Relevant bibliographies by topics / Social prediction

Academic literature on the topic 'Social prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Social prediction"

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Stoodley, Catherine J., and Peter T. Tsai. "Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.

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Social interactions involve processes ranging from face recognition to understanding others’ intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt them to changing situations, and cerebellar predictive modeling is thought to extend to nonmotor behaviors. Primary cerebellar dysfunction can produce social deficits, and atypical cerebellar structure and function are reported in autism, which is characterized by social communication challenges and atypical predictive processing. We examine the evidence that cerebellar-mediated predictions and adaptation play important roles in social processes and argue that disruptions in these processes contribute to autism.
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Wu, Jianjun, Yuxue Hu, Zhongqiang Huang, Junsong Li, Xiang Li, and Ying Sha. "Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks." Applied Sciences 13, no. 22 (November 17, 2023): 12437. http://dx.doi.org/10.3390/app132212437.

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Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.
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Hall, Andrew N., and Sandra C. Matz. "Targeting Item–level Nuances Leads to Small but Robust Improvements in Personality Prediction from Digital Footprints." European Journal of Personality 34, no. 5 (September 2020): 873–84. http://dx.doi.org/10.1002/per.2253.

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In the past decade, researchers have demonstrated that personality can be accurately predicted from digital footprint data, including Facebook likes, tweets, blog posts, pictures, and transaction records. Such computer–based predictions from digital footprints can complement—and in some circumstances even replace—traditional self–report measures, which suffer from well–known response biases and are difficult to scale. However, these previous studies have focused on the prediction of aggregate trait scores (i.e. a person's extroversion score), which may obscure prediction–relevant information at theoretical levels of the personality hierarchy beneath the Big 5 traits. Specifically, new research has demonstrated that personality may be better represented by so–called personality nuances—item–level representations of personality—and that utilizing these nuances can improve predictive performance. The present work examines the hypothesis that personality predictions from digital footprint data can be improved by first predicting personality nuances and subsequently aggregating to scores, rather than predicting trait scores outright. To examine this hypothesis, we employed least absolute shrinkage and selection operator regression and random forest models to predict both items and traits using out–of–sample cross–validation. In nine out of 10 cases across the two modelling approaches, nuance–based models improved the prediction of personality over the trait–based approaches to a small, but meaningful degree (4.25% or 1.69% on average, depending on method). Implications for personality prediction and personality nuances are discussed. © 2020 European Association of Personality Psychology
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Utter, Glenn H., and James Vanderleeuw. "Review Essay: Election Predictions: Theory and Social Science." American Review of Politics 12 (July 1, 1991): 114–29. http://dx.doi.org/10.15763/issn.2374-7781.1991.12.0.114-129.

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An important concern for political scientists is the extent to which the discipline has progressed as a science. Political science has based its claim to being a science on its ability to construct models that predict as well as explain political phenomena. We examine the role that philosophers of science have given to prediction in science generally, and then note examples from the history of science that demonstrate a varied role for prediction in differing sciences. A review of the literature on predicting congressional and presidential election outcomes indicates the impressive success of predictive models. Nonetheless, such models are often open to the criticism that they lack a firm theoretical foundation.
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Wu, Shuying. "Study on Generation and Development of Social Prediction System." Journal of Management and Strategy 12, no. 1 (March 5, 2021): 36. http://dx.doi.org/10.5430/jms.v12n1p36.

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Prediction means predicting future scientifically which has a great relationship with human beings. Based on the development history of prediction system, this paper discusses establishment of modern prediction system and its importance in leadership structure, especially when national prediction system has become a major factor of national security among modern prediction systems. It’s just because each country has built a gradually improved prediction system that international political relation could form and stay stabilized. Modern prediction system is now steering to the tendency of controlling the whole social system. In modern society, all the decisions made by institutions depend more and more on prediction and the reform of social system is the soul of predicting the new paradigm of institutional behaviors.
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Kaufman, Aaron Russell, Peter Kraft, and Maya Sen. "Improving Supreme Court Forecasting Using Boosted Decision Trees." Political Analysis 27, no. 3 (February 19, 2019): 381–87. http://dx.doi.org/10.1017/pan.2018.59.

