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

Reorganizing Educational Institutional Domain using Faceted Ontological Principles

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval.Comment: 26 pages, 12 figures, KNOWLEDGE ORGANIZATION Journal Pape

Pattern of social media engagements by the learners of a library and information science MOOC course: an analytical study

56-66This study aims to highlight the popularity of Emerging Trends & Technologies in Library & Information Services (ETTLIS) course and investigate the learners' involvement using the YouTube Channel and Discussion Forum of the course. The authors statistically analyzed the learner's engagement in the course by using social media channels. It was found that the learners' active participation in the online discussion forum saw an increase from time to time, and the performance of social media involvement also got popularized through the YouTube channel. The paper, based on social media analytics of the course ETTLIS, suggests the possibility of the development of a set of stable performance indicators based on online engagements in LMS platforms

Pattern of social media engagements by the learners of a library and information science MOOC course: an analytical study

This study aims to highlight the popularity of Emerging Trends & Technologies in Library & Information Services (ETTLIS) course and investigate the learners' involvement using the YouTube Channel and Discussion Forum of the course. The authors statistically analyzed the learner's engagement in the course by using social media channels. It was found that the learners' active participation in the online discussion forum saw an increase from time to time, and the performance of social media involvement also got popularized through the YouTube channel. The paper, based on social media analytics of the course ETTLIS, suggests the possibility of the development of a set of stable performance indicators based on online engagements in LMS platforms

Emotion Dynamics of Public Opinions on Twitter

[EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). 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Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. doi:10.1016/j.jocs.2010.12.007Chen, W., Wang, C., & Wang, Y. (2010). Scalable influence maximization for prevalent viral marketing in large-scale social networks. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’10. doi:10.1145/1835804.1835934Ding, Z., Jia, Y., Zhou, B., Zhang, J., Han, Y., & Yu, C. (2013). An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs. Lecture Notes in Computer Science, 372-383. doi:10.1007/978-3-642-37401-2_38Ding, Z., Wang, H., Guo, L., Qiao, F., Cao, J., & Shen, D. (2015). Finding Influential Users and Popular Contents on Twitter. Web Information Systems Engineering – WISE 2015, 267-275. doi:10.1007/978-3-319-26187-4_23Feldman Barrett, L., & Russell, J. A. (1998). Independence and bipolarity in the structure of current affect. 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Melatonin enhances L-DOPA therapeutic effects, helps to reduce its dose, and protects dopaminergic neurons in 1-methyl-4- phenyl-1,2,3,6-tetrahydropyridine-induced parkinsonism in mice

L-3,4-dihydroxyphenylalanine (L-DOPA) reduces symptoms of Parkinson’s disease (PD), but suffers from serious side effects on long-term use. Melatonin (10–30 mg/kg, 6 doses at 10 hr intervals) was investigated to potentiate L-DOPA therapeutic effects in 1-methyl-4-phenyl-1,2,3,6- tetrahydropyridine (MPTP)-induced parkinsonism in mice. Striatal tyrosine hydroxylase (TH) immunoreactivity, TH, and phosphorylated ser 40 TH (p-TH) protein levels were assayed on 7th day. Nigral TH-positive neurons stereology was conducted on serial sections 2.8 mm from bregma rostrally to 3.74 mm caudally. MPTP caused 39% and 58% decrease, respectively, in striatal fibers and TH protein levels, but 2.5-fold increase in p-TH levels. About 35% TH neurons were lost between 360 and 600 lm from 940 lm of the entire nigra analyzed, but no neurons were lost between 250 lm rostrally and 220 lm caudally. When L-DOPA in small doses (5–8 mg/kg) failed to affect MPTPinduced akinesia or catalepsy, co-administration of melatonin with L-DOPA attenuated these behaviors. Melatonin administration significantly attenuated MPTP-induced loss in striatal TH fibers (82%), TH (62%) and p-TH protein (100%) levels, and nigral neurons (87–100%). Melatonin failed to attenuate MPTP-induced striatal dopamine depletion. L-DOPA administration (5 mg/kg, once 40 min prior to sacrifice, p.o.) in MPTP- and melatonin-treated mice caused significant increase in striatal dopamine (31%), as compared to L-DOPA and MPTP-treated mice. This was equivalent to 8 mg/kg L-DOPA administration in parkinsonian mouse. Therefore, prolonged, effective use of L-DOPA in PD with lesser side effects could be achieved by treating with 60% lower doses of L-DOPA along with melatonin

Predicting Emotion Dynamics Sequence on Twitter via Deep Learning Approach

[EN] Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. Despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users influenced by his/her friends. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose an Emotion-based User Sequential Influence Model (E-USIM) on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We then apply the developed model to predict users' future emotions by combing of personal and interpersonal influence.This work is partially supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Naskar, D.; Onaindia De La Rivaherrera, E.; Rebollo Pedruelo, M.; Singh, SR. (2020). Predicting Emotion Dynamics Sequence on Twitter via Deep Learning Approach. Association for Computing Machinery (ACM). 20-24. https://doi.org/10.1145/3428690.3429156S202