MSU expert: What Meta’s new fact-checking policies mean for misinformation and hate speech

Meta’s decision to change its content moderation policies by replacing centralized fact-checking teams with user-generated community labeling has stirred up a storm of reactions. But taken at face value, the changes raise questions about the effectiveness of Meta’s old policy and fact-checking and how this new approach with community comments will perform.

Anjana Susarla, Omura-Saxena Professor in Responsible AI in MSU’s Eli Broad College of Business, is an expert on social media and technology policy. Here, she discusses the serious large-scale social challenge of combating online harms on social media platforms. 

Answers are excerpts from an article originally published in The Conversation.

What is content moderation and how does it work?

With billions of people worldwide accessing their social media services, platforms such as Meta’s Facebook and Instagram have a responsibility to ensure that users are not harmed by consumer fraud, hate speech, misinformation or other online ills. Given the scale of this problem, combating online harms is a serious societal challenge. Content moderation plays a role in addressing these online harms.

Moderating content involves three steps. The first step is scanning online content — typically social media posts — to detect potentially harmful words or images. The second is assessing whether the flagged content violates the law or the platform’s terms of service. The third is intervening in some way. Interventions include removing posts, adding warning labels to posts and diminishing how much a post can be seen or shared.

Content moderation can range from user-driven moderation models on community-based platforms such as Wikipedia to centralized content moderation models such as those used by Instagram. Research shows that both approaches are a mixed bag.

How does Meta’s new approach to fact-checking compare with its old approach?

Meta’s previous content moderation policy relied on third-party fact-checking organizations, which brought problematic content to the attention of Meta staff. Meta’s U.S. fact-checking organizations were AFP USA, Check Your Fact, Factcheck.org, Lead Stories, PolitiFact, Science Feedback, Reuters Fact Check, Televisa, Univision, The Dispatch and USAToday.

Meta CEO Mark Zuckerberg highlighted that content moderation at Meta would shift to a community labeling model similar to X, formerly Twitter. X’s Community Notes is a crowdsourced fact-checking approach that allows users to write notes to inform others about potentially misleading posts.

How effective is crowdsourced fact-checking at combating misinformation?

Studies are mixed on the effectiveness of X-style content moderation efforts. A large-scale study found little evidence that the introduction of Community Notes significantly reduced engagement with misleading tweets on X. Rather, it appears that such crowd-based efforts might be too slow to effectively reduce engagement with misinformation in the early and most viral stage of its spread.

There have been some successes from quality certifications and badges on platforms. However, community-provided labels might not be effective in reducing engagement with misinformation, especially when they’re not accompanied by appropriate training about labeling for a platform’s users. Research also shows that X’s Community Notes is subject to partisan bias.

Crowdsourced initiatives such as the community-edited online reference Wikipedia depend on peer feedback and rely on having a robust system of contributors. A Wikipedia-style model needs strong mechanisms of community governance to ensure that individual volunteers follow consistent guidelines when they authenticate and fact-check posts.

What role does content moderation play in consumer safety and product liability?

Content moderation has implications for businesses that either use Meta for advertising or to connect with their consumers. Content moderation is also a brand safety issue because platforms have to balance their desire to keep the social media environment safer against that of greater engagement.

How does artificial intelligence affect content moderation?

Content moderation is likely to be further strained by growing amounts of content generated by artificial intelligence tools. AI-detection tools are flawed, and developments in generative AI are challenging people’s ability to differentiate between human-generated and AI-generated content.

There is potential for a flood of inauthentic accounts — AI bots — that exploit algorithmic and human vulnerabilities to monetize false and harmful content. For example, they could commit fraud and manipulate opinions for economic or political gain.

Generative AI tools such as ChatGPT make it easier to create large volumes of realistic social media profiles and content. AI-generated content primed for engagement can also exhibit significant biases, such as race and gender. In fact, Meta faced a backlash for its own AI-generated profiles, with commentators labeling it “AI-generated slop.”

Regardless of the type of content moderation, the practice alone is not effective at reducing belief in misinformation or at limiting its spread.

Ultimately, research shows that a combination of fact-checking approaches, which rely on impartial expert review approaches, in tandem with audits of platforms and partnerships with researchers and citizen activists are important in ensuring safe and trustworthy community spaces on social media.

By Evan Katz

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