Newspaper Signaling for Crisis Prediction (2024)

Abstract: To establish sophisticated monitoring of newspaper articles for detecting crisis-related signals, natural language processing has to cope with unstructured data, media, and cultural bias as well as multiple languages. So far, research on detecting signals in newspaper articles is focusing on structured data, restricted language settings, and isolated application domains. When considering complex crisis-related signals, a high number of diverse newspaper articles in terms of language and culture reduces potential biases. We demonstrate MENDEL – a model for multi-lingual and open-domain newspaper signaling for detecting crisis-related indicators in newspaper articles. The model works with unstructured news data and combines multiple transformer-based models for pre-processing (STANZA) and content filtering (RoBERTa, GPT-3.5). Embedded in a Question-Answering (QA) setting, MENDEL supports multiple languages (>66) and can detect early newspaper signals for open crisis domains in real-time.

## introduction monitoring newspaper sources has a significant impact on companies, health organizations, and civil defense in preparing for and responding to emerging trends, conflicts, and crises situations effectively @xcite . nonetheless, up until now, newspaper signaling is not applied in crisis and risk management in practice as it is challenging due to the amount of unstructured data, media, cultural bias @xcite , and multiple languages @xcite . so far, research on detecting potential signals for crisis-related events based on newspaper articles is focusing on structured data @xcite @xcite @xcite , restricted language settings (luca barbaglia and manzan, 2023), and isolated application domains, e.g., mobility, finances @xcite . the existing systems that monitors newspapers (gaia-x, 2022; eurostat, 2023) only aggregates and visualize current news, they don't posses advanced language processing capabilities. however, mendel distinguishes itself by processing unstructured newspaper data in real-time, and its use of large language models for multi-lingual content filtering and context-based crisis forecasting, offering a broader and more dynamic approach to early crisis signaling by encouraging organizational preparedness and supporting a rapid and effective response @xcite . the major challenge in using newspaper articles for crisis prediction is the management of unstructured data. most approaches tackle this challenge by specifying a domain ontology for converting unstructured data into structured data, e.g., @xcite ; facing all the wellknown disadvantages in performance, flexibility, and openness with regard to domains. thus, multilingual issues as well as media and cultural biases in newspaper articles written by diverse journalists for diverse newspapers in diverse countries on the same events cannot be captured @xcite . furthermore, the media landscape in each country has an influence on how events are portrayed @xcite . therefore, considering a high number of diverse newspaper resources in terms of language and culture reduces potential biases when seeking to identify complex crisis-related signals in newspaper articles. in this paper, we demonstrate mendel -a model for multi-lingual and open-domain newspaper signaling for detecting crisis-related indicators in newspaper articles (cf. figure 1 ). our model works with unstructured data of newspaper articles and combines multiple state-of-the-art transformer-based models @xcite for pre-processing (stanza @xcite ) and content filtering (xlm-roberta @xcite , gpt-3.5 @xcite ). mendel supports multiple of the most spoken languages in the world (>66) (e.g., mandarin chinese, spanish, english, hindi, arabic). the model is able to detect newspaper signals for open domains, e.g., energy, finances, and supply chains, that can be directly adjusted by the user in terms of keywords. one appeal of the model is the usage of purely unstructured data on newspaper articles for processing signals in realtime in contrast to other approaches mixing those data with already existing structured data suffering from lower quality and timeliness, e.g., wikidata @xcite @xcite . furthermore, mendel covers a 2-step filtration pipeline based on roberta @xcite and cosine similarity for determining domain relevance and crisis reference enabling an extensive filtration of articles with high accuracy and less redundancy in distinction from other approaches, that use no filtration, restricted approaches and only sentiment analyses 1 @xcite . the model provides the risk and warnings, statistical trends of the crisis and is exemplified within a qa system as a natural language assistant in risk and crisis management (cf. figure 1 ) 2 . we were able to evaluate the proposed approach by means of a set of newspaper articles (total: 18,673 news articles) in terms of performance in