting humanoid robots to succeed at things as opposed to getting robots to tolerate (or recover from) failing at things, but sometimes, failure is inevitable because stuff happens that’s outside your control. Earthquakes, accidentally clumsy grad students, tornadoes, deliberately malicious grad students—the list goes on.

When humans lose their balance, the go-to strategy is a highly effective one: use whatever happens to be nearby to keep from falling over. While for humans this approach is instinctive, it’s a hard problem for robots, involving perception, semantic understanding, motion planning, and careful force control, all executed under aggressive time constraints. In a paper published earlier this year in IEEE Robotics and Automation Letters, researchers at Inria in France show some early work getting a TALOS humanoid robot to use a nearby wall to successfully keep itself from taking a tumble.

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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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