The relative hostile media effect suggests that partisans tend to perceive the bias of slanted news differently depending on whether is in favor or against their sides. To explore an algorithmic vs. human source perceptions, this study conducts a 3 (author attribution: human, algorithm, human-assisted algorithm) x (news attitude: pro-issue, neutral, anti-issue) mixed factorial design online exp...

Two studies examine the effects of reporting rumors of sexual assaults on a college campus. Participants read headlines and short articles about an unfamiliar college. Key headlines and articles reported an increase in sexual assaults as a fact, a rumor, a denied rumor, or a proven falsehood. Participants then rated perceptions of the college, including opinions on the level of crime and safety...

This paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial Microblogs and News Task. The premise is that it is possible to understand market reaction over a company stock by measuring the positive/negative sentiment contained in the financial tweets an...

This paper describes a proposed document analysis system that aims at automatic indexing of digitized images of old newspaper microfilms. This is done by extracting news headlines from microfilm images. The headlines are then converted to machine readable text by OCR to serve as indices to the respective news articles. A major challenge to us is the poor image quality of the microfilm as most i...

Headline generation is a short-form variant of document summarization that has been studied in natural language processing. This paper presents a case study examining the application of several different headline generation models at The Washington Post. Currently for individual news articles, multiple different headlines are manually written in order to target different platforms such as the w...