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Though used frequently in machine learning, boosted decision trees are largely unused in political science, despite many useful properties. We explain how to use one variant of boosted decision trees, AdaBoosted decision trees (ADTs), for social science predictions. We illustrate their use by examining a well-known political prediction problem, predicting U.S. Supreme Court rulings. We find that our ADT approach outperforms existing predictive models. We also provide two additional examples of the approach, one predicting the onset of civil wars and the other predicting county-level vote shares in U.S. presidential elections.
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Tan, Leonard, Thuan Pham, Kei Ho Hang, and Seng Kok Tan. "Event Prediction in Online Social Networks." Journal of Data Intelligence 2, no. 1 (March 2021): 64–94. http://dx.doi.org/10.26421/jdi2.1-4.

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Event prediction is a very important task in numerous applications of interest like fintech, medical, security, etc. However, event prediction is a highly complex task because it is challenging to classify, contains temporally changing themes of discussion and heavy topic drifts. In this research, we present a novel approach which leverages on the RFT framework developed in \cite{tan2020discovering}. This study addresses the challenge of accurately representing relational features in observed complex social communication behavior for the event prediction task; which recent graph learning methodologies are struggling with. The concept here, is to firstly learn the turbulent patterns of relational state transitions between actors preceeding an event and then secondly, to evolve these profiles temporally, in the event prediction process. The event prediction model which leverages on the RFT framework discovers, identifies and adaptively ranks relational turbulence as likelihood predictions of event occurrences. Extensive experiments on large-scale social datasets across important indicator tests for validation, show that the RFT framework performs comparably better by more than 10\% to HPM \cite{amodeo2011hybrid} and other state-of-the-art baselines in event prediction.
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Manan koli, Abdul, Muqeem Ahmed, and . "Election Prediction Using Big Data Analytics-A Survey." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 366. http://dx.doi.org/10.14419/ijet.v7i4.5.20108.

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Social media has received much attention due to it's real-time and interactive nature for political discourse, especially around election times. Recently studies have explored the power of social media platforms such as Twitter or Facebook, on recording current social trends and predicting the voting outcomes of an area. These social media generate a large amount of raw data that can be used in decision making for election predictions. This tremendously generated data is referred to as “Big data”. After scrutinized a lot of research work related to election prediction, a survey paper is presented in which every work related to election prediction using social media is incorporated. This paper is an attempt to review various tools, models, and algorithms used for the observation of campaign, discussion, prediction, and analysis of the election, and also suggest further tools and techniques for improvement.
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Fauser, Daniel V., and Andreas Gruener. "Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach." Credit and Capital Markets – Kredit und Kapital: Volume 53, Issue 4 53, no. 4 (October 1, 2020): 513–54. http://dx.doi.org/10.3790/ccm.53.4.513.

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This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.
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Stenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (February 13, 2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.

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The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, “predictive fit,” based on the model’s ability to predict new data is advocated. The authors define two prediction tasks: “missing responses prediction”—where the goal is to predict an in-sample person’s response to an in-sample item—and “missing persons prediction”—where the goal is to predict an out-of-sample person’s string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a “model’s predictions on average?”). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike’s information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.
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Dissertations / Theses on the topic "Social prediction"

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Hayward, Peter C., and n/a. "From individual to social foresight." Swinburne University of Technology. Australian Graduate School of Entrepreneurship, 2005. http://adt.lib.swin.edu.au./public/adt-VSWT20061108.153623.

