Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Temporal Emotion Dynamics in Social Networks

[ES] El análisis de sentimientos en redes sociales se ha estudiado ampliamente durante la última década. A pesar de ello, las distintas categorías de sentimientos no se consideran adecuadamente en muchos casos, y el estudio de patrones de difusión de las emociones es limitado. Por lo tanto, comprender la importancia de emociones específicas será más beneficioso para diversas actividades de marketing, toma de decisiones empresariales y campañas políticas. Esta tesis doctoral se centra en el diseño de un marco teórico para analizar el amplio espectro de sentimientos y explicar cómo se propagan las emociones utilizando conceptos de redes temporales y multicapa. Particularmente, nuestro objetivo es proporcionar información sobre el modelado de la influencia de las emociones y como esta afecta a los problemas de estimación de las emociones y a la naturaleza dinámica temporal en la conversación social. Para mostrar la eficacia del modelo propuesto, se han recopilado publicaciones relacionadas con diferentes eventos de Twitter y hemos construido una estructura de red temporal sobre la conversación. En primer lugar, realizamos un análisis de sentimientos adoptando un enfoque basado en el léxico y en el modelo circunflejo de emociones de Russell que mejora la efectividad de la caracterización del sentimiento. A partir de este análisis investigamos la dinámica social de las emociones presente en las opiniones de los usuarios analizando diferentes características de influencia social. A continuación, diseñamos un modelo estocástico temporal basado en emociones para investigar el patrón de participación de los usuarios y predecir las emociones significativas. Nuestra contribución final es el desarrollo de un modelo de influencia secuencial basado en emociones mediante la utilización de redes neuronales recurrentes que permiten predecir emociones de una manera más completa. Finalmente, el documento presenta algunas conclusiones y también describe las direcciones de investigación futuras.[CA] L'anàlisi de sentiments en xarxes socials s'ha estudiat àmpliament durant l'última dècada. Malgrat això, les diferents categories de sentiments no es consideren adequadament en molts casos, i l'estudi de patrons de difusió de les emocions és limitat. Per tant, comprendre la importància d'emocions específiques serà més beneficiós per a diverses activitats de màrqueting, presa de decisions empresarials i campanyes polítiques. Aquesta tesi doctoral se centra en el disseny d'un marc teòric per a analitzar l'ampli espectre de sentiments i explicar com es propaguen les emocions utilitzant conceptes de xarxes temporals i multicapa. Particularment, el nostre objectiu és proporcionar informació sobre el modelatge de la influència de les emocions i com aquesta afecta als problemes d'estimació de les emocions i a la naturalesa dinàmica temporal en la conversa social. Per a mostrar l'eficàcia del model proposat, s'han recopilat publicacions relacionades amb diferents esdeveniments de Twitter i hem construït una estructura de xarxa temporal sobre la conversa. En primer lloc, realitzem una anàlisi de sentiments adoptant un enfocament basat en el lèxic i en el model circumflex d'emocions de Russell que millora l'efectivitat de la caracterització del sentiment. A partir d'aquesta anàlisi investiguem la dinàmica social de les emocions present en les opinions dels usuaris analitzant diferents característiques d'influència social. A continuació, dissenyem un model estocàstic temporal basat en emocions per a investigar el patró de participació dels usuaris i predir les emocions significatives. La nostra contribució final és el desenvolupament d'un model d'influència seqüencial basat en emocions mitjançant la utilització de xarxes neuronals recurrents que permeten predir emocions d'una manera més completa. Finalment, el document presenta algunes conclusions i també descriu les direccions d'investigació futures.[EN] Sentiment analysis in social networks has been widely analysed over the last decade. Despite the amount of research done in sentiment analysis in social networks, the distinct categories are not appropriately considered in many cases, and the study of dissemination patterns of emotions is limited. Therefore, understanding the significance of specific emotions will be more beneficial for various marketing activities, policy-making decisions and political campaigns. The current PhD thesis focuses on designing a theoretical framework for analyzing the broad spectrum of sentiments and explain how emotions are propagated using concepts from temporal and multilayer networks. More precisely, our goal is to provide insights into emotion influence modelling that solves emotion estimation problems and its temporal dynamics nature on social conversation. To exhibit the efficacy of the proposed model, we have collected posts related to different events from Twitter and build a temporal network structure over the conversation. Firstly, we perform sentiment analysis with the adaptation of a lexicon-based approach and the circumplex model of affect that enhances the effectiveness of the sentiment characterization. Subsequently, we investigate the social dynamics of emotion present in users' opinions by analyzing different social influential characteristics. Next, we design a temporal emotion-based stochastic model in order to investigate the engagement pattern and predict the significant emotions. Our ultimate contribution is the development of a sequential emotion-based influence model with the advancement of recurrent neural networks. It offers to predict emotions in a more comprehensive manner. Finally, the document presents some conclusions and also outlines future research directions.Naskar, D. (2022). Temporal Emotion Dynamics in Social Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/180997TESI

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research