Publication exploring how headlines in The New York Times negotiated the fact of lying in click-friendly headlines.
Outliars presents a data set of lies highlighting the diversity of their contexts and the predictable patterns they express. Using headlines from a twenty-eight year archive from The New York Times, this study analyzes how headlines of the Trump era negotiated the fact of lying in contrast with previous administrations. The book combines the statistical term “outliers” but modifies the spelling to also mean the “calling out of liars.”
Through charts and diagrams, the book illustrates the volume of coverage that Trump received compared to preceding administrations. This dramatic increase in coverage coincides with the influence of technologies such as search engine optimization and headline A/B testing. The book explores how these techniques can result in the newsroom spreading misinformation.
Headlines are annotated to highlight keywords and punctuation that are common for various types of lies such as: deception, contradiction, and evasion. These labels and keywords follow strategies used in machine learning for creating training data. In doing so, the pages suggest that this collection of data could serve as an automated lie detector.
Combining discourse analysis and online publishing strategies, this book highlights the futility of calling out lies in the age of “hedgy headlines.” Showing how deceptive narratives are spread, this book empowers readers to filter out the noise of our information landscape. For newsrooms not willing to call out a lie directly, it is best for them to leave the liar out of coverage altogether.