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To this point in time humanity has successfully responded to the challenges to its existence. A viewpoint becoming widespread is that humanity will have to respond to even greater challenges to its existence in the future. If adequate responses are not formulated to these emerging challenges then a dystopian future for humanity is a strong possibility. While experience can teach us how to act in the future it is the express intent of this research that we should not have to experience dystopia in order to learn how to prevent it. The innate human capacity for foresight has played a pivotal role in responding to past challenges, however, a more extensive form of foresight will need to be developed to respond to these future challenges. That form of foresight will need to be both individual and social in nature. Part I of this thesis generates an original theory of how foresight could develop in individuals beyond our innate capacities. The theory argues that foresight ca- pacities develop through the expansion of individual consciousness, particularly the individual's sense of `self'. The theory is synthesised from the work of a num- ber of psychological researchers including Jean Piaget, Jane Loevinger, Lawrence Kohlberg, Clare Graves, Susan Cook-Greuter and Ken Wilber. Part II is a two year study of students undertaking a postgraduate course in strategic foresight. The study is utilised to add preliminary empirical support to the theory proposed in Part I. Part III integrates the previous two parts to further elaborate the attributes and dynamics of individual foresight development before describing how social foresight capacity can emerge from individual development. Expanded individual and so- cial foresight capacities are achievable, but cannot be assumed. The contribution of this thesis is to give a theoretical base to such development and to outline fur- ther research. The development of individual foresight and the emergence of social expressions of foresight can offer preferable, and not dystopian, futures for both current and future generations.
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Hayward, Peter. "From individual to social foresight." Australasian Digital Thesis Program, 2005. http://adt.lib.swin.edu.au/public/adt-VSWT20061108.153623.

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Thesis (PhD) -- Swinburne University of Technology, Australian Graduate School of Entrepreneurship, 2005.
Submitted to the fulfillment of the requirements for the degree of Doctor of Philosophy - Australian Graduate School of Entrepreneurship, Faculty of Business and Enterprise, Swinburne University of Technology, 2005. Typescript. Includes bibliographical references (p. 294-308).
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Pesquita, Ana. "The social is predictive : human sensitivity to attention control in action prediction." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59076.

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Observing others is predicting others. Humans have a natural tendency to make predictions about other people’s future behavior. This predisposition sits at the basis of social cognition: others become accessible to us because we are able to simulate their internal states, and in this way make predictions about their future behavior (Blakemore & Decety, 2001). In this thesis, I examine prediction in the social realm through three main contributions. The first contribution is of a theoretical nature, the second is methodological, and the third contribution is empirical. On the theoretical plane, I present a new framework for cooperative social interactions – the predictive joint-action model, which extends previous models of social interaction (Wolpert, Doya, & Kawato, 2003) to include the higher level goals of joint action and planning (Vesper, Butterfill, Knoblich, & Sebanz, 2010). Action prediction is central to joint-action. A recent theory proposes that social awareness to someone else’s attentional states underlies our ability to predict their future actions (Graziano, 2013). In the methodological realm, I developed a procedure for investigating the role of sensitivity to other’s attention control states in action prediction. This method offers a way to test the hypothesis that humans are sensitive to whether someone’s spatial attention was endogenously controlled (as in the case of choosing to attend towards a particular event) or exogenously controlled (as in the case of attention being prompted by an external event), independent of their sensitivity to the spatial location of that person’s attentional focus. On the empirical front, I present new evidence supporting the hypothesis that social cognition involves the predictive modeling of other’s attentional states. In particular, a series of experiments showed that observers are sensitive to someone else’s attention control and that this sensitivity occurs through an implicit kinematic process linked to social aptitude. In conclusion, I bring these contributions together. I do this by offering an interpretation of the empirical findings through the lens of the theoretical framework, by discussing several limitations of the present work, and by pointing to several questions that emerge from the new findings, thereby outlining avenues for future research on social cognition.
Arts, Faculty of
Graduate
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Bahceci, Oktay, and Oscar Alsing. "Stock Market Prediction using Social Media Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166448.

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Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices. The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter. Implementing various machine learning and classification models such as the Artificial Neural Network we successfully implemented a company-specific model capable of predicting stock price movement with 80% accuracy.
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Theriault, Jordan Eugene. "Morality as a Scaffold for Social Prediction." Thesis, Boston College, 2017. http://hdl.handle.net/2345/bc-ir:107624.

