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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Deng, Shangkun, Takashi Mitsubuchi, and Akito Sakurai. "Stock Price Change Rate Prediction by Utilizing Social Network Activities." Scientific World Journal 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/861641.
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Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.
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Bart, Evgeniy, Rui Zhang, and Muzammil Hussain. "Where Would You Go this Weekend? Time-Dependent Prediction of User Activity Using Social Network Data." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 669–72. http://dx.doi.org/10.1609/icwsm.v7i1.14453.
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Predicting user activities and interests has many applications, for example, in contextual recommendations. Although the problem of predicting interests in general has been studied extensively, the problem of predicting when the users are likely to act on those interests has received considerably less attention. Such predictions of timing are extremely important when the application itself is time-sensitive (e.g., travel recommendations are irrelevant too far in advance and after reservations have already been made). Particularly important is the ability to predict likely future activities long in advance (as opposed to short-term prediction of imminent activities). In this paper we describe a comprehensive study that addresses this problem of making long-term time-dependent predictions of user interest. We have conducted this study on a large collection of visits to various venues of interest performed by users of Foursquare. We have built models that, given a user's history, can predict whether or not the user will visit a venue of a particular type on a given day. These models provide useful prediction accuracy of up to 75% for up to several weeks into the future. Our study explores and compares various feature sets and prediction methods. Of particular interest is the fact that venues interact with each other: to predict visits to one type of venue, it helps to use the history of visits to all venue types.
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Mukesh M. Raghuwanshi, Pradnya S. Borkar, Sachin U. Balvir,. "Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network." Journal of Electrical Systems 20, no. 2s (April 4, 2024): 639–49. http://dx.doi.org/10.52783/jes.1530.
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Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.
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Haimovich, Daniel, Dima Karamshuk, Thomas J. Leeper, Evgeniy Riabenko, and Milan Vojnovic. "Popularity prediction for social media over arbitrary time horizons." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 841–49. http://dx.doi.org/10.14778/3503585.3503593.
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Predicting the popularity of social media content in real time requires approaches that efficiently operate at global scale. Popularity prediction is important for many applications, including detection of harmful viral content to enable timely content moderation. The prediction task is difficult because views result from interactions between user interests, content features, resharing, feed ranking, and network structure. We consider the problem of accurately predicting popularity both at any given prediction time since a content item's creation and for arbitrary time horizons into the future. In order to achieve high accuracy for different prediction time horizons, it is essential for models to use static features (of content and user) as well as observed popularity growth up to prediction time. We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content's popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth. This results in a highly scalable method for popularity prediction over arbitrary prediction time horizons that also achieves a high degree of accuracy, compared to several leading baselines, on a dataset of public page content on Facebook over a two-month period, covering billions of content views and hundreds of thousands of distinct content items. The model has shown competitive prediction accuracy against a strong baseline that consists of separately trained models for specific prediction time horizons.
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Ooka, Tadao, Hisashi Johno, Kazunori Nakamoto, Yoshioki Yoda, Hiroshi Yokomichi, and Zentaro Yamagata. "Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan." BMJ Nutrition, Prevention & Health 4, no. 1 (March 11, 2021): 140–48. http://dx.doi.org/10.1136/bmjnph-2020-000200.
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IntroductionEarly intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methodsThis study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors.ResultsThe RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power.ConclusionsCorrect use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.
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Kukharuk, Olha. "Theory of social identity and prediction of social behavior: Basic approaches." SCIENTIFIC STUDIOS ON SOCIAL AND POLITICAL PSYCHOLOGY 51, no. 48 (January 10, 2022): 16–23. http://dx.doi.org/10.61727/sssppj/2.2021.16.
