Indonesian people’s resilience detection method based on big data

The resilience condition of the Indonesian people in facing threats, disturbances, obstacles, and challenges (AGHT) can be identified through conversations on social media. The conversational data of social media users is important data for understanding the national resilience of the Indonesian people. The method developed is more explorative, descriptive, and quantitative by describing the variable: volume of social media users, user profiles, reach, conversation trends, types of issues, top tweets, emotion, sentiment, people who influence (top influencer), intermediary (bridge), and robot analysis (bot analysis). The research sample is from March 1, 2022, to May 1, 2022. Consideration of the timing is due to many public reactions to the “three periods” issue. The results of this study indicate that the three-period issue is the most dominant compared to other topics. The issue of “three periods” spread throughout Indonesia, and the most dominant was in DKI Jakarta Province. The social media users’ profile shows that the issue of three periods is mostly discussed by users between the ages of 19 and 29. Men are more dominant in discussing the “three periods” issue than women. Most Indonesian people reject the three-period issue. It shows that the resilience of the Indonesian people is exceptional because they can confront negative issues

Detecting and explaining unfairness in consumer contracts through memory networks

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes

Detecting and explaining unfairness in consumer contracts through memory networks

Published online: 11 May 2021Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes. Sponsor information: Francesca Lagioia has been supported by the European Research Council (ERC) Project “CompuLaw” (Grant Agreement No. 833647) under the European Union’s Horizon 2020 research and innovation programme. Paolo Torroni has been partially supported by the H2020 Project AI4EU (Grant Agreement No. 825619). Marco Lippi would like to thank NVIDIA Corporation for the donation of the Titan X Pascal GPU used for this research.Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement

Big Data for Qualitative Research

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher

Big Data for Qualitative Research

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher

Management Responses to Online Reviews: Big Data From Social Media Platforms

User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided

Dinámica social de las emociones: Redes sociales y patrones emocionales colectivos analizados mediante técnicas de machine learning e Inteligencia Artificial

La presente Tesis, realizada mediante un compendio de publicaciones, representa la línea de investigación seguida por el Autor a lo largo de dos etapas distintas. En ambas etapas, sin embargo, la temática de investigación ha permanecido coherente: las redes sociales, los lazos interindividuales, y las emociones que acompañan a las dinámicas de socialización. Lo que ha variado es la metodología de análisis. En primer lugar, a través del trabajo de campo y la creación de un nuevo test sobre la desaparición de las redes sociales del individuo (el grave problema de detectar la soledad no deseada entre los mayores) se ha procedido a analizar la conexión entre los estratos de relación social (lazos fuertes versus lazos débiles) junto con la respectiva presencia de emociones bien diferenciadas (primarias versus secundarias) en el contexto de los procesos adaptativos del "sociotipo". Paralelamente, todo ello se ha continuado a otro nivel mediante el uso de herramientas de inteligencia artificial para el análisis de sentimientos (sentiment analysis), combinando estas técnicas de manera novedosa con machine learning y análisis estadístico multivariante. Estas técnicas se han aplicado, entre otros trabajos, al análisis de intercambios masivos de mensajes en las redes sociales durante la reciente pandemia y al estudio de compilaciones de noticias publicadas sobre una catástrofe natural, como la reciente erupción del volcán Cumbre Vieja en las Islas Canarias. Los resultados obtenidos a lo largo de esta línea de investigación pueden contribuir, por un lado, a mejorar la detección de la soledad en personas mayores y a clarificar los procesos emocionales en las relaciones sociales en general. Por otro lado, y muy especialmente, al seguimiento de las consecuencias en la opinión pública de las decisiones y políticas adoptadas tanto en situaciones de graves desafíos sanitarios como frente a catástrofes naturales. Indudablemente, la repercusión multidisciplinar de este tipo de estudios de análisis de sentimientos, que complementan la teoría de decisión y el análisis de riesgos, es cada vez mayor y abarca múltiples ramas de las ciencias sociales, la economía, las ciencias políticas y las ciencias de la comunicación.<br /