We may experience strong moral outrage when we read a news headline that describes a prohibited action, but when we gain additional information by reading the main news story, do our emotional experiences change at all, and if they do in what way do they change? In a single online study with 80 participants the aim was to examine the extent to which emotional experiences (disgust, anger) and mo...

This paper proposes a novel approach to sentiment analysis that leverages work in sociology on symbolic interactionism. The proposed approach uses Affect Control Theory (ACT) to analyze readers’ sentiment towards factual (objective) content and towards its entities (subject and object). ACT is a theory of affective reasoning that uses empirically derived equations to predict the sentiments and ...

Automatic headline generation is a sub-task of document summarization with many reported applications. In this study we present a sequence-prediction technique for learning how editors title their news stories. The introduced technique models the problem as a discrete optimization task in a feature-rich space. In this space the global optimum can be found in polynomial time by means of dynamic ...

Rank Order of the Nine Most Frequent Hazard Stories, regarding only "ordinary" news stories, by Press Type (per cent) Table 3.9 Rank Order of the Nine Most Frequent Hazard Stories, regarding only "less ordinary" news stories, by Press Type (per cent) Table 4.1 Total Volume Devoted to the Various Hazards, together with Average Size and Headline Proportion of the Articles. Table 4.25 Personalized...

Objectives: This study is about the realization of critical discourse analysis on TV talkshows’ headlines corona delta variant. Methods: The data was gathered by collecting 31 headline news stories Corona from three channels: Metro TV, One, and Kompas TV. Result: result showed that at level micro, linguistic features used channels were dominated expressing declarative ideas. On macro, facts as ...

Headline generation is a task where the most important information of news article condensed and embodied into single short sentence. This normally addressed by summarization techniques, ideally combining extractive abstractive methods together with sentence compression or fusion techniques. Although Natural Language Generation (NLG) techniques have not been directly exploited for headline gene...

Fake news has become increasingly prevalent in recent years. The evolution of social websites spurred the expansion fake causing it to a mixture with truthful information. English detection had largest share studies, unlike Arabic detection, which is still very limited. phenomenon changed people and perspectives through revolts several Arab countries. False results distortion reality ignite cha...

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...

This work deals with determination of meaningful and terse cluster labels for News document clusters. We analyze a number of alternatives for selecting headlines and/or sentences of document in a document cluster (obtained as a result of an entity-event-duration query), and formalize an approach to extracting a short phrase from well-supported headlines/sentences of the cluster that can serve a...

According to the St. Louis POST-DISPATCH, Monsanto, the St. Louis chemical and biotechnology giant, last month announced it had recalled "small quantities" of a genetically engineered canola seed containing an unapproved gene that had gotten into the product by mistake. Canola is a crop grown for livestock feed, and for oil consumed by humans. The canola-recall story, only 84 words long, was bu...