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The article’s relevance is due to the importance of problems of social forecasting, in particular the prediction of social behavior. Classical and modern studies of social identity are analyzed. In the world, this theory is one of the basic for the analysis and prediction of group behavior; in Ukrainian psychological researches, the realization of social identity’s behavioral aspect is almost not represented. The purpose of the study is the analysis of theoretical and methodological approaches to the study of behavior through the prism of the theory of social identity in various fields of sociopsychological knowledge. The research methodology – analysis of articles and research papers carried out within the social identity theory to study the behavioral component of social identity. The analysis results showed that predicting social behavior is one of the critical areas of study within the social identity theory. Social identity theory’s heart of predicting social behavior can be divided into three essential components. Appeal to the individual as a carrier of identity and influence on the behavior of group members through such a carrier. Request to the crucial components of identity – the impact on behavior is possible both through the appeal to individual components of identity and social identity as a whole, as a unique combination of types of identity. Strengthening the value component and striving for positive identification is the driving force for maintaining and preserving the group’s identity. All these postulates have been confirmed by numerous applied studies and make it possible to consider them as a theoretical basis for developing methodological foundations for predicting social behavior. Prospects for further research are seen in a deeper analysis of the research results conducted within the theory of social identity, highlighting the basic patterns common to these studies and the integration of knowledge into Ukrainian socio-psychological scientific thought to develop methodological approaches to predicting social behavior
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Fleetwood, Steve, and Anthony Hesketh. "Prediction in Social Science." Journal of Critical Realism 5, no. 2 (August 2006): 228–50. http://dx.doi.org/10.1558/jocr.v5i2.228.
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Radović-Marković, Mirjana, and Slađana Vujičić. "Prediction in social sciences." International Review, no. 1-2 (2021): 18–24. http://dx.doi.org/10.5937/intrev2102018r.
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Considerable interest has been shown over recent decades in the application of quantitative methods in social sciences. The purpose of this paper is to discuss the ability to make predictions in social sciences with a focus on economics. Quantification of social and economic phenomena from the start of application had a lot of supporters but even more opponents, mathematics and methodological knowledge have passed the test of time and have lost none of their importance to the present day. The paper concludes that, forecasts may more desirable for many reasons. Namely, a better and more complete understanding of future trends and their effects will improve theories and models in economics and other social sciences. These improvements will greatly benefit those who explicitly seek to create a "ready society."
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Gelfand, Michele J., David Spurlock, Janet A. Sniezek, and Liang Shao. "Culture and Social Prediction." Journal of Cross-Cultural Psychology 31, no. 4 (July 2000): 498–516. http://dx.doi.org/10.1177/0022022100031004004.
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Chernozub, O. L. "Do indirect measures of attitudes improve our predictions of behavior? Evaluating and explaining the predictive validity of GATA." RUDN Journal of Sociology 24, no. 1 (March 15, 2024): 241–58. http://dx.doi.org/10.22363/2313-2272-2024-24-1-241-258.
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The generalization of the results accumulated to date has shown that the implicit measures of attitudes (some even suggest defining them with a less pretentious term “indirect”) show a disappointingly weak predictive potential in relation to real behavior. Thus, the predictive validity of the Graphical Association Test of Attitude (GATA), which also claims to be an indirect method, has been questioned. To check this assumption, we analyzed the results obtained with GATA in 64 predictions provided that the predicted outcome could be verified by real action. Such forecasts cover the domains of electoral, consumer and communicative behavior. In some cases, the prediction based on the data from a representative sample was checked referring to the actual behavior of the group represented by the sample, e.g., the electorate, or the consumers of a certain category of goods, etc. In other cases, the accuracy of the forecast was checked for each respondent. This allows to avoid the effect of “mutual compensation” of erroneous forecasts with opposite valence. The test method consisted of a comparison of the prediction accuracy of pairs of “control” and “experimental” prediction models: the only difference identified was that the latter used the data from indirect measurements of GATA as an additional factor of action. In the article, all models are presented in their simplest and most transparent versions. The results of the conducted meta-analysis do not fully correspond to the general trend: the use of the GATA data significantly and continuously improves the accuracy of predicting behavior. In addition, the incremental effect on the accuracy of individual forecasts (for each respondent) turned out to be higher than that of the sample-based group forecasts.
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Jain, Puneet, Justin Manweiler, Arup Acharya, and Romit Roy Choudhury. "Scalable Social Analytics for Live Viral Event Prediction." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 226–35. http://dx.doi.org/10.1609/icwsm.v8i1.14504.
