Learning and Forecasting Opinion Dynamics in Social Networks

Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast opinions from users? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives

Detecting indicators for startup business success: Sentiment analysis using text data mining

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues

Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms.Computer Science

Language change and evolution in Online Social Networks

Language is in constant flux, whether through the creation of new terms or the changing meanings of existing words. The process by which language change happens is through complex reinforcing interactions between individuals and the social structures in which they exist. There has been much research into language change and evolution, though this has involved manual processes that are both time consuming and costly. However, with the growth in popularity of osn, for the first time, researchers have access to fine-grained records of language and user interactions that not only contain data on the creation of these language innovations but also reveal the inter-user and inter-community dynamics that influence their adoptions and rejections. Having access to these osn datasets means that language change and evolution can now be assessed and modelled through the application of computational and machine-learning-based methods. Therefore, this thesis looks at how one can detect and predict language change in osn, as well as the factors that language change depends on. The answer to this over-arching question lies in three core components: first, detecting the innovations; second, modelling the individual user adoption process; and third, looking at the collective adoption across a network of individuals. In the first question, we operationalise traditional language acceptance heuristics (used to detect the emergence of new words) into three classes of computation time-series measures computing the variation in frequency, form and/or meaning. The grounded methods are applied to two osn, with results demonstrating the ability to detect language change across both networks. By additionally applying the methods to communities within each network, e.g. geographical regions, on Twitter and Subreddits in Reddit, the results indicate that language variation and change can be dependent on the community memberships. The second question in this thesis focuses on the process of users adopting language innovations in relation to other users with whom they are in contact. By modelling influence between users as a function of past innovation cascades, we compute a global activation threshold at which users adopt new terms dependent on exposure to them from their neighbours. Additionally, by testing the user interaction networks through random shuffles, we show that the time at which a user adopts a term is dependent on the local structure; however, a large part of the influence comes from sources external to the observed osn. The final question looks at how the speakers of a language are embedded in social networks, and how the networks' resulting structures and dynamics influence language usage and adoption patterns. We show that language innovations diffuse across a network in a predictable manner, which can be modelled using structural, grammatical and temporal measures, using a logistic regression model to predict the vitality of the diffusion. With regard to network structure, we show how innovations that manifest across structural holes and weak ties diffuse deeper across the given network. Beyond network influence, our results demonstrate that the grammatical context through which innovations emerge also play an essential role in diffusion dynamics - this indicates that the adoption of new words is enabled by a complex interplay of both network and linguistic factors. The three questions are used to answer the over-arching question, showing that one can, indeed, model language change and forecast user and community adoption of language innovations. Additionally, we also show the ability to apply grounded models and methods and apply them within a scalable computational framework. However, it is a challenging process that is heavily influenced by the underlying processes that are not recorded within the data from the osns

Network Geometry

Networks are finite metric spaces, with distances defined by the shortest paths between nodes. However, this is not the only form of network geometry: two others are the geometry of latent spaces underlying many networks and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale invariance, self-similarity and other forms of fundamental symmetries in networks. Network geometry is also of great use in a variety of practical applications, from understanding how the brain works to routing in the Internet. We review the most important theoretical and practical developments dealing with these approaches to network geometry and offer perspectives on future research directions and challenges in this frontier in the study of complexity

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio