“Troll Watch: Trending Hashtags - NPR” plus 1 more

“Troll Watch: Trending Hashtags - NPR” plus 1 more

Troll Watch: Trending Hashtags - NPR

Posted: 11 Aug 2019 02:03 PM PDT

NPR's Michel Martin speaks with journalist Emily Stewart.


It's a big weekend in politics with a lot of Democratic contenders at the Iowa State Fair and different candidates making the benchmarks for the next scheduled debates in September. So that's one reason we thought it would be worthwhile to look back at the last round of debates. You might have noticed that hashtags like #DemDebateSoWhite and #KamalaHarrisDestroyed started trending, and you might have wondered why. Well, in the time since, we've learned more about what made those hashtags go viral and who was behind them, so we've decided to take this to our regular segment Troll Watch.


MARTIN: We're joined now by Emily Stewart. She's a reporter at Vox, and she recently wrote about the hashtag #KamalaHarrisDestroyed. She says it reveals how much we still don't understand about social media manipulation, and she's with us now.

Emily Stewart, welcome. Thanks for joining us.

EMILY STEWART: Thank you for having me.

MARTIN: So let's start with the conclusion and work backward. The trending hashtag #KamalaHarrisDestroyed - look, it's a reference to the confrontation between Hawaii Congresswoman Tulsi Gabbard when she criticized California Senator Kamala Harris' record as a prosecutor. So it was a moment, but there were people who thought it odd that it got so much traction and wondered whether bots or conservative activists were kind of pushing it out. So do we know?

STEWART: Well, I think that's sort of the big question here. And I think the answer is that it seems as though it is a combination. It looks like that specific hashtag was started by conservative activists, but then Fox found it and spread it and helped it to get farther than it might have otherwise. So that seems to be what happened.

But, you know, I think it reveals broader questions that we just really still do not know how social media manipulation works. And we can't figure out what to trust and what not to trust.

MARTIN: You say that Twitter and other platforms have improved their practices post-2016. They're still not perfect in your reporting. Do the social media companies take this seriously?

STEWART: They take it seriously to the extent that it would be a bad look if they didn't look like they were trying at all. But obviously, social media companies make money when we are there and we are engaged. And we know that what keeps people engaged is controversy. You know, you hear a lot of talk about social media bias.

Well, social media's bias is toward extremism, towards keeping people excited. And so, yes, does Facebook and Twitter and Google want to do a little bit better? Maybe. But it's not convenient for them to be boring. And so this sort of stuff is what drums people up and keeps people coming back.

MARTIN: One of the points that you make in the piece is this - I just want to read something from your piece. You said, it's not just the misinformation itself that sows division. It's also the debate about it. People are confused about what social media manipulation is, how it works and whether it's happening. And they've also got their own political motivations to believe whether or not it exists. You say that Harris's camp had an incentive to claim that negative hashtag about her is Russian propaganda, but her opponents have an incentive to brush it off. And so the more people debate what is and isn't fake news, the harder it becomes for voters to determine the truth.

Maybe this is sort of a tautology here, but I wonder if - could it possibly work the other way? That people perhaps can be better educated about whether they should hold back before they decide whether something's true because there's so much fog around it?

STEWART: Yeah. I mean, I think that there is - it's hard to say hold on and wait for all the information, especially now when the Internet has made communication so frictionless. I think it's important to keep motivations in mind, and it's good to hang back and wait for more information before you make conclusions. But it's also - I mean, it's human nature. It's hard to do sometimes.

MARTIN: That's Emily Stewart, reporter at Vox. We're talking about her piece "#KamalaHarrisDestroyed Debate Signals How Much We Still Don't Understand About Social Media Manipulation."

Emily, thanks so much for talking to us.

STEWART: Thank you.

MARTIN: This is NPR News.

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Global Computer Vision Software Market Trending Research Report 2019 - Microsoft, AWS, OpenCV, Google, Sight Machine, Scikit-image - Top News Herald

Posted: 14 Aug 2019 12:36 AM PDT

Global Computer Vision Software Market Research Report 2019 Overview :

Global Computer Vision Software Market Growth (Status and Outlook) 2019-2024 has its complete summary provided in such a pattern that the reader will get the consequences of the vital information associated with the Computer Vision Software market. The research study provides comprehensive data of manufacturers, regions, applications, and others. The report includes the competitive landscape by elaborating on the current mergers and acquisitions (M&A), venture funding, and product developments that took place in the market in the recent past. It provides product distribution, product demand, growth benefits, business flexibility, financial growth, and applications.

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The market statistics are portrayed in a very cear-cut format for the convenience of the readers. It comprises of top to bottom illumination of the past information along with the present and future needs. Additionally, an examination of current market designs and other basic characteristics all around the world is covered. The report covers estimation from a global aspect that contains a regional expansion class, along with Computer Vision Software market size, scope and benefit, and expenditure data. Moreover, the report has included ongoing industry trends and advances as well as preceding, existing, and estimated market size in terms of volume and value.

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  • APAC (China, Japan, Korea, Southeast Asia, India, Australia)
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