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The rise of geotargeted online advertising has disrupted the business model of local journalism, but it remains ambiguous whether online advertising platforms can effectively reach local audiences. To address this ambiguity, we present a focused study auditing the positional accuracy of geotargeted display advertisements on Google. We measure the frequency and severity of geotargeting errors by targeting display ads to random ZIP codes across the United States, collecting self-reported location information from users who click on the advertisement. We find evidence that geotargeting errors are common, but minor in terms of advertising goals. While 41% of respondents lived outside the target ZIP code, only 11% lived outside the target county, and only 2% lived outside the target state. We also present details regarding a high volume of suspicious clicks in our data, which made the cost per sample extremely expensive. The paper concludes by discussing implications for advertisers, the business of local journalism, and future research. 

This article explores how Twitter’s algorithmic timeline influences exposure to different types of external media. We use an agent-based testing method to compare chronological timelines and algorithmic timelines for a group of Twitter agents that emulated real-world archetypal users. We first find that algorithmic timelines exposed agents to external links at roughly half the rate of chronological timelines. Despite the reduced exposure, the proportional makeup of external links remained fairly stable in terms of source categories (major news brands, local news, new media, etc.). Notably, however, algorithmic timelines slightly increased the proportion of “junk news” websites in the external link exposures. While our descriptive evidence does not fully exonerate Twitter’s algorithm, it does characterize the algorithm as playing a fairly minor, supporting role in shifting media exposure for end users, especially considering upstream factors that create the algorithm’s input—factors such as human behavior, platform incentives, and content moderation. We conclude by contextualizing the algorithm within a complex system consisting of many factors that deserve future research attention. 

This paper presents a crowdsourced auditing framework for news aggregators and applies it to the trending section of Apple News. The framework audits the aggregator algorithm, determining the refresh interval and detecting the presence of "adaptation" (an aggregator presenting different headlines based on a user's location or individual preferences). It is also used for a content audit which tabulates the distribution of news sources found in the aggregator. We deploy this framework on the trending stories section of Apple News, observing (1) a refresh interval of approximately 60 minutes, (2) adaptation at the user level, and (3) a unique distribution of news sources that prompts further investigation.