identifying potential signals for economic recession and energy-related crisis situations (i.e., availability and costs of energy like gas, oil, coal, solar, wind, supply chain disruption, mobility, etc) in germany. ## multi-lingual and open-domain newspaper signaling for crisis prediction we present mendel, a model for multi-lingual and open-domain newspaper signaling for crisis prediction powered with a real-time alert system, statistical trend visualization, and a qa chat-bot. in this paper, we explore crises defined as periods of substantial instability that disrupt the normal functioning of systems, leading to notable consequences; specifically, we concentrate on events that are predictable but challenging to influence, e.g. supply chain disruptions, mobility, rise in energy prices, economic crisis etc as outlined in various crisis taxonomies, including @xcite . the framework consists of four main modules (cf. figure 2 ): data acquisitions, data-processing pipeline, two-stage data filtration, and context-based reasoning and forecasting. mendel operates on domain-specific keywords given by a user with additional parameters such as language, country, and time frame. this serves as input to the data acquisition module which consists of several components, charged with generating domain-specific keywords, extracting newspaper articles, and a data parser. outputs of these components are fed to the dataprocessing module which handles data cleaning by removing stopwords, punctuation followed by tokenizer and lemmatizer. this module processes the data that can be directly fed to our two-stage data filtration, which is responsible to filters the articles based on users specified crisis domain and by finding future and present tenses in articles. outputs are further passed to the context-based reasoning and forecasting module. it generates, risks and warnings for the filtered articles and also provide statistical trend visualization for six months period. it also provides relevant keywords and qa chatbot. overall, mendel pipeline uses completely unstructured data, i.e., newspaper articles, supports open crisis domain, multiple languages and provide early signaling and warnings. to introduce the proposed approach in the demo, we give a short example course of detecting crisisrelated indicators in newspaper articles, starting with the domain name provided by the user e.g. 'high energy prices' and ending with identified domain-specific newspaper signals. for the following, imagine a user, such as a company, looking for signals of energy-related crises due to reduced availability or rising costs of energy such as gas, oil, coal, supply chain disruption and mobility, etc. mendel would provide them with alert trends in the form of risk and warnings for rising energy prices. additionally, it provides visualizations of the statistical crisis trend over 6 months period. ## implementation and evaluation based on the proposed model mendel (cf. figure 2 ), we implemented a newspaper signaling service for crisis and risk management foot_3 . the service architecture has been deployed using python and django while the client side interface has been designed as a web interface using html, css, and javascript. the system accepts keywords by the user in the form of plain text along with specific countries, languages, and time frames. in response, the system presents domain-specific crisis signals and highlights the most influential articles. moreover, it delivers alerts with confidence and severity levels, along with the relevant keywords foot_4 . ## conclusion we considered real-time newspaper signaling for detecting crisis-related indicators based on purely unstructured data. so far, research on detecting signals in newspaper articles is focusing on structured data, restricted language settings, and isolated application domains, giving little attention to the thereby induced potential biases. we introduced mendel -a model for multi-lingual and opendomain newspaper signaling for detecting crisisrelated indicators in newspaper articles. the model works with unstructured data from newspaper articles and combines multiple transformer-based models for pre-processing (stanza) and content fil-tering (xlm-roberta, gpt-3.5). embedded in a question-answering setting, mendel supports multiple spoken languages in the world (>66) and is able to detect newspaper signals for open domains in real time. we were able to evaluate the proposed approach by identifying potential signals for events (e), economic recession, and energyrelated crisis situations. in terms of performance, we evaluated our alert and forecasting model by creating human-annotated data and achieved up to 80% average micro-f1 score. we also were able to identify the potential signals for recession and energy-related crisis, from four (t -1 ), eight (t -2 ), and twelve (t -3 ) weeks before the crisis event occurred.