GEPSA , a tool for monitoring social challenges in digital press (2021)

Abstract: This papers presents a platform for monitoring press narratives with respect to several social challenges, including gender equality, migrations and minority languages. As narratives are encoded in natural language, we have to use natural processing techniques to automate their analysis. Thus, crawled news are processed by means of several NLP modules, including named entity recognition, keyword extraction,document classification for social challenge detection, and sentiment analysis. A Flask powered interface provides data visualization for a user-based analysis of the data. This paper presents the architecture of the system and describes in detail its different components. Evaluation is provided for the modules related to extraction and classification of information regarding social challenges.

## introduction today society presents several challenges concerning equality, diversity and inclusion. among them, this paper focuses on gender equality, migration and minority languages. when developing policies to address these challenges, evidence-based diagnostics are necessary, and the digital press is a source of evidence or more specifically the narratives of reality that they offer. this type of analysis has already been carried out for several social challenges such as immigration @xcite , gender equality @xcite and armed conflicts @xcite . in these narratives we can analyze different aspects or indicators referring to social challenges (importance of the challenge, agents involved, relevant issues in each challenge, etc.). however, narratives are encoded in natural language which implies that to automate their analysis we have to use natural language processing techniques. in this paper we describe gepsa, a tool that facilitates the analysis of social challenges on the narratives built in the digital press. for that aim, it automates the analysis of several macro key indicators (ki) associated with each challenge. it also offers navigation to inspect the different indicators at the micro level. automation is possible thanks to the use of different nlp techniques: entity extraction, keyword extraction, polarity, and text classification. the current demo includes models and resources developed for basque and spanish languages. from here on the paper is organized as follows: the next section gives details about the main features of the system and the indicators extracted for each social challenge analyzed. section 3 describes the architecture of the tool and its main modules. section 4 discusses the validation of the tool. the last section summarizes the paper contribution and conclusions drawn from this work. ## features gepsa is a tool that allows permanent listening of digital press sources. sources are customizable to each use case, and are defined at the beginning of the monitoring, depending on variables such as the geographical scope or the type of publications to include in the analysis. listening is permanent and consists of analyzing different macro indicators for three social challenges: immigration, gender equality, and minority languages. table1 lists the macro kis defined for each of the social challenges. different filters may be applied interactively to the indicators, thus allowing micro-analysis through access to specific news: ## system architecture gepsa is designed as a pipeline working on a document basis. its first module is responsible for crawling news data, and retrieved documents are passed through the various modules for information extraction. each module adds new enriched metadata to a common database. figure 1 offers a detailed view of the system components. lemmatization and named entity recognition and classification (nerc) module is responsible for extracting the people and organizations involved in an specific news article, and it also assigns male or female gender to extracted people named entities. keyword extraction module extracts most salient terms in a document and detects polar words among them. the next module uses supervised classifiers to label the thematic section of a document. the last module detects if a news article belongs to any of the analyzed challenges. ## user validation a tool such as gepsa should be validated by users in real case scenarios, if we want such automatic analysis tools to be adopted by social researchers and policy makers. in order to validate gepsa, a pilot study is being conducted using the tool as diagnostic tool for the aforementioned challenges. there are 5 participants in the study: the two social researchers that participated in the annotation of the datasets, two data scientists and a policy maker. the pilot will last for the first half of 2021 and is expected to generate an intermediate and a final report on aspects such as the validity of the data gathered, the indicators measured, and the usability of the interface. although we can not yet draw conclusions, preliminary feedback from participants shows that they find the interface intuitive and easy to use, and indicators provide accurate results both for macro and micro analysis. ## conclusions this paper presents a tool that assists researchers analyzing the narratives built by the press respect to specific social challenges, including gender equality, migrations and minority languages. the developed tool is an example of how by combining various nlp techniques we can automate the process of narrative analysis focused on social challenges. moreover, the tool is a showcase of how policy makers may observe those narratives based on evidences collected from a big data environment, according to several predefined key indicators and their temporal evolution. this is useful both to enact more inclusive policies and also to observe the impact of those policies. lastly, the user interface and access to data gathered since the beginning of 2020 are publicly available at https://talaia.elhuyar.eus:8978