Facebook's Fake News Solutions May Pose Dangers

Facebook's Fake News Solutions May Pose Dangers

Assessment

Interactive Video

Business, Social Studies

University

Hard

Created by

Wayground Content

FREE Resource

The video discusses the complexity of addressing misinformation on social media platforms like Facebook. It highlights the challenges in using technology to solve the problem and the potential risks of quick fixes, such as silencing diverse ideas. Facebook's current approach involves hiring more staff to manually review political ads, acknowledging the limitations of their machine learning capabilities.

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5 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is one of the main challenges in using technology to address misinformation on social media?

It is easy to identify bot-spread stories.

There is no risk of silencing ideas.

Quick solutions can lead to biased information control.

Technology can solve the problem without human intervention.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a potential risk of implementing quick technological solutions to misinformation?

It could enhance the spread of misinformation.

It might lead to the silencing of certain ideas.

It will make the platform more popular.

It will have no impact on information quality.

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What concern does Stamos express about public expectations of Facebook?

There is a desire for solutions that favor specific ideologies.

The public is satisfied with Facebook's current efforts.

Facebook should not address misinformation at all.

People want Facebook to remain neutral.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does Facebook plan to handle political ads and misinformation?

By using only machine learning algorithms.

By hiring more staff for manual review.

By ignoring political ads altogether.

By allowing all ads without review.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is Facebook's current limitation in addressing misinformation?

Lack of public support.

Insufficient machine learning capabilities.

Over-reliance on technology.

Too many staff members.