Detecting the Magnitude of Events from News Articles

Agrawal, Ameeta; Sahdev, Raghavender; Davoudi, Heidar; Khonsari, Forouq; An, Aijun; McGrath, Susan

doi:10.1109/wi.2016.0034

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…The most similar research to ours was performed by Agrawal et al [1]. They introduced a method to extract the magnitude of violence from news articles.…”

Section: Detecting Factors Of Forced Migrationmentioning

confidence: 85%

“…To evaluate PD-SEF violence scores and to compare them with violence scores extracted from EOS articles using the method introduced in [1], we compare both sets of scores against UNHCR dataset and check for Pearson correlation between violence scores and number of refugees. The PD-SEF violence showed 22% more correlation with refugee displacement than violence scores of [1]. Figure 5 shows PD-SEF violence scores against average refugee population of Iraq and Syria.…”

Section: Violence Scores Evaluationmentioning

confidence: 99%

“…However, building a quantitative model that extracts and measures the forced migration signals (particularly violence in this research) and finds the relationship between violence and forced displacement remains relatively unexplored. To contribute to this problem, we introduce the PD-SEF framework 1 to detect signals of forced population displacement by analyzing news articles. We mainly focus on Iraq and Syria as case studies and our experiments are centered around detecting violence as the most critical indicator of forced migration.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Forced Population Displacement Using News Articles

Abrishamkar¹,

Khonsari²

2019

MLAIJ

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The world has witnessed mass forced population displacement across the globe. Population displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to allocate the necessary resources and inform the policymakers. The set of events that triggers population movements can be traced in the news articles. In this paper, we propose the Population Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract the signals of forced population displacement. PD-SEF measures and evaluates violence signals, which is a critical factor of forced displacement from it. Following signal extraction, we propose a displacement prediction model based on extracted violence scores. Experimental results indicate the effectiveness of our framework in extracting high quality violence scores and building accurate prediction models.

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“…The most similar research to ours was performed by Agrawal et al [1]. They introduced a method to extract the magnitude of violence from news articles.…”

Section: Detecting Factors Of Forced Migrationmentioning

confidence: 85%

Section: Violence Scores Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Forced Population Displacement Using News Articles

Abrishamkar¹,

Khonsari²

2019

MLAIJ

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“…Word vectors were trained to learn the vector representation of a given vocabulary. Word vectors have been used in many domains like sentiment analysis [6], detecting magnitude of events [20], named entity recognition [21], etc.…”

Section: Related Workmentioning

confidence: 99%

Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings

Chen¹,

Sahdev²,

Wu³

et al. 2019

Preprint

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Scene Classification has been addressed with numerous techniques in the computer vision literature. However with the increasing number of scene classes in datasets in the field, it has become difficult to achieve high accuracy in the context of robotics. In this paper, we implement an approach which combines traditional deep learning techniques with natural language processing methods to generate a word embedding based Scene Classification algorithm. We use the key idea that context (objects in the scene) of an image should be representative of the scene label meaning a group of objects could assist to predict the scene class. Objects present in the scene are represented by vectors and the images are re-classified based on the objects present in the scene to refine the initial classification by a Convolutional Neural Network (CNN). In our approach we address indoor Scene Classification task using a model trained with a reduced pre-processed version of the Places365 dataset and an empirical analysis is done on a real world dataset that we built by capturing image sequences using a GoPro camera. We also report results obtained on a subset of the Places365 dataset using our approach and additionally show a deployment of our approach on a robot operating in a real world environment.

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“…The central and subsidiary events manifest themselves in the article content through the event trigger words. Despite the importance of events in news analytics applications (Agrawal et al, 2016), to the best of our knowledge, no study has considered them in article dwell time analysis. We adapt the method proposed in (Yang and Mitchell, 2016) to extract the events at the article level.…”

Section: Article-level Event Detectionmentioning

confidence: 99%

Content-based Dwell Time Engagement Prediction Model for News Articles

Davoudi

Edall³

2019

Proceedings of the 2019 Conference of the North

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The article dwell time (i.e., expected time that users spend on an article) is among the most important factors showing the article engagement. It is of great interest to news agencies to predict the dwell time of an article before its release. It allows online newspapers to make informed decisions and publish more engaging articles. In this paper, we propose a novel content-based approach based on a deep neural network architecture for predicting article dwell times. The proposed model extracts emotion, event and entity-based features from an article, learns interactions among them, and combines the interactions with the word-based features of the article to learn a model for predicting the dwell time. We apply the proposed model to a real dataset from a national newspaper showing that the proposed model outperforms other state-of-the-art baselines.

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Detecting the Magnitude of Events from News Articles

Cited by 9 publications

References 16 publications

Predicting Forced Population Displacement Using News Articles

Predicting Forced Population Displacement Using News Articles

Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings

Content-based Dwell Time Engagement Prediction Model for News Articles

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