On the Extraction and Use of Arguments in Recommender Systems: A Case Study in the E-participation Domain

In this paper, we present ongoing work on the automatic extraction of arguments from textual content, and on the use of interconnected argument structures by recommender systems. Differently to the majority of existing argument mining methods –which only consider ‘premise’ and ‘claim’ as the components of an argument, and ‘support’ and ‘attack’ as the possible relations between argument components–, we propose an argumentation model based on a detailed taxonomy of argumentative relations. Moreover, we provide a lexicon of English and Spanish linguistic connectors categorized in our taxonomy. As a proof of concept, we apply a simple, yet effective method that makes use of the built taxonomy and lexicon to extract argument graphs from citizen proposals and debates of an e-participation platform. We then describe how the extracted graphs could be exploited to generate and explain argument-based recommendationsThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00

A flexible and lightweight interactive data mining tool to visualize and analyze digital citizen participation content

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]"© Author(s), {2020}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published inProceedings of the 36th Annual ACM Symposium on Applied Computing, http://dx.doi.org/10.1145/3412841.3442081Addressing information overload in current e-participation platforms, we present a lightweight web application consisting of a simple HTML-based data panel that, through the use of date, location and category based filters, and several interactive graphs, allows visualizing, exploring and analyzing data obtained from public deliberation platforms in an easy and clear way. The tool, which implements natural language processing, text similarity, and graph clustering techniques to group citizen proposals, may serve as a decision support system for the municipal government, and may contribute to increase the citizens' participation and engagementThis work was conducted with financial support from the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) and the Centre of Andalusian Studies (PR137/19

Analyzing Citizen Participation and Engagement in European Smart Cities

With the advent of smart cities (SCs), governance has been placed at the core of the debate on how to create public value and achieve a high quality of life in urban environments. In particular, given that public value is rooted in democratic theory and new technologies that promote networking spaces have emerged, citizen participation represents one of the principal instruments to make government open and close to the citizenry needs. Participation in urban governance has undergone a great development: from the first postmodernist ideals of countering expert dominance to today’s focus on learning and social innovation, where citizen participation is conceptualized as co-creation and co-production. Despite this development, there is a lack of research to know how this new governance context is taking place in the SC arena. Addressing this situation, in this article, we present an exhaustive survey of the research literature and a deep study of the experience in participative initiatives followed by SCs in Europe. Through an analysis of 149 SC initiatives from 76 European cities, we provide interesting insights about how participatory models have been introduced in the different areas and dimensions of the cities, how citizen engagement is promoted in SC initiatives, and whether the so-called creative SCs are those with a higher number of projects governed in a participatory wayThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P), and the Spanish Ministry of Culture and Sport (CAS18/00035

Automatic Intent-based Classification of Citizen–toGovernment Tweets

Social networking technologies offer opportunities for governments to engage with citizens. However, the inability to filter relevant citizens' messages out of the vast amount of available social media content lessens their impact. In this paper, we propose a set of categories encapsulating the different citizens' intents when directing messages to public institutions, e.g., complaining, making requests, and proposing solutions to existing problems. We present a novel artificial intelligence approach, built upon natural language processing and machine learning algorithms, that enables the categorisation of citizens' messages into such intents automatically, and at scale. Through an empirical evaluation on a Twitter dataset, we show the effectiveness of our approach in terms of categorisation performance. We also discuss the value of the presented solution, as a novel tool for governments to achieve a more effective and informed communication with citizensThis work was conducted with financial support from the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) and the Centre of Andalusian Studies (PR137/19). José L. Lavado is partially supported by the UAM–ADIC Chair for Data Science and Machine Learnin

A Chatbot for Searching and Exploring Open Data: Implementation and Evaluation in E-Government

© 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in DG.O2021: The 22nd Annual International Conference on Digital Government Research, https://doi.org/10.1145/3463677.3463681In this paper, we present a chatbot to access open government data. Differently to similar systems reported in the research literature, the developed chatbot not only allows searching for data collections, but also exploring information within the collections. The exploration is done via complex queries that are easily built by non-expert users through a natural language conversation. Moreover, as another novel, differentiating contribution, we report a conducted user study aimed to evaluate the chatbot according to the achievement of a number of public service values, as well as measuring distinct objective and subjective metrics. Experimental results show that the proposed system outperforms traditional methods followed in open data portalsThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) and the Centre of Andalusian Studies (PR137/19). The authors thank to all people who participated in the reported stud