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Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception. This paper proposes CrowdCast, a cloud-based framework to enable real-time analysis and prediction from streaming social data. As an instantiation of this framework, we tune CrowdCast to observe Twitter tweets, and predict which YouTube videos are most likely to “go viral” in the near future. To this end, CrowdCast first applies online machine learning to map natural language tweets to a specific YouTube video. Then, tweets that indeed refer to videos are weighted by the perceived “influence” of the sender. Finally, the video’s spread is predicted through a sociological model, derived from the emerging structure of the graph over which the video-related tweets are (still) spreading. Combining metrics of influence and live structure, CrowdCast outputs sets of candidate videos, identified as likely to become viral in the next few hours. We monitor Twitter for more than 30 days, and find that CrowdCast’s real-time predictions demonstrate encouraging correlation with actual YouTube viewership in the near future.
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Latif, Atefeh, Alireza Hedayati, and Vahe Aghazarian. "Improving Link Prediction in Dynamic Co-authorship Social Networks." International Academic Journal of Science and Engineering 05, no. 01 (June 1, 2018): 222–40. http://dx.doi.org/10.9756/iajse/v5i1/1810020.
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Lyu, Xiaozhong, Cuiqing Jiang, Yong Ding, Zhao Wang, and Yao Liu. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions." Sustainability 11, no. 3 (February 11, 2019): 913. http://dx.doi.org/10.3390/su11030913.
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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.
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Baydar, Mucahit, and Songul Albayrak. "Location prediction in location-based social networks." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (December 24, 2017): 149–56. http://dx.doi.org/10.18844/gjit.v7i3.2835.
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AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.
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Fattah, Mohammed, and Mohd Anul Haq. "Tweet Prediction for Social Media using Machine Learning." Engineering, Technology & Applied Science Research 14, no. 3 (June 1, 2024): 14698–703. http://dx.doi.org/10.48084/etasr.7524.
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Tweet prediction plays a crucial role in sentiment analysis, trend forecasting, and user behavior analysis on social media platforms such as X (Twitter). This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances within tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-related tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust predictive outcomes. The SVM model showed significantly higher performance with 82% accuracy and an F1 score of 81%, whereas LR, XGBoost, and RF achieved 79% accuracy with average F1-scores of 78%.
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Andrade, João, Andreia Duarte, and Artur Arsénio. "Social Web for Large-Scale Biosensors." International Journal of Web Portals 4, no. 3 (July 2012): 1–19. http://dx.doi.org/10.4018/jwp.2012070101.
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Recent technological developments on mobile technologies associated with the growing computational capabilities of sensing enabled devices have given rise to mobile sensing systems that can target community level problems. These systems are capable of inferring intelligence from acquired raw sensed data, through the use of data mining and machine learning techniques. However, due to their recent advent, associated issues remain to be solved in a systematized way. Various areas can benefit from these initiatives, with public health systems having a major application gain. There has been interest in the use of social networks as a mean of epidemic prediction. Still, the integration between large-scale sensor networks and these initiatives, required to achieve seamless epidemic detection and prediction, is yet to be achieved. In this context, it is essential to review systems applied to epidemic prediction. This paper presents an application scenario for such predictions, namely fetus health monitoring in pregnant woman, presenting a new non-invasive portable alternative system that allows long-term pregnancy surveillance.
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Saucier, Gerard, Kathryn Iurino, and Amber Gayle Thalmayer. "Comparing predictive validity in a community sample: High–dimensionality and traditional domain–and–facet structures of personality variation." European Journal of Personality 34, no. 6 (December 2020): 1120–37. http://dx.doi.org/10.1002/per.2235.
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Prediction of outcomes is an important way of distinguishing, among personality models, the best from the rest. Prominent previous models have tended to emphasize multiple internally consistent “facet” scales subordinate to a few broad domains. But such an organization of measurement may not be optimal for prediction. Here, we compare the predictive capacity and efficiency of assessments across two types of personality–structure model: conventional structures of facets as found in multiple platforms, and new high–dimensionality structures emphasizing those based on natural–language adjectives, in particular lexicon–based structures of 20, 23, and 28 dimensions. Predictions targeted 12 criterion variables related to health and psychopathology, in a sizeable American community sample. Results tended to favor personality–assessment platforms with (at least) a dozen or two well–selected variables having minimal intercorrelations, without sculpting of these to make them function as indicators of a few broad domains. Unsurprisingly, shorter scales, especially when derived from factor analyses of the personality lexicon, were shown to take a more efficient route to given levels of predictive capacity. Popular 20th–century personality–assessment models set out influential but suboptimal templates, including one that first identifies domains and then facets, which compromise the efficiency of measurement models, at least from a comparative–prediction standpoint. © 2020 European Association of Personality Psychology
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Verhagen, Mark D. "A Pragmatist’s Guide to Using Prediction in the Social Sciences." Socius: Sociological Research for a Dynamic World 8 (January 2022): 237802312210817. http://dx.doi.org/10.1177/23780231221081702.
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Prediction is an underused tool in the social sciences, often for the wrong reasons. Many social scientists confuse prediction with unnecessarily complicated methods or with narrowly predicting the future. This is unfortunate. When we view prediction as the simple process of evaluating a model’s ability to approximate an outcome of interest, it becomes a more generally applicable and disarmingly simple technique. For all its simplicity, the value of prediction should not be underestimated. Prediction can address enduring sources of criticism plaguing the social sciences, like a lack of assessing a model’s ability to reflect the real world, or the use of overly simplistic models to capture social life. The author illustrates these benefits with empirical examples that merely skim the surface of the many and varied ways in which prediction can be applied, staking the claim that prediction is a truly illustrious “free lunch” that can greatly benefit social scientists in their empirical work.
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Li, Yanying, Xiuling Wang, Yue Ning, and Hui Wang. "FairLP: Towards Fair Link Prediction on Social Network Graphs." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 628–39. http://dx.doi.org/10.1609/icwsm.v16i1.19321.
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Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil the existence of bias in six existing state-of-the-art link prediction algorithms through extensive empirical studies over real world datasets. Third, we identify the imbalanced density across intra-group and inter-group links in training graphs as one of the underlying causes of bias in link prediction. Based on the identified cause, fourth, we design a pre-processing bias mitigation method named FairLP to modify the training graph, aiming to balance the distribution of intra-group and inter-group links while preserving the network characteristics of the graph. FairLP is model-agnostic and thus is compatible with any existing link prediction algorithm. Our experimental results on real-world social network graphs demonstrate that FairLP achieves better trade-off between fairness and prediction accuracy than the existing fairness-enhancing link prediction methods.
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Wang, Jingjing, Wenjun Jiang, Kenli Li, Guojun Wang, and Keqin Li. "Incremental Group-Level Popularity Prediction in Online Social Networks." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–26. http://dx.doi.org/10.1145/3461839.
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Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.
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Jalili, Mahdi, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, and Matjaž Perc. "Link prediction in multiplex online social networks." Royal Society Open Science 4, no. 2 (February 2017): 160863. http://dx.doi.org/10.1098/rsos.160863.
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Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
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Yan, Jin, Songhui Hou, and Alexander Unger. "High Construal Level Reduces Overoptimistic Performance Prediction." Social Behavior and Personality: an international journal 42, no. 8 (September 24, 2014): 1303–13. http://dx.doi.org/10.2224/sbp.2014.42.8.1303.
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Overoptimistic performance prediction is a very common feature of people's goal-directed behavior. In this study we examined overoptimistic prediction as a function of construal level. In construal level theory an explanation is set out with regard to how people make predictions through the abstract connections between past and future events, with high-level construal bridging near and distant events. We conducted 2 experiments to confirm our hypothesis that, compared with people with local, concrete construals, people with global, abstract construals would make predictions that were less overoptimistic. In Study 1 we manipulated construal level by priming mindset, and participants (n = 81) predicted the level of their productivity in an anagram task. The results supported our hypothesis. In Study 2, in order to improve the generalizability of the conclusion, we varied the manipulation of the construal level by priming a scenario, and measured performance prediction by having the participants (n = 119) estimate task duration. The results showed that high-level construal consistently decreased overoptimistic prediction, supporting our hypothesis. The theoretical implications of our findings are discussed.
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Shafiekhani, Sajad, Touraj Harati Khalilabad, Sima Rafiei, Vahid Sadeghi, Amir Homayoun Jafari, and Nematollah Gheibi. "Trend and prediction of COVID-19 outbreak in Iran: SEIR and ANFIS model." Polish Journal of Medical Physics and Engineering 27, no. 3 (September 1, 2021): 241–49. http://dx.doi.org/10.2478/pjmpe-2021-0029.
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Abstract Background: Mathematical and predictive modeling approaches can be used in COVID-19 crisis to forecast the trend of new cases for healthcare management purposes. Given the COVID-19 disease pandemic, the prediction of the epidemic trend of this disease is so important. Methods: We constructed an SEIR (Susceptible-Exposed-Infected-Recovered) model on the COVID-19 outbreak in Iran. We estimated model parameters by the data on notified cases in Iran in the time window 1/22/2020 – 20/7/2021. Global sensitivity analysis is performed to determine the correlation between epidemiological variables and SEIR model parameters and to assess SEIR model robustness against perturbation to parameters. We Combined Adaptive Neuro-Fuzzy Inference System (ANFIS) as a rigorous time series prediction approach with the SEIR model to predict the trend of COVID-19 new cases under two different scenarios including social distance and non-social distance. Results: The SEIR and ANFIS model predicted new cases of COVID-19 for the period February 7, 2021, till August 7, 2021. Model predictions in the non-social distancing scenario indicate that the corona epidemic in Iran may recur as an immortal oscillation and Iran may undergo a recurrence of the third peak. Conclusion: Combining parametrized SEIR model and ANFIS is effective in predicting the trend of COVID-19 new cases in Iran.
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Lo, Adeline, Herman Chernoff, Tian Zheng, and Shaw-Hwa Lo. "Why significant variables aren’t automatically good predictors." Proceedings of the National Academy of Sciences 112, no. 45 (October 26, 2015): 13892–97. http://dx.doi.org/10.1073/pnas.1518285112.
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Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.
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Chelmis, Charalampos, and Viktor K. Prasanna. "Social Link Prediction in Online Social Tagging Systems." ACM Transactions on Information Systems 31, no. 4 (November 2013): 1–27. http://dx.doi.org/10.1145/2516891.
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Mishra, Aamlan Saswat. "Social Acceptance Prediction Model for Generative Architectural Spaces in India." Journal of Advanced Research in Construction and Urban Architecture 6, no. 3 (July 23, 2021): 50–57. http://dx.doi.org/10.24321/2456.9925.202109.
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Generative Architectural design is an emerging design process that is evolving due to evolution of computational power of computers and its ability to provide multiple choices of design solutions in architecture. This process, however, has a few drawbacks, some of which are, a high number of solutions which take less time for computers to produce than for their human counterpart to interpret and choose from and the less social acceptance of generative architectural design solutions. Due to the algorithms being unaware of what humans deem as acceptable solutions, these problems persist. A way to bridge such gap is through a survey simulation model, which the computer can apply to simulate acceptance of the created solution if it were put through a survey. A mathematical model has been developed though analysis of a survey such that a computer can predict how acceptable a particular iteration of a Generative Architectural design process is if it were put through a similar survey. Scores obtained in the survey simulation can be used to predict how acceptable a particular design iteration is there by culling less acceptable solutions and reducing the number of iterations provided to humans for review after running Generative Architectural algorithms.
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Salmon, Merrilee H. "Prediction in the social sciences." Enrahonar. Quaderns de filosofia 37 (July 7, 2005): 169. http://dx.doi.org/10.5565/rev/enrahonar.359.
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Ji, KeKe, ZhengZhong Li, Jian Chen, GuanYan Wang, KeLiang Liu, and Yi Luo. "Freeway accident duration prediction based on social network information." Neural Network World 32, no. 2 (2022): 93–112. http://dx.doi.org/10.14311/nnw.2022.32.006.
