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Found 9 papers, 1 papers with code
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.
Factoring Hate Speech: A New Annotation Framework to Study Hate Speech in Social Media
In this work we propose a novel annotation scheme which factors hate speech into five separate discursive categories.
Detecting Narrative Elements in Informational Text
For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e. g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success).
A Decomposition-Based Approach for Evaluating Inter-Annotator Disagreement in Narrative Analysis
For this purpose, we present a method for a conceptual decomposition of an existing annotation into two separate levels: (1) \textbf{whether} or not a narrative plot exists in the text, and (2) \textbf{which} plot elements exist in the text.
CompRes: A Dataset for Narrative Structure in News
We describe the process in which the dataset was constructed: first, we designed a new narrative annotation scheme, better suited for news media, by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success); then, we used that scheme to annotate a set of 29 English news articles (containing 1, 099 sentences) collected from news and partisan websites.
Computing Word Classes Using Spectral Clustering
Clustering a lexicon of words is a well-studied problem in natural language processing (NLP).
Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making
A large body of work in behavioral fields attempts to develop models that describe the way people, as opposed to rational agents, make decisions.
Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference
The run time complexity of state-of-the-art inference algorithms in graph-based dependency parsing is super-linear in the number of input words (n).