Automating the synthesis of recommender systems for modelling languages

We are witnessing an increasing interest in building recommender systems (RSs) for all sorts of Software Engineering activities. Modelling is no exception to this trend, as modelling environments are being enriched with RSs that help building models by providing recommendations based on previous solutions to similar problems in the same domain. However, building a RS from scratch requires considerable effort and specialized knowledge. To alleviate this problem, we propose an automated approach to the generation of RSs for modelling languages. Our approach is model-based, and we provide a domain-specific language called Droid to configure every aspect of the RS (like the type and features of the recommended items, the recommendation method, and the evaluation metrics). The RS so configured can be deployed as a service, and we offer out-of-the-box integration of this service with the EMF tree editor. To assess the usefulness of our proposal, we present a case study on the integration of a generated RS with a modelling chatbot, and report on an offline experiment measuring the precision and completeness of the recommendationsThis project has received funding from the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813884, the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314

Structured argumentation modeling and extraction: Understanding the semantics of parliamentary content

In information overload scenarios, the volume, structure and complexity of generated data represent a challenge that hinders the content comprehension. Aiming to overcome these dissuasive issues, the modeling and extraction of arguments in textual content has become a prominent topic in the information retrieval field. In this paper, we propose a new argumentation model, where different semantic components and their relationships are considered. Our proposal aims to enhance state of the art approaches, which limit their scope to identifying chunks of text as argumentative or not, leading to large amounts of texts left unanalyzed. The presented model, differently to domain-specific corpus methods, is designed to enable a generic, cross-lingual semantic annotation that promotes reusability. As a proof of concept, the model is exemplified in a case study for an e-government platform intended to annotate semantically, and provide information retrieval and filtering functionalities on content produced in the Spanish ParliamentThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00

Semantic Annotation and Retrieval of Parliamentary Content: A Case Study on the Spanish Congress of Deputies

In this paper, we present an ontology-based annotation and retrieval approach for parliamentary content, such as debate transcripts and law proposals. Exploiting a number of domain ontologies, semantic web technologies and information retrieval techniques, our approach extracts topics, concepts and named entities (e.g., names of politicians and political parties) appearing in input documents. The domain ontologies were designed to support multilinguality, and were built from the United Nations taxonomy of sustainable development goals. The approach was instantiated with a text corpus extracted from the Spanish Congress of Deputies and is being integrated into an e-government platformThis work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00). The authors acknowledge CIECODE for providing the dataset used in this work, and thank its director –Javier Pérez– and members –Pablo Martín, Belén Agüero and Irene Matín– for their help and support in the project. They also thank Alejandro Bellogín for his help on the regular expression processin

Un modelo dimensional de emociones basado en etiquetas sociales: construcción de folcsonomías en dominios cruzados

En este trabajo se presenta un modelo dimensional de emociones basado en etiquetas sociales. El modelo se construye sobre un léxico generado automáticamente que caracteriza emociones por medio de términos sinónimos y antónimos. Este léxico se enlaza con diversas folcsonomías emocionales específicas de dominio. Se propone una serie de métodos para transformar perfiles de objetos basados en etiquetas sociales en perfiles emocionales. El objetivo de estos perfiles es su uso por parte de sistemas adaptativos y de personalización que permitan recuperar o recomendar contenidos en función del estado de ánimo del usuario. Para validar el modelo, se muestra que la representación de un conjunto de emociones básicas se corresponde con la del aceptado modelo de Russell. También se reportan resultados de un estudio de usuario que demuestran una alta precisión de los métodos propuestos para inferir emociones evocadas por objetos en los dominios del cine y la música.We present an emotion computational model based on social tags. The model is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms, and that is linked to multiple domain-specific emotion folksonomies extracted from entertainment social tagging systems. Using these cross-domain folksonomies, we develop a number of methods that automatically transform tag-based item profiles into emotion-oriented item profiles, which may be exploited by adaptation and personalization systems. To validate our model, we show that its representation of a number of core emotions is in accordance with the well-known psychological circumplex model of affect. We also report results from a user study that show a high precision of our methods to infer the emotions evoked by items in the movie and music domains

Exploiting Open Data to analyze discussion and controversy in online citizen participation

In this paper we propose a computational approach that applies data mining techniques to analyze the citizen participation recorded in an online digital platform. Differently to previous work, the approach exploits external knowledge extracted from Open Government Data for processing the citizens’ proposals and debates of the platform, enabling to characterize targeted issues and problems, and analyze the levels of discussion, support and controversy raised by the proposals. As a result of our analysis, we derive a number of insights and conclusions of interest and value for both citizens and government stakeholders in decision and policy making tasks. Among others, we show that proposals targeting issues that affect large majorities tend to be supported by citizens and ultimately implemented by the city council, but leave aside other very important issues affecting minority groups. Our study reveals that most controversial, likely relevant, problems do not always receive sufficient attention in e-participation. Moreover, it identifies several types of controversy, related to ideological and socioeconomic factors and political attitudesThis work was supported by the Spanish Ministries of Economy, Industry and Competitiveness (TIN2016-80630-P) and Science, Innovation and Universities (CAS18/00035