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Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.
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Wen, Yean-Fu, Ko-Yu Hung, Yi-Ting Hwang, and Yeong-Sung Frank Lin. "Sports lottery game prediction system development and evaluation on social networks." Internet Research 26, no. 3 (June 6, 2016): 758–88. http://dx.doi.org/10.1108/intr-05-2014-0139.
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Purpose – The purpose of this paper is to establish a social-network sp.orts lottery system to support users in predicting and simulating sports lottery betting. The community data were generated to support user decision and featured instant game records and odds data synchronisation. Furthermore, the next development cycle were evaluated through a questionnaire. Design/methodology/approach – An extended prototype website development methodology was applied to develop the system. An online sample was collected to evaluate the function, interface, operation, and prediction designs. The χ2 test and variance analysis were used to determine the association between facets and basic demographics. Finally, the regression model was used to identify the potentially essential predictors that influence the measurement facets. Findings – The high frequency of Facebook users, sports lottery purchases, and sports game viewers prefer the ability to predict the results of future sports games as advanced decision-making functions. However, the agent-based virtual gift presentation function was the least preferred function. Research limitations/implications – The study sample was limited only to users: who used PTT and Facebook; were of uneven age, education, and gender; and none segment groups. The study sample primarily comprised Taiwanese respondents. These differences might influence the practicality and prediction bias of the designed website and related models. Practical implications – The proposed method integrates social-network messages with real-time data access by using APIs, crawler schemes, and prediction mechanisms that enable developers to devise strategies for obtaining high system satisfaction. The system can be improved by adding the results of future sports games and excluding authorised Facebook message posts. Originality/value – A social-network-based sports lottery and prediction prototyping website was evaluated through a user-preference survey regarding design functions. The measurement results indicated that users share their opinions, predictions, and personal betting results and interact with their friends.
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Rona-Tas, Akos. "Predicting the Future: Art and Algorithms." Socio-Economic Review 18, no. 3 (July 2020): 893–911. http://dx.doi.org/10.1093/ser/mwaa040.
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Abstract Predictive algorithms are replacing the art of human judgement in rapidly growing areas of social life. By offering pattern recognition as forecast, predictive algorithms mechanically project the past onto the future, embracing a peculiar notion of time where the future is different in no radical way from the past and present, and a peculiar world where human agency is absent. Yet, prediction is about agency, we predict the future to change it. At the individual level, the psychological literature has concluded that in the realm of predictions, human judgement is inferior to algorithmic methods. At the sociological level, however, human judgement is often preferred over algorthms. We show how human and algorithmic predictions work in three social contexts—consumer credit, college admissions and criminal justice—and why people have good reasons to rely on human judgement. We argue that mechanical and overly successful local predictions can result in self-fulfilling prophecies and, eventually, global polarization and chaos. Finally, we look at algorithmic prediction as a form of societal and political governance and discuss how it is currently being constructed as a wide net of control by market processes in the USA and by government fiat in China.
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Sakal, Collin, Tingyou Li, Juan Li, and Xinyue Li. "Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study." JMIR Aging 7 (March 22, 2024): e53240-e53240. http://dx.doi.org/10.2196/53240.
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Abstract Background The societal burden of cognitive impairment in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear what types of risk factors best predict future cognitive impairment, if known risk factors make equally accurate predictions across different socioeconomic groups, and if existing prediction models are equally accurate across different subpopulations. Objective This paper aimed to identify which domain of health information best predicts future cognitive impairment among Chinese older adults and to examine if discrepancies exist in predictive ability across different population subsets. Methods Using data from the Chinese Longitudinal Healthy Longevity Survey, we quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors and hobbies, psychological factors, diet, exercise and sleep, chronic diseases, and 3 recently published logistic regression–based prediction models to predict 3-year risk of cognitive impairment in the general Chinese population and among male, female, rural-dwelling, urban-dwelling, educated, and not formally educated older adults. Predictive ability was quantified using the area under the receiver operating characteristic curve (AUC) and sensitivity-specificity curves through 20 repeats of 10-fold cross-validation. Results A total of 4047 participants were included in the study, of which 337 (8.3%) developed cognitive impairment 3 years after baseline data collection. The risk factor groups with the best predictive ability in the general population were demographics (AUC 0.78, 95% CI 0.77-0.78), cognitive tests (AUC 0.72, 95% CI 0.72-0.73), and instrumental activities of daily living (AUC 0.71, 95% CI 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all 3 recreated prediction models had significantly higher AUCs when making predictions among female older adults compared to male older adults and among older adults with no formal education compared to those with some education. Conclusions This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among Chinese older adults. However, the most predictive risk factors and existing models have lower predictive power among male, urban-dwelling, and educated older adults. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.
