The untapped potential of mining news media events for understanding environmental change

Buckingham, Kathleen; Brandt, John; Anderson, Will; Amaral, Luiz Fernando do; Singh, Ruchika

doi:10.1016/j.cosust.2020.08.015

Cited by 13 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With significant advances in data sciences, we believe the use of social media and news mining methods have immense potential for application in agriculture and food systems. Multiple case studies have highlighted the use of social media analysis in climate sciences (Buckingham et al, 2020) and disaster management (Kryvasheyeu et al, 2016;Kibanov et al, 2017;Cecinati et al, 2019). Future research agenda can focus on utilizing big data analytics for farm risk management, particularly localized weather events (like hailstorms, landslides, inundation, cloud burst and lightninginduced fire events, among others) which are difficult to monitor due to data scarcity (especially in developing countries) and cause significant farm distress (Prein and Holland, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, therefore, we utilize news mining as an innovative data collection methodology to analyze the immediate impact of COVID-19 on the Indian food supply chain. News mining is an upcoming tool that can generate useful and actionable insights, especially for dynamic scenarios such as the ongoing pandemic (Buckingham et al, 2020;Jahanbin and Rahmanian, 2020;Sadman et al, 2021). In fact, recent studies have used media analysis to understand the impact of the COVID-19 pandemic on various sectors including public health and food (Bai et al, 2020;Thomas et al, 2020;Moriom Khatun et al, 2021;Suryadi, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain

Vyas

Chanana

et al. 2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

COVID-19 pandemic has resulted in widespread global disruptions. While much is being discussed about the health and economic impacts, there has been a limited focus on the immediate food sector shocks and their related social implications in developing countries, especially when the farmer surveys cannot be conducted due to mobility restrictions in many countries. To overcome these challenges, this study uses news mining and content analysis of media articles published from February to April 2020, to assess the early impacts of the COVID-19 pandemic on the food supply chain and farm distress in India. It also presents the media perception of the impact of the pandemic and resulting policy measures using sentiment analysis, in addition to the cross-tabulation of results that show differential impacts across food supply chain components among different commodity groups and regions. The results show wide-scale impacts across different components of the food supply chain ranging from crop harvesting and processing, distribution and logistics to disruptions across food markets, as represented by 22, 11 and 30% of total articles, respectively. The impacts are also differentiated by commodity groups, with animal products having more trade and demand-side issues, logistic bottlenecks in fruits and vegetables and crops showing problems in labor availability and harvesting. Sentiment analysis of news items shows a spike in the negative sentiment immediately post the national lockdown, with relatively less negativity in subsequent weeks due to large-scale policy and community action. Sentiment classification along different indicators shows the highest negative sentiment for animal products (85%) in commodity groups, western states of India (78%) among different regions, and food supply (85%) and markets (83%) among supply chain components. Further, extreme weather analysis (using excess rainfall events) shows that farmers faced compound risks from the COVID-19 pandemic and extreme weather events in many parts of the country. The results highlight the importance of building resilient food systems, especially when the biotic and abiotic shocks are projected to increase globally due to many drivers including biodiversity loss and climate change.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain

Vyas

Chanana

et al. 2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

show abstract

“…Buckingham et al determine the potential of new data sources by analyzing the data, applications, and methods of gdelt to understand the changing events in the news media, help understand social changes, and detect large-scale environmental changes. News mining plays an increasingly important role (Buckingham et al 2020) [10]. Reddy et al only uses the features of news text to detect false news methods.…”

Section: Related Workmentioning

confidence: 99%

“…In formula (10), s i and s j represent different sentences, respectively, vec(s i ) represents the vector of the sentence, and vec(s i ) � (􏽐 w k ∈s i vec(w k )/|s i |) and EE represent the words in the sentence.…”

Section: P(w|context(w)) � F(w Context(w) ϕ) (9)mentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the rapid development of the information technology, mining news content efficiently is a difficult problem faced by the government and enterprises. The classification, clustering, and prediction of neural network are used in news mining. A keyword based on neural network and word vector are proposed as a text feature model, and the model is compared with other neural network models. This paper studies the vectorization of text for similarity recommendation and introduces two models: word vector model and text vector model doc2vec based on neural network. In the model with word vector as feature vector, the recommendation accuracy is about 75.35%, doc2vec model is about 44.5%, and the recommendation accuracy of the model with keyword as text is about 88.61%. The recommendation accuracy is about 91%, and the performance has greatly improved. The more the number of keywords, the better the recommendation effect of the model. When the number is about 20, the improvement of the recommendation effect tends to be gentle, and the continuous increase of the number of keywords will increase the operation time of the model. It is proposed that the keyword and word vector based on neural network together as the text vector model can more accurately mine news data, quickly obtain news information, and make prediction and early warning for many industries such as the real estate industry.

show abstract

“…News media can serve as a near-real-time geolocated information, which can support the understanding of social movements and earlywarning systems. "Combining news media with social and biophysical data is important to verify results and limit biases in analysis" (Buckingham et al, 2020). One of the issues concerning urban environments is energy efficiency and carbon emissions, for which net zero energy movements seek to bring about a solution as well as the application of a resilience ecological framework for net zero energy research (Hu and Pavao-Zuckerman, 2019).…”

Section: Overview Of Big Data-based Climate Change Analysismentioning

confidence: 99%

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

View full text Add to dashboard Cite

The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

show abstract

The untapped potential of mining news media events for understanding environmental change

Cited by 13 publications

References 30 publications

From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain

From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Contact Info

Product

Resources

About