A delivery driver posted a TikTok alleging she had been sexually assaulted by a customer. The deepfakes that followed reveal a growing digital blackface problem. When DoorDash delivery driver Livie Rose Henderson posted a video alleging that one of her customers sexually assaulted her in October, it set off a firestorm of reactions. Henderson's TikTok claimed that when she was dropping off a delivery in Oswego, New York, she found a customer's front door wide open and inside, a man on the couch with his pants and underwear pulled down to his ankles. Henderson was dubbed the "DoorDash Girl," and her video accrued tens of millions of views, including some supportive and consoling responses to what she said she had endured on the job as a young woman.

Ohio Republican candidates who are vying to take on Democratic incumbent Sen. Sherrod Brown clashed over border security and drone strikes in Mexico during Monday's first statewide debate. Facing off at WJW Fox 8 Studios in Cleveland, businessman Bernie Moreno, Ohio Secretary of State Frank LaRose and state Sen. Matt Dolan generally agreed on a few issues, including calling for fully securing the U.S.-Mexico border, but then quickly clashed upon delving into the immigration crisis further. Dolan accused Moreno, who was endorsed by former President Trump, of wanting "to militarize the federal government and deport children" for his stance calling for deporting anybody in the country illegally. LaRose called earlier Monday for President Biden to deploy three military divisions to the border, which Dolan said was irresponsible. "We need to work with the Mexican government, we need to be tough with the Mexican government," Dolan said.

Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this paper, we introduce a novel method to synthesize data for CQA with various question types, including open-ended, closed-ended, and unanswerable questions. We design a different generation flow for each question type and effectively combine them in a single, shared framework. Moreover, we devise a hierarchical answerability classification (hierarchical AC) module that improves quality of the synthetic data while acquiring unanswerable questions. Manual inspections show that synthetic data generated with our framework have characteristics very similar to those of human-generated conversations. Across four domains, CQA systems trained on our synthetic data indeed show good performance close to the systems trained on human-annotated data.