Geotagging Text Content With Language Models and Feature Mining

Kordopatis-Zilos, Giorgos; Papadopoulos, Symeon; Kompatsiaris, Ioannis

doi:10.1109/jproc.2017.2688799

Cited by 27 publications

(24 citation statements)

References 32 publications

(32 reference statements)

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“…Recent language modeling-based approaches have given better results. Among these, Kordopatis-Zilos et al [27] created a rectangular grid of cells and generated term-cell probabilities. The term occurrence probabilities are calculated from processing a geotagged corpus.…”

Section: Discussionmentioning

confidence: 99%

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Estimating spatiotemporal focus of documents using entropy with PMI

Yaşar¹,

Tekir²

2020

Turk J Elec Eng & Comp Sci

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Many text documents are spatiotemporal in nature, i.e. contents of a document can be mapped to a specific time period or location. For example, a news article about the French Revolution can be mapped to year 1789 as time and France as place. Identifying this time period and location associated with the document can be useful for various downstream applications such as document reasoning or spatiotemporal information retrieval. In this paper, temporal entropy with pointwise mutual information (PMI) is proposed to estimate the temporal focus of a document. PMI is used to measure the association of words with time expressions. Moreover, a word's temporal entropy is considered as a weight to its association with a time point and a single time point with the highest overall score is chosen as the focus time of a document. The proposed method is generic in the sense that it can also be applied for spatial focus estimation of documents. In the case of spatial entropy with PMI, PMI is used to calculate the association between words and place entities. The effectiveness of our proposed methods for spatiotemporal focus estimation is evaluated on diverse datasets of text documents. The experimental evaluation confirms the superiority of our proposed temporal and spatial focus estimation methods.

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Section: Discussionmentioning

confidence: 99%

“…Kordopatis-Zilos et al [27] presented an approach for estimating locations using text annotations based on refined language models that are learned from massive corpora of social media annotations. They also explored the impact of different feature selection and weighting techniques on the performance of the approach.…”

Section: Related Workmentioning

confidence: 99%

Estimating spatiotemporal focus of documents using entropy with PMI

Yaşar¹,

Tekir²

2020

Turk J Elec Eng & Comp Sci

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“…The latter may achieve high precision for the geo-localization of users at a country level, or even within country regions or cities [40,41]. However, to achieve a good performance at a finer grain classification, such as commune/neighborhood level, massive corpora of social media annotation is required [37]. On the other hand, the geo-localization of users based on their network (based on the assumption that users are more likely to interact with other users that are geographically closer to them) are more accurate at a finer level [42,43].…”

Section: Geolocation Of the Followersmentioning

confidence: 99%

Understanding news outlets’ audience-targeting patterns

2019

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The power of the press to shape the informational landscape of a population is unparalleled, even now in the era of democratic access to all information outlets. However, it is known that news outlets (particularly more traditional ones) tend to discriminate who they want to reach, and who to leave aside. In this work, we attempt to shed some light on the audience targeting patterns of newspapers, using the Chilean media ecosystem. First, we use the gravity model to analyze geography as a factor in explaining audience reachability. This shows that some newspapers are indeed driven by geographical factors (mostly local news outlets) but some others are not (national-distribution outlets). For those which are not, we use a regression model to study the influence of socioeconomic and political characteristics in news outlets adoption. We conclude that indeed larger, national-distribution news outlets target populations based on these factors, rather than on geography or immediacy.

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“…We inferred the location of posts, even in cases when it was not explicitly available through the geotagging metadata accompanying a tweet. Geolocation inference from text was based on the approach by Kordopatis-Zilos et al (2017), which employs refined language models learned from massive corpora of social media annotations. The results of the geolocation extraction for the posts of suspended and non-suspended users are presented in Figs.…”

Section: Spatial Distribution Of Accountsmentioning

confidence: 99%

Community-Oriented Policing and Technological Innovations

Leventakis¹,

Haberfeld²

2018

SpringerBriefs in Criminology

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SpringerBriefs in Criminology present concise summaries of cutting edge research across the fields of Criminology and Criminal Justice. It publishes small but impactful volumes of between 50-125 pages, with a clearly defined focus. The series covers a broad range of Criminology research from experimental design and methods, to brief reports and regional studies, to policy-related applications.The scope of the series spans the whole field of Criminology and Criminal Justice, with an aim to be on the leading edge and continue to advance research. The series will be international and cross-disciplinary, including a broad array of topics, including juvenile delinquency, policing, crime prevention, terrorism research, crime and place, quantitative methods, experimental research in criminology, research design and analysis, forensic science, crime prevention, victimology, criminal justice systems, psychology of law, and explanations for criminal behavior.SpringerBriefs in Criminology will be of interest to a broad range of researchers and practitioners working in Criminology and Criminal Justice Research and in related academic fields such as Sociology, Psychology, Public Health, Economics and Political Science. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland We also thank the following people f...

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Geotagging Text Content With Language Models and Feature Mining

Cited by 27 publications

References 32 publications

Estimating spatiotemporal focus of documents using entropy with PMI

Estimating spatiotemporal focus of documents using entropy with PMI

Understanding news outlets’ audience-targeting patterns

Community-Oriented Policing and Technological Innovations

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