TWITTER IS LEANING TOO MUCH ON MACHINE-BASED CONTENT MODERATION. HERE’S WHY THIS IS PROBLEMATIC

Twitter has taken a new approach to content moderation, which might not be that fruitful. While the platform has dealt with child pornography and pedophilia adequately, how it is going about it may be a short-term solution at best.

Twitter’s new Vice President of Trust and Safety, Ella Irwin, recently revealed that Elon Musk’s Twitter is leaning heavily on automation to moderate content, doing away with specific manual reviews and favoring restrictions on distribution rather than removing clear speech outright. While this is in line with what Musk had said about free speech when he took over, the way the social media platform is going about its content moderation is a stop-gap solution at best.

Twitter is also more aggressively restricting abuse-prone hashtags and search results in areas including child exploitation, regardless of potential impacts on “benign uses” of those terms, said Irwin.

The company has faced difficult questions about its ability and willingness to moderate harmful and hateful content since Musk slashed half of Twitter’s staff and issued an ultimatum to work long hours, resulting in hundreds more employees’ loss. Things for content moderation were made even worse when Musk’s team terminated over 4000 contractual content moderators and several other content moderation teams on Twitter’s payroll. Two sources familiar with the cuts said that more than 50% of the Health engineering unit was laid off. Health engineering is the term used for the content moderation team at Twitter.

On Friday, Musk vowed “significant reinforcement of content moderation and protection of freedom of speech” in a meeting with President Emmanuel Macron.

One approach, captured in the industry mantra “freedom of speech, not freedom of reach,” entails leaving up specific tweets that violate the company’s policies but barring them from appearing in places like the home timeline and search.

Twitter has long deployed such “visibility filtering” tools around misinformation and had already incorporated them into its official hateful conduct policy before the Musk acquisition. The approach allows for more freewheeling speech while reducing the potential harm of abusive viral content.

Tweets containing derogatory words for African-Americans, Asians, and Jews were triple the number seen in the month before Musk took over. At the same time, tweets containing a slur for homosexuals were up 31 percent. All in all, racial slur usage increased by 500 percent on the day Elon Musk took over.

Irwin says Musk was focused on using automation more, arguing that the company had erred on the side of using time and labor-intensive human reviews of harmful content in the past. On child safety, for instance, Irwin said Twitter had shifted toward automatically taking down tweets reported by trusted figures with a track record of accurately flagging harmful posts.

Twitter also restricts hashtags and search results frequently associated with abuse, like those aimed at looking up “teen” pornography. She said that past concerns about the impact of such restrictions on permitted uses of the terms were gone.

The problem with this model is outsourcing content moderation to users, who may not always be available to inappropriate flag content. This also opens up an entirely different pandora’s box on who is considered a “trusted figure.” In the instance of child pornography and pedophilia, this was pretty easy to determine, but things get a lot murkier when you consider narratives that are tinted with political ideology.

Another reason this is problematic is that trolls on Twitter adapt, perhaps faster than developers at Twitter can detect. Suppose Twitter starts using a system that automatically detects specific hashtags or keywords used by predators and trolls to block out their tweets or content from mass distribution. In that case, these actors will only need to develop different characters that look like those words. Elon Musk has to realize that even though they may be faulty and not be entirely successful 100 percent of the time, content moderation on social media requires human beings.

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