research-article

Explaining food security warning signals with YouTube transcriptions and local news articles

Authors:

Cheick Tidiane Ba,

Chloé Choquet,

Roberto Interdonato,

Mathieu RocheAuthors Info & Claims

GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good

Pages 315 - 322

https://doi.org/10.1145/3524458.3547240

Published: 07 September 2022 Publication History

Abstract

Food security is a major concern in many countries all over the world. After a relatively long period characterized by a positive trend, the number and severity of food insecurity situations has been growing again in recent years, with alarming projections for the near future. While several Early Warning Systems (EWS) exist to monitor this phenomenon and guide the interventions of governments and ONGs, such systems rely on a narrow set of data types, i.e., mainly satellite imagery and survey data. These data can explain just a limited number of the multiple factors that impact on food security, thus producing an incomplete picture of the real scenario. In this work, we propose a spatio-temporal analysis of unconventional textual data (i.e., YouTube transcriptions and articles from local news papers) to support the explanatory process of food insecurity situations. This data, being completely exogenous to the one used in currently active EWS, can offer a different and complementary perspective on the causes of such crises. We focus on the area of West Africa, which has been at the center of many humanitarian crisis since the beginning of this century. By exploiting state of the art text mining techniques on a corpus of textual documents in French (including video transcriptions extracted from the YouTube channels of four West African news broadcasters and news articles obtained from the online versions of two local newspapers of Burkina Faso) we will analyze food security situations in different regions of the study area in recent years, by also proposing a food security indicator based on textual data, namely TXT-FS.

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Cited By

Molenaar ALukose DBrennan LJenkins EMcCaffrey T(2024)Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling StudyJournal of Medical Internet Research10.2196/4782626(e47826)Online publication date: 21-Mar-2024
https://doi.org/10.2196/47826
Koptelov MHolveck MCremilleux BReynaud JRoche MTeisseire M(2023)Towards a (Semi-)Automatic Urban Planning Rule Identification in the French Language2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302561(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302561
Ahn YYan MLin YWang Z(2023)HungerGist: An Interpretable Predictive Model for Food Insecurity2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386346(1591-1600)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386346
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cover image ACM Conferences

GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good

September 2022

436 pages

ISBN:9781450392846

DOI:10.1145/3524458

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 September 2022

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Cited By

Molenaar ALukose DBrennan LJenkins EMcCaffrey T(2024)Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling StudyJournal of Medical Internet Research10.2196/4782626(e47826)Online publication date: 21-Mar-2024
https://doi.org/10.2196/47826
Koptelov MHolveck MCremilleux BReynaud JRoche MTeisseire M(2023)Towards a (Semi-)Automatic Urban Planning Rule Identification in the French Language2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302561(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302561
Ahn YYan MLin YWang Z(2023)HungerGist: An Interpretable Predictive Model for Food Insecurity2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386346(1591-1600)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386346
Deléglise HBégué AInterdonato RMaître d’Hôtel ERoche MTeisseire M(2023)How can text mining improve the explainability of Food security situations?Journal of Intelligent Information Systems10.1007/s10844-023-00832-xOnline publication date: 11-Dec-2023
https://doi.org/10.1007/s10844-023-00832-x

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