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Prédhumeau, Manon, Lyuba Mancheva, Julie Dugdale, and Anne Spalanzani. "Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle." Journal of Artificial Intelligence Research 73 (April 19, 2022): 1385–433. http://dx.doi.org/10.1613/jair.1.13425.
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This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.
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Janson, Charles H. "Intra-Specific Food Competition and Primate Social Structure: a Synthesis." Behaviour 105, no. 1-2 (1988): 1–17. http://dx.doi.org/10.1163/156853988x00412.
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AbstractThe results of the various studies in this volume lead to a series of predictions about the relationships of group size to various components of food intake. Individuals in larger groups should generally encounter fewer new food sources per unit foraging effort than they would alone (prediction 1); an exception may occur when large groups defend areas of high food density against small groups. In addition, individuals in larger groups generally will suffer reduced intake per food source encountered because of increased sharing with other group members, at least for food sources that supply little total nutrient relative to an individual's satiation level for the nutrient (prediction 3) or are scarce relative to the spacing between individuals in the group (prediction 5). Individuals in larger groups may compensate for such reductions in foraging efficiency by increasing rates of food encounter (prediction 2), using food sources with greater amounts of nutrient (prediction 4), or increasing total foraging effort per day (prediction 6). Reduced foraging efficiency for a particular nutrient may not affect total intake of that nutrient if other nutrients require greater daily foraging effort (prediction 7). Food competition is expected to be highest in species using small and scarce food sources, subject to a high risk of predation, and with large satiation levels. An appendix on statistical problems describes some of the pitfalls inherent in studies of the kind presented in this volume.
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Choudhury, Nazim, and Shahadat Uddin. "Evolutionary Features for Dynamic Link Prediction in Social Networks." Applied Sciences 13, no. 5 (February 24, 2023): 2913. http://dx.doi.org/10.3390/app13052913.
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One of the inherent characteristics of dynamic networks is the evolutionary nature of their constituents (i.e., actors and links). As a time-evolving model, the link prediction mechanism in dynamic networks can successfully capture the underlying growth mechanisms of social networks. Mining the temporal patterns of dynamic networks has led researchers to utilise dynamic information for dynamic link prediction. Despite several methodological improvements in dynamic link prediction, temporal variations of actor-level network structure and neighbourhood information have drawn little attention from the network science community. Evolutionary aspects of network positional changes and associated neighbourhoods, attributed to non-connected actor pairs, may suitably be used for predicting the possibility of their future associations. In this study, we attempted to build dynamic similarity metrics by considering temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics then worked as dynamic features in the supervised link prediction model, and performances were compared against static similarity metrics (e.g., AdamicAdar). Improved performance is achieved by the metrics considered in this study, representing them as prospective candidates for dynamic link prediction tasks and to help understand the underlying evolutionary mechanism.
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Emerson, Anne, and Debra Costley. "A Scoping Review of School-Based Strategies for Addressing Anxiety, Intolerance of Uncertainty and Prediction in Autistic Pupils." Education Sciences 13, no. 6 (June 2, 2023): 575. http://dx.doi.org/10.3390/educsci13060575.
