Digital advertising is now a commonplace feature of political communication in the United States. Previous research has documented the key innovations associated with digital political advertising and its consequences for campaigns and elections. However, a comprehensive picture of political spending on digital advertising remains elusive because of the challenges associated with accessing and analyzing data. We address this challenge with a unique dataset (N=3,639,166) derived from over 13 million expenditure records reported to the Federal Election Commission (FEC) between 2004 and 2020. Employing a machine learning model to classify expenditures into nine categories including digital ads and services, this paper makes four key observations. First, 2020 was a watershed election in the growth of digital campaign spending. Second, there are clear partisan differences in the resources allocated to digital advertising. Third, platform companies play a central role in an otherwise partisan market for digital ads and services. Fourth, digital platforms and consultants occupy a distinct ideological niche within each party.

We apply statistical techniques from natural language processing to a collection of Western and Hong Kong-based English-language newspaper articles spanning the years 1998-2020, studying the difference and evolution of its portrayal. We observe that both content and attitudes differ between Western and Hong Kong-based sources. ANOVA on keyword frequencies reveals that Hong Kong-based papers discuss protests and democracy less often. Topic modeling detects salient aspects of protests and shows that Hong Kong-based papers made fewer references to police violence during the Anti-Extradition Law Amendment Bill Movement. Diachronic shifts in word embedding neighborhoods reveal a shift in the characterization of salient keywords once the Movement emerged. Together these raise questions about the existence of anodyne reporting from Hong Kong-based media. Likewise, they illustrate the importance of sample selection for protest event analysis.

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