While Dems Talk Policy, Republicans Attack Their Characters Online

An analysis of 200,000 tweets revealed significant online attacks on the character, but not the policies, of the Democratic front-runners after the last debate

Former Vice President Joe Biden challenges Sen. Elizabeth Warren (D-MA) during the Democratic Presidential Debate at Otterbein University on October 15, 2019 in Westerville, Ohio. Photo: Win McNamee/Getty Images

As soon as former vice president Joe Biden finished defending his son Hunter during the Tuesday night CNN/New York Times Democratic debate, his rivals seemed to back off the topic. The internet, though, was ablaze with vigorous criticism of him. People on Twitter, led by a group of high-profile tweeters close to the president, launched barrage after barrage of attacks on Biden’s character.

Donald Trump Jr.’s tweet—“Yea, #QuidProJoe had no idea what was going on and never discussed business with his son!🙄”—was shared almost 10,000 times in the lead-up to and during the debate. “Sleepy Creepy Sloppy Slow Crooked Joe is not above the law!” was being shared heavily as well.

In fact, Biden was the most attacked Democratic candidate on Twitter in the three days before and during the debate, according to an analysis of approximately 200,000 tweets by MarvelousAI, a startup using natural language processing techniques to explore political narratives and misinformation on social media. Notably, many of the tweets weren’t created during the three days before the debate, but rather saw a groundswell of sharing activity before and after it.

The research, led by Berkeley-trained linguist Olya Gurevich, started with clustering tweets by similar topics; human annotators then assigned a specific narrative to each cluster. Those narratives were then segregated by tone and meaning as either attacks (“Joe Biden is corrupt,” or “Elizabeth Warren is a liar and opportunist”) or support (“Bernie Sanders is a consistent progressive,” or “Kamala Harris speaks truth”).

Courtesy of the author.

Their analysis of debate-night tweets, along with tweets from the three days before the debate, showed that one-third of the major narratives that emerged were about Biden, and nearly 99% of them were attacks, specifically on his character. Other Twitter narratives were sorted into categories that included electability, identity, ideology, and policy.

The online attacks on Biden and Warren differed markedly from mainstream media accounts of the debate, which focused on Warren taking fire from her Democratic rivals, suggesting that Republicans were using the debates to shape broad Twitter narratives that could follow the Democratic nominee into the general election. That’s consistent with the GOP approach on Facebook, where Trump and his allies have been busy painting broad-brush narratives meant to discredit opponents and win him reelection.

Courtesy of the author.

Meanwhile, Sanders and Pete Buttigieg saw a surge of support, with popular tweets describing Sanders’ ideology as consistently progressive and Buttigieg’s character as embodying a well-spoken candidate.

Like Biden, Warren came in for significant criticism on Twitter, with thousands of accounts framing her as dishonest. For instance, a tweet from the Republican National Committee — “Once again, Elizabeth Warren is getting caught lying about who she is” — was shared more than 2,000 times in that time frame. Also shared more than 2,000 times over those three days was a tweet from Kellyanne Conway: “Another Elizabeth Warren lie about Elizabeth Warren?”

This is not a surprising or novel tactic, of course. It’s stock criticism of Democratic front-runners that we’ve seen before: Hillary Clinton, John Kerry and Al Gore were all attacked for being dishonest.

While this analysis of Twitter shows the reemergence of persistent and successful attack lines, what’s perhaps unprecedented is the ability to extract these strategies and frames in nearly real time from the milieu of social media.

To be sure, these techniques are far from perfect. Not every tweet was hand coded, and there is blurring between what counts as an attack versus support or character versus identity. But this kind of work underscores the approaches now available to help get a handle on what’s actually happening in the noxious, chaotic free-for-all of the digital commons.

As more people flock to Twitter, Facebook, Reddit, or more-toxic platforms like 4chan to talk politics, how are we to understand which narratives dominate or what has changed over time? These tools have implications for the social sciences, political science, data journalism, and beyond, and they’ll no doubt help more forensic technicians comb through the ashes of the dumpster fire that is our social media.