TopExNet: Entity-Centric Network Topic Exploration in News Streams

The recent introduction of entity-centric implicit network representations of unstructured text offers novel ways for exploring entity relations in document collections and streams efficiently and interactively. Here, we present TopExNet as a tool for exploring entity-centric network topics in streams of news articles. The application is available as a web service at https://topexnet.ifi.uni-heidelberg.de/ .Comment: Published in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 201

Media Coverage of Law Enforcement Use of Force and Disability

Disability intersects with other factors such as race, class, gender, and sexuality, to magnify degrees of marginalization and increase the risk of violence. When the media ignores or mishandles a major factor, as we contend they generally do with disability, it becomes harder to effect change.This white paper focuses on the three years of media coverage of police violence and disability since the death of a young man with Down syndrome, named Ethan Saylor, in January 2013. After reviewing media coverage of eight selected cases of police violence against individuals with disabilities, the paper reveals the following patterns in the overall data:? Disability goes unmentioned or is listed as an attribute without context.? An impairment is used to evoke pity or sympathy for the victim.? A medical condition or "mental illness" is used to blame victims for their deaths.? In rare instances, we have identified thoughtful examinations of disability from within its social context that reveal the intersecting forces that lead to dangerous use-of-force incidents. Such stories point the way to better models for policing in the future. We conclude by proposing best practices for reporting on disability and police violence

A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles

Infrastructures for digital research: new opportunities and challenges

No abstract available

Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Q-network, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases -- of shooting incidents, and food adulteration cases -- demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.Comment: Appearing in EMNLP 2016 (12 pages incl. supplementary material

What lies beneath: exploring links between asylum policy and hate crime in the UK

This paper explores the link between increasing incidents of hate crime and the asylum policy of successive British governments with its central emphasis on deterrence. The constant problematisation of asylum seekers in the media and political discourse ensures that 'anti-immigrant' prejudice becomes mainstr earned as a common-sense response. The victims are not only the asylum seekers hoping for a better life but democratic society itself with its inherent values of pluralism and tolerance debased and destabilised