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In a typical school day, young people need to do many tasks which rely on the ability to predict. Since prediction underpins cognitive and social skills, difficulties with prediction lead to multiple challenges to learning. In this review, we consider the evidence that autistic people often have difficulty making predictions about other people’s behaviour, or understanding what they are required to do, contributing to high rates of anxiety and intolerance of uncertainty. The focus of the review is to consider what we already know about effective strategies used by schools to support learning and social inclusion and to consider how we might build on these approaches. We propose a number of so far unexplored ideas with the potential to build predictive skills and which require evaluation.
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Zogan, Hamad, Imran Razzak, Xianzhi Wang, Shoaib Jameel, and Guandong Xu. "Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media." World Wide Web 25, no. 1 (January 2022): 281–304. http://dx.doi.org/10.1007/s11280-021-00992-2.
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AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
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Skoric, Marko M., Jing Liu, and Kokil Jaidka. "Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis." Information 11, no. 4 (March 31, 2020): 187. http://dx.doi.org/10.3390/info11040187.
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In recent years, many studies have used social media data to make estimates of electoral outcomes and public opinion. This paper reports the findings from a meta-analysis examining the predictive power of social media data by focusing on various sources of data and different methods of prediction; i.e., (1) sentiment analysis, and (2) analysis of structural features. Our results, based on the data from 74 published studies, show significant variance in the accuracy of predictions, which were on average behind the established benchmarks in traditional survey research. In terms of the approaches used, the study shows that machine learning-based estimates are generally superior to those derived from pre-existing lexica, and that a combination of structural features and sentiment analyses provides the most accurate predictions. Furthermore, our study shows some differences in the predictive power of social media data across different levels of political democracy and different electoral systems. We also note that since the accuracy of election and public opinion forecasts varies depending on which statistical estimates are used, the scientific community should aim to adopt a more standardized approach to analyzing and reporting social media data-derived predictions in the future.
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R, Valanarasu. "Comparative Analysis for Personality Prediction by Digital Footprints in Social Media." June 2021 3, no. 2 (May 31, 2021): 77–91. http://dx.doi.org/10.36548/jitdw.2021.2.002.
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The use of social media and leaving a digital footprint has recently increased all around the world. It is being used as a platform for people to communicate their sentiments, emotions, and expectations with their data. The data available in social media are publicly viewable and accessible. Any social media network user's personality is predicted based on their posts and status in order to deliver a better accuracy. In this perspective, the proposed research article proposes novel machine learning methods for predicting the personality of humans based on their social media digital footprints. The proposed model may be reviewed for any job applicant during the times of COVID'19 through online enrolment for any organisation. Previously, the personality prediction methods are failed due to the differing perspectives of recruiters on job applicants. Also, this estimation is modernized and the prediction time is also reduced due to the implementation of the proposed hybrid approach on machine learning prediction. The artificial intelligence based calculation is used for predicting the personality of job applicants or any person. The proposed algorithm is organized with dynamic multi-context information and it also contains the account information of multiple platforms such as Facebook, Twitter, and YouTube. The collection of the various dataset from different social media sites constitute to the increase in the prediction rate of any machine learning algorithm. Therefore, the accuracy of personality prediction is higher than any other existing methods. Despite the fact that a person's logic varies from season to season, the proposed algorithm consistently outperforms other existing and traditional approaches in predicting a person's mentality.
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Shi, Liushuai, Le Wang, Chengjiang Long, Sanping Zhou, Fang Zheng, Nanning Zheng, and Gang Hua. "Social Interpretable Tree for Pedestrian Trajectory Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2235–43. http://dx.doi.org/10.1609/aaai.v36i2.20121.
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Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-K predictions.
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Kimura, Masahiro, Kazumi Saito, Kouzou Ohara, and Hiroshi Motoda. "Learning to Predict Opinion Share in Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1364–70. http://dx.doi.org/10.1609/aaai.v24i1.7501.
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We address the problem of predicting the expected opinion share over a social network at a target time from the opinion diffusion data under the value-weighted voter model with multiple opinions. The value update algorithm ensures that it converges to a correct solution and the share prediction results outperform a simple linear extrapolation approximation when the available data is limited. We further show in an extreme case of complete network that the opinion with the highest value eventually takes over, and the expected share prediction problem with uniform opinion value is not well-defined and any opinion can win.