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

Ba, Cheick Tidiane; Choquet, Chloé; Interdonato, Roberto; Roche, Mathieu

doi:10.1145/3524458.3547240

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The topic modeling findings of this study can be used in data triangulation with data related to food security from other sources, including sources that are not social media. Previous research has used topic modeling of YouTube and newspaper data and found some consistency between the regions discussing food security and household survey data on the food security risk of these same regions, potentially serving as early warning signals for at-risk areas [84]. Our study found that the topics in food security social media data reflected all the dimensions of food security as defined by the Food and Agriculture Organization [3] and Clapp et al [39] to different extents.…”

Section: Xsl • Fomentioning

confidence: 49%

“…These techniques could also include using multiple platforms of information-from news articles to different social media platforms and using search terms beyond hashtags-to capture a wider understanding. Infoveillance techniques also have the potential to track trends in the prevalence of food insecurity, thereby enabling the public health sector to improve some of the major effects of food insecurity on health status in a more proactive way by detecting early warning signals [84]. Currently, the prevalence of food insecurity is not completely understood or able to be tracked because of the difficulty in obtaining data and the use of different tools that do not measure all the dimensions of food security [96,97].…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

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Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

Molenaar,

Lukose,

Brennan

et al. 2024

J Med Internet Res

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Background Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. Objective This study aims to explore the potential use of NLP tools to gather insights from real-world social media data on the public health issue of food security. Methods A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia-based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. Results In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): “Global production,” “Food insecurity and health,” “Use of food banks,” “Giving to food banks,” “Family poverty,” “Food relief provision,” “Global food insecurity,” “Climate change,” “Australian food insecurity,” and “Human rights.” The topic “Giving to food banks,” which focused on support and donation, had the highest proportion of positive sentiment, and “Global food insecurity,” which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. Conclusions In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence-based decision-making in this area.

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Section: Xsl • Fomentioning

confidence: 49%

Section: Suggestions For Future Researchmentioning

confidence: 99%

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

Molenaar,

Lukose,

Brennan

et al. 2024

J Med Internet Res

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“…In addition to traditional NLP, word embedding [13] can be used to extract information. [14] uses word embedding vectors to derive spatio-temporal characteristics and special indicators from text documents describing food security problems. The method is then used for analyzing the food crisis in West Africa.…”

Section: Related Work a Rule Identificationmentioning

confidence: 99%

Towards a (Semi-)Automatic Urban Planning Rule Identification in the French Language

Koptelov,

Holveck,

Cremilleux

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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One of the objectives of the Hérelles project is to find new mechanisms to facilitate the labeling (or semantization) of clusters from time series of satellite images. To achieve this, a proposed solution is to associate textual elements of interest with satellite data. The first step in this process consists of an automatic extraction of the information in the form of rules from urban planning documents composed in the French language. To address this challenge, we propose a method which is based on the multi-label classification of textual segments. It includes a special format for representing segments, in which each segment has a title and a subtitle. In addition, we propose a cascade approach aiming to deal with hierarchy of class labels. Finally, we develop several text augmentation techniques for the texts in French, which are able to improve the prediction results. We demonstrate experimentally that the resulting framework correctly classifies each type of segment with more than 90% of accuracy.

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“…In this study, we examine the ability of text mining methods to extract and analyze the qualitative information used as proxies for the national and regional food situation and its evolution over the last ten years in Burkina Faso from a corpus of newspapers from the country. The aim is to provide explainable indicators complementary to the automatic predictions of food security scores, i.e., as the ones obtained in our previous works based on the application of machine learning approaches on heterogeneous [3] and textual [2] data.…”

Section: Introductionmentioning

confidence: 99%

Mining News Articles Dealing with Food Security

Deléglise

Bégué

Interdonato

et al. 2022

Lecture Notes in Computer Science

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Food security is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for food insecurity and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. To this end, we propose an original and dedicated pipeline that combines different textual analysis approaches (e.g., word embedding, sentiment analysis, and discrimination calculation) to obtain an explanatory model evaluated on real-world and large-scale data. The results of our analyses have proven how our approach provides significant results that offer distinct and complementary qualitative information on the food security theme and its spatial and temporal characteristics.

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

Cited by 5 publications

References 15 publications

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

Towards a (Semi-)Automatic Urban Planning Rule Identification in the French Language

Mining News Articles Dealing with Food Security

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