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Thesis advisor: Liane L. Young
Thesis advisor: Elizabeth A. Kensinger
Theory of mind refers to the process of representing others’ mental states. This process consistently elicits activity in a network of brain regions: the theory of mind network (ToMN). Typically, theory of mind has been understood in terms of content, i.e. representing the semantic content of someone’s beliefs. However, recent work has proposed that ToMN activity could be better understood in the context of social prediction; or, more specifically, prediction error—the difference between observed and predicted information. Social predictions can be represented in multiple forms—e.g. dispositional predictions about who a person is, prescriptive norms about what people should do, and descriptive norms about what people frequently do. Part 1 examined the relationship between social prediction error and ToMN activity, finding that the activity in the ToMN was related to both dispositional, and prescriptive predictions. Part 2 examined the semantic content represented by moral claims. Prior work has suggested that morals are generally represented and understood as objective, i.e. akin to facts. Instead, we found that moral claims are represented as far more social than prior work had anticipated, eliciting a great deal of activity across the ToMN. Part 3 examined the relationship between ToMN activity and metaethical status, i.e. the extent that morals were perceived as objective or subjective. Objective moral claims elicited less ToMN activity, whereas subjective moral claimed elicited more. We argue that this relationship is best understood in the context of prediction, where objective moral claims represent strong social priors about what most people will believe. Finally, I expand on this finding and argue that a theoretical approach incorporating social prediction has serious implications for morality, or more specifically, for the motivations underlying normative compliance. People may be compelled to observe moral rules because doing so maintains a predictable social environment
Thesis (PhD) — Boston College, 2017
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Psychology
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Aloufi, Samah. "Trust-aware Link Prediction in Online Social Networks." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23303.

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As people go about their lives, they form a variety of social relationships, such as family, friends, colleagues, and acquaintances, and these relationships differ in their strength, indicating the level of trust among these people. The trend in these relationships is for people to trust those who they have met in real life more than unfamiliar people whom they have only met online. In online social network sites the objective is to make it possible for users to post information and share albums, diaries, videos, and experiences with a list of contacts who are real-world friends and/or like-minded online friends. However, with the growth of online social services, the need for identifying trustworthy people has become a primary focus in order to protect users’ vast amounts of information from being misused by unreliable users. In this thesis, we introduce the Capacity- first algorithm for identifying a local group of trusted people within a network. In order to achieve the outlined goals, the algorithm adapts the Advogato trust metric by incorporating weighted social relationships. The Capacity-first algorithm determines all possible reliable users within the network of a targeted user and prevents malicious users from accessing their personal network. In order to evaluate our algorithm, we conduct experiments to measure its performance against other well-known baseline algorithms. The experimental results show that our algorithm’s performance is better than existing alternatives in finding all possible trustworthy users and blocking unreliable ones from violating users’ privacy.
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Gao, Fei. "Structure based online social network link prediction study." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/structure-based-online-social-network-link-prediction-study(41697041-bfe4-4e64-a516-1a0703cfb4bb).html.

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This thesis shed light on the Internet-based social network link prediction problem. After reviewing recent research achievements in this area, two hypotheses are introduced: (i) The performance of topology-based network prediction methods and the characteristics of the networks are correlated. (ii) As networks are dynamic, the performance of prediction can be improved by providing different treatment to different nodes and links. To verify the Hypothesis (i), we conduct experiments with six selected online social networks. The correlation coefficients are calculated between six common network metrics and ten widely used topology-based network link prediction methods. The results show a strong correlation between Gini Coefficient and Preferential Attachment method. This study also reveals two types of networks: prediction-friendly network, for which most of the selected prediction methods perform well with an AUC result above 0.8, and prediction unfriendly network that on the contrary. For Hypothesis (ii), we proposed two network prediction models, the Hybrid Prediction Model and Community Bridge Boosting Prediction Model (CBBPM). The hybrid prediction model assumes network links are formed following different rules. The model linearly combines eight link prediction methods and the evolvement rules have been probed by finding the best weight for each of the methods by solving the linear optimization problem. This experiment result shows an improvement of prediction accuracy. This model takes link prediction as a time series problem. Different from Hybrid Prediction Model, CBBPM provides a different treatment on nodes. We define and classify network nodes as community bridge nodes in a novel approach based on their degree and links position in network communities. The similarity score that is calculated from the selected prediction methods is then boosted for predicting new links. The results from this model also show an enhancement of prediction accuracy. The two hypotheses are validated using the research experiments.
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Dimadi, Ioanna. "Social media sentiment analysis for firm's revenue prediction." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363117.

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The advent of the Internet and its social media platforms have affected people’s daily life. More and more people use it as a tool in order to communicate, exchange opin-ions and share information with others. However, those platforms have not only been used for socializing but also for expressing people’s product preferences. This wide spread of social networking sites has enabled companies to take advantage of them as an important way of approaching their target audience. This thesis focuses on study-ing the influence of social media platforms on the revenue of a single organization like Nike that uses them actively. Facebook and Twitter, two widely-used social me-dia platforms, were investigated with tweets and comments produced by consumer’s online discussions in brand’s hosted pages being gathered. This unstructured social media data were collected from 26 Nike official pages, 13 fan pages from each plat-form and their sentiment was analyzed. The classification of those comments had been done by using the Valence Aware Dictionary and Sentiment Reasoner (VADER), a lexicon-based approach that is implemented for social media analysis. After gathering the five-year Nike’s revenue, the degree to which these could be affected by the clas-sified data was examined by using multiple stepwise linear regression analysis. The findings showed that the fraction of positive/total for both Facebook and Twitter ex-plained 84.6% of the revenue’s variance. Fitting this data on the multiple regression model, Nike’s revenue could be forecast with a root mean square error around 287 billion.
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Mallek, Sabrine. "Social Network Analysis : Link prediction under the Belief Function Framework." Thesis, Artois, 2018. http://www.theses.fr/2018ARTO0204/document.

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Les réseaux sociaux sont de très grands systèmes permettant de représenter les interactions sociales entre les individus. L'analyse des réseaux sociaux est une collection de méthodes spécialement conçues pour examiner les aspects relationnels des structures sociales. L'un des défis les plus importants dans l'analyse de réseaux sociaux est le problème de prédiction de liens. La prédiction de liens étudie l'existence potentielle de nouvelles associations parmi des entités sociales non connectées. La plupart des approches de prédiction de liens se concentrent sur une seule source d'information, c'est-à-dire sur les aspects topologiques du réseau (par exemple le voisinage des nœuds) en supposant que les données sociales sont entièrement fiables. Pourtant, ces données sont généralement bruitées, manquantes et sujettes à des erreurs d'observation causant des distorsions et des résultats probablement erronés. Ainsi, cette thèse propose de gérer le problème de prédiction de liens sous incertitude. D'abord, deux nouveaux modèles de graphes de réseaux sociaux uniplexes et multiplexes sont introduits pour traiter l'incertitude dans les données sociales. L'incertitude traitée apparaît au niveau des liens et est représentée et gérée à travers le cadre de la théorie des fonctions de croyance. Ensuite, nous présentons huit méthodes de prédiction de liens utilisant les fonctions de croyance fondées sur différentes sources d'information dans les réseaux sociaux uniplexes et multiplexes. Nos contributions s'appuient sur les informations disponibles sur le réseau social. Nous combinons des informations structurelles aux informations des cercles sociaux et aux attributs des nœuds, ainsi que l'apprentissage supervisé pour prédire les nouveaux liens. Des tests sont effectués pour valider la faisabilité et l'intérêt de nos approches à celles de la littérature. Les résultats obtenus sur les données du monde réel démontrent que nos propositions sont pertinentes et valables dans le contexte de prédiction de liens
Social networks are large structures that depict social linkage between millions of actors. Social network analysis came out as a tool to study and monitor the patterning of such structures. One of the most important challenges in social network analysis is the link prediction problem. Link prediction investigates the potential existence of new associations among unlinked social entities. Most link prediction approaches focus on a single source of information, i.e. network topology (e.g. node neighborhood) assuming social data to be fully trustworthy. Yet, such data are usually noisy, missing and prone to observation errors causing distortions and likely inaccurate results. Thus, this thesis proposes to handle the link prediction problem under uncertainty. First, two new graph-based models for uniplex and multiplex social networks are introduced to address uncertainty in social data. The handled uncertainty appears at the links level and is represented and managed through the belief function theory framework. Next, we present eight link prediction methods using belief functions based on different sources of information in uniplex and multiplex social networks. Our proposals build upon the available information in data about the social network. We combine structural information to social circles information and node attributes along with supervised learning to predict new links. Tests are performed to validate the feasibility and the interest of our link prediction approaches compared to the ones from literature. Obtained results on social data from real-world demonstrate that our proposals are relevant and valid in the link prediction context
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Suaysom, Natchanon. "Iterative Matrix Factorization Method for Social Media Data Location Prediction." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/hmc_theses/96.

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Since some of the location of where the users posted their tweets collected by social media company have varied accuracy, and some are missing. We want to use those tweets with highest accuracy to help fill in the data of those tweets with incomplete information. To test our algorithm, we used the sets of social media data from a city, we separated them into training sets, where we know all the information, and the testing sets, where we intentionally pretend to not know the location. One prediction method that was used in (Dukler, Han and Wang, 2016) requires appending one-hot encoding of the location to the bag of words matrix to do Location Oriented Nonnegative Matrix Factorization (LONMF). We improve further on this algorithm by introducing iterative LONMF. We found that when the threshold and number of iterations are chosen correctly, we can predict tweets location with higher accuracy than using LONMF.
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Books on the topic "Social prediction"

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Srinivas, Virinchi, and Pabitra Mitra. Link Prediction in Social Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28922-9.

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Zhang, Xiaomei, and Guohong Cao. Event Attendance Prediction in Social Networks. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89262-3.

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Liu, Huan, John J. Salerno, and Michael J. Young, eds. Social Computing, Behavioral Modeling, and Prediction. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-77672-9.

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Kawash, Jalal, Nitin Agarwal, and Tansel Özyer, eds. Prediction and Inference from Social Networks and Social Media. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51049-1.

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Kennedy, William G., Nitin Agarwal, and Shanchieh Jay Yang, eds. Social Computing, Behavioral-Cultural Modeling and Prediction. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05579-4.

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Yang, Shanchieh Jay, Ariel M. Greenberg, and Mica Endsley, eds. Social Computing, Behavioral - Cultural Modeling and Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3.

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Salerno, John, Shanchieh Jay Yang, Dana Nau, and Sun-Ki Chai, eds. Social Computing, Behavioral-Cultural Modeling and Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19656-0.

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Agarwal, Nitin, Kevin Xu, and Nathaniel Osgood, eds. Social Computing, Behavioral-Cultural Modeling, and Prediction. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3.

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Greenberg, Ariel M., William G. Kennedy, and Nathan D. Bos, eds. Social Computing, Behavioral-Cultural Modeling and Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37210-0.

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C, Land Kenneth, Schneider Stephen Henry, and Social Science Research Council (U.S.), eds. Forecasting in the social and natural sciences. Dordrecht: D. Reidel Pub. Co., 1987.

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Book chapters on the topic "Social prediction"

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Aggrawal, Niyati, and Adarsh Anand. "Link Prediction." In Social Networks, 83–96. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-6.

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Rezvanian, Alireza, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, and Mohammad Reza Meybodi. "Social Link Prediction." In Studies in Computational Intelligence, 169–239. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10767-3_6.

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Solinger, Carrie, Leanne Hirshfield, Stuart Hirshfield, Rachel Friendman, and Christopher Leper. "Beyond Facebook Personality Prediction:." In Social Computing and Social Media, 486–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07632-4_46.

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Wootton, Barbara. "Studies in Criminological Prediction." In Social Science and Social Pathology, 173–200. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003503156-7.

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Tuomela, Raimo. "Holistic Social Causation and Explanation." In Explanation, Prediction, and Confirmation, 305–18. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_21.

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Ishii, Mamoru. "Social Impacts of Space Weather." In Solar-Terrestrial Environmental Prediction, 3–7. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7765-7_1.

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Srinivas, Virinchi, and Pabitra Mitra. "Locally Adaptive Link Prediction." In Link Prediction in Social Networks, 27–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28922-9_3.

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Srinivas, Virinchi, and Pabitra Mitra. "Applications of Link Prediction." In Link Prediction in Social Networks, 57–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28922-9_5.

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Tayebi, Mohammad A., and Uwe Glässer. "Co-offence Prediction." In Social Network Analysis in Predictive Policing, 77–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41492-8_6.

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Tiwari, Rajesh Ranjan. "Explanation and Prediction." In The Nature of Explanation in Social Sciences, 84–93. London: Routledge India, 2023. http://dx.doi.org/10.4324/9781003405719-6.

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Conference papers on the topic "Social prediction"

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Heymann, Paul, Daniel Ramage, and Hector Garcia-Molina. "Social tag prediction." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390425.

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Park, Hyun Soo, and Jianbo Shi. "Social saliency prediction." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7299110.

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Ding, Keyan, Ronggang Wang, and Shiqi Wang. "Social Media Popularity Prediction." In MM '19: The 27th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3343031.3356062.

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Maruyama, William Takahiro, and Luciano Antonio Digiampietri. "Co-authorship prediction in academic social network." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/brasnam.2016.6445.

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The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.
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Jin, Shengmin, and Reza Zafarani. "Sentiment Prediction in Social Networks." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00190.

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Fei, Hongliang, Ruoyi Jiang, Yuhao Yang, Bo Luo, and Jun Huan. "Content based social behavior prediction." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063719.

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de Moraes, Camila Mesquita, Eduardo Bezerra, and Ronaldo Goldschmidt. "Link prediction in social networks." In WebMedia '19: Brazilian Symposium on Multimedia and the Web. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3323503.3349556.

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Oliva, Carmine, and Hannes Högni Vilhjálmsson. "Prediction in social path following." In MIG '14: Motion in Games. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2668064.2668103.

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Sharma, Upasana, and Bhawna Minocha. "Link Prediction in Social Networks." In the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905149.

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Dong, Yinghong, Hao Chen, Xijin Tang, Weining Qian, and Aoying Zhou. "Prediction of social mood on Chinese societal risk perception." In 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC). IEEE, 2015. http://dx.doi.org/10.1109/besc.2015.7365966.

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Reports on the topic "Social prediction"

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Du Bois, Barbara, and Jerry D. Goodman. Social Ecological Prediction of Obesity in U.S. Naval Personnel. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada378984.

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Bates Mike, Paula. Evaluation of the Admissions Process at Portland State University School of Social Work : Prediction and Performance. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1808.

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Perdigão, Rui A. P. Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211028.

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We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.
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Golbeck, Jennifer. Generating Predictive Movie Recommendations from Trust in Social Networks. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada447900.

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Andreoni, James, Nikos Nikiforakis, and Simon Siegenthaler. Predicting Social Tipping and Norm Change in Controlled Experiments. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27310.

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Grimm, Kevin. Machine Learning for Social and Health Sciences in R. Instats Inc., 2023. http://dx.doi.org/10.61700/61p1kmxy6183q469.

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This workshop, 'Introduction to Machine Learning with R', led by Kevin Grimm from Arizona State University, is designed to equip PhD students, professors, and professional researchers with the skills to apply machine learning techniques in their respective fields. Participants will gain a comprehensive understanding of machine learning concepts, techniques, and their application using R, enhancing their ability to analyze complex data, make accurate predictions, and connect with other professionals in their field.
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Соловйов, В. М., and В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Song, So Young, Erin Cho, Youn-Kyung Kim, and Theresa Hyunjin Kwon. Clothing Communication via Social Media: A Decision Tree Predictive Model. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-102.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Nau, Dana, and V. S. Subrahmanian. 2011 International Conference on Socio-Cultural Behavior and Prediction